image-based characterization of the mechanical behaviour ... · this dissertation aims to combine...
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Image-Based Characterization of the Mechanical
Behaviour of Healthy and Metastatically-Involved
Vertebrae
By
Chetan Choudhari
A thesis submitted in conformity with the requirements
for the degree of Masters of Applied Science
Institute of Biomaterials and Biomedical Engineering,
University of Toronto
© Copyright by Chetan Choudhari 2014
ii
Image-Based Characterization of the Mechanical Behaviour of
Healthy and Metastatically-Involved Vertebrae
Chetan Choudhari
Master of Applied Science
Institute of Biomaterials and Biomedical Engineering
University of Toronto
2014
Abstract
Skeletal metastasis leads to changes in bone architecture, quality and strength, including
microdamage accumulation. This dissertation aims to combine image-based computational
and experimental techniques to study trabecular bone microdamage in healthy and metastatic
whole bones. Deformable image registration was used to demonstrate proof of concept that
post-euthanasia strain analysis of µCT images represents in vivo quasi static mechanical
behavior of whole rat vertebrae. The ability to concurrently identify microdamage in whole
vertebrae using histologic techniques (calcein and fuschin) and contrast enhanced BaSO4
µCT imaging was demonstrated and compared to stresses and strains calculated through
micro finite element analysis. Significantly higher stresses and strains were found in regions
of trabecular microdamage compared to undamaged regions, and in metastatic compared to
healthy vertebrae. The techniques and knowledge developed through this work improve
understanding of trabecular bone microdamage and form a solid platform for modeling the
material and structural behaviour of skeletal tissue.
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Acknowledgements
First and foremost, I would like to express my special appreciation and gratitude to my
advisor Dr. Cari Whyne. I attribute my Master’s degree to her encouragement and effort.
Without Cari this thesis, too, would not have been completed or written. Cari has a
tremendous passion for science and a contagious enthusiasm for research. She has supported
me throughout the duration of my project with her knowledge and insight, whilst patiently
allowing me to think and work independently. One simply could not wish for a better and
friendlier supervisor.
I would like to thank my committee members Dr. Radovan Zdero, Dr. Albert Yee and Dr.
Tom Willett for providing meaningful suggestions and insights, which contributed
significantly to the overall success of this project.
Working in Orthopedic Biomechanics Lab (OBL) has been a great learning experience for
me. The incredible work ethic followed by each and every member of the lab was inspiring.
The environment in the lab was full of energy and intellectually stimulating. I would like to
thank the members of the OBL, who also share the credit for this work. I thank Dr.
Margarete Akens for arranging the samples used in this project. Much of my understanding
about animal models and experimental techniques used in this project can be attributed to
her. I would also like to acknowledge Stew, Hamid, Zoryana, Mikhael and Edwin for making
this project a thoroughly enjoyable ride. Additionally, I want would like to appreciate
Katelyn for her work on the project over the summer.
Lastly, I am forever grateful to my family and friends for their selfless love and support,
which have allowed me to overcome the challenges I faced over the duration of the project.
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Contents
List of Tables ........................................................................................................................ viii
List of Figures ......................................................................................................................... ix
List of Appendices ................................................................................................................. xii
Chapter 1: Introduction and Thesis layout................................................................................ 1
1.1 Layout of the Thesis ................................................................................................... 3
Chapter 2: Literature review ..................................................................................................... 4
2.1 Bone structure and composition ...................................................................................... 4
2.2 Bone remodeling ............................................................................................................. 6
2.3 Anatomy of Vertebral column ........................................................................................ 7
2.4 Spinal metastasis ............................................................................................................. 8
2.4.1 Fracture risk .............................................................................................................. 9
2.4.2 Clinical interventions for spinal metastasis ............................................................ 10
2.4.3 Pre-clinical models of spinal metastasis ................................................................. 11
2.5 Trabecular biomechanics of the vertebral body ............................................................ 11
2.5.1 Trabecular microarchitecture in vertebral body ..................................................... 12
2.5.2 Microdamage in trabecular bone ............................................................................ 14
2.5.3 Mechanical properties of trabecular bone .............................................................. 14
2.6 Biomechanical analysis of bone .................................................................................... 15
2.6.1 Mechanical testing .................................................................................................. 16
2.6.2 Microdamage analysis through staining ................................................................. 16
2.6.3 Finite element analysis ........................................................................................... 17
2.6.4 Image-based strain analysis .................................................................................... 19
2.7 Summary ....................................................................................................................... 21
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Chapter 3: Post euthanasia micro-CT based strain analysis is able to represent quasi-static in
vivo behavior of whole vertebrae. ........................................................................................... 22
3.1 Abstract ......................................................................................................................... 22
3.2 Introduction ................................................................................................................... 23
3.3 Methods ......................................................................................................................... 24
3.3.1 Deformable image registration algorithm .............................................................. 24
3.3.2 Animal model ......................................................................................................... 24
3.3.3 Loading ................................................................................................................... 25
3.3.4 Imaging ................................................................................................................... 26
3.3.5 Strain calculations and data analysis ...................................................................... 26
3.4 Results ........................................................................................................................... 27
3.5 Discussion ..................................................................................................................... 31
3.6 Conclusion .................................................................................................................... 33
Chapter 4: Barium sulfate contrast enhanced μCT imaging to identify microdamage in whole
rat vertebrae ............................................................................................................................ 34
4.1 Abstract ......................................................................................................................... 34
4.2 Introduction ................................................................................................................... 35
4.3 Methods ......................................................................................................................... 36
4.3.1 Staining protocols ................................................................................................... 36
4.3.2 BaSO4 staining of rat vertebrae .............................................................................. 37
4.3.3 Histological validation of BaSO4 staining.............................................................. 37
4.4 Results ........................................................................................................................... 39
4.4.1 BaSO4 staining protocol for rat vertebrae .............................................................. 39
4.4.2 BaSO4 and Calcein/Fuchsin compatibility ............................................................. 40
4.5 Discussion ..................................................................................................................... 42
4.6 Conclusion .................................................................................................................... 44
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Chapter 5: Evaluation of tissue level stresses and strains under uniaxial compression of
whole healthy and osteolytic rat spines .................................................................................. 45
5.1 Abstract ......................................................................................................................... 45
5.2 Introduction ................................................................................................................... 46
5.3 Methods ......................................................................................................................... 47
5.3.1 Animal models: ...................................................................................................... 49
5.3.2 Microdamage Evaluation using calcein/fuchsin staining and contrast enhanced
μCT .................................................................................................................................. 49
5.3.3 Loading ................................................................................................................... 50
5.3.4 Strain fields and boundary conditions .................................................................... 51
5.3.5 Alignment of histology slides................................................................................. 51
5.3.6 Creating µFE models .............................................................................................. 52
5.3.7 Statistics and data analysis ..................................................................................... 53
5.4 Results ........................................................................................................................... 53
5.4.1 Microdamage identification using histology .......................................................... 53
5.4.2 Image registration to determine strain fields and boundary conditions ................. 58
5.4.3 Alignment of histology slides with unloaded scans ............................................... 61
5.4.4 µFE modeling of healthy and metastatic spines ..................................................... 66
5.4.5 Determining tissue-level stresses and strains in histologically damaged and
undamaged regions .......................................................................................................... 68
5.4.6 Stresses and strains in damage regions identified by BaSO4 ................................. 73
5.5 Discussion ..................................................................................................................... 76
5.5.1 Microdamage analysis using sequential staining ................................................... 76
5.5.2 Alignment of histology slides................................................................................. 77
5.5.3 Trabecular stresses and strains in histologically identified microdamage ............. 78
5.5.4 Microdamage identification using BaSO4 contrast enhanced imaging .................. 81
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5.5.5 µFE modeling ......................................................................................................... 83
5.6 Conclusion .................................................................................................................... 86
Chapter 6: Concluding remarks .............................................................................................. 87
Chapter 7: References ............................................................................................................. 89
Appendix 1: Copyright Permissions ....................................................................................... 99
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List of Tables
Table 2.1: Local compressive yield stresses and strains for trabecular bone……………..…15
Table 3.1: Strains (µm/µm) obtained from the comparisons of loaded-unloaded images…..27
Table 3.2: Strains (µm/µm) obtained from the comparisons of images under similar loading
conditions………..…………………………………………………………………………...27
Table 4.1: BaSO4 staining parameters used for various bone types till date……..………….35
Table 5.1: Trabecular stresses and strains at locations of microdamage determined under
axial load by previous studies…………….……….……...……….……….……….…….….47
Table 5.2: Average strains (µm/µm) obtained from the comparisons of loaded/unloaded
images of healthy and osteolytic spines………….…………….…………………………….59
Table 5.3: Volumetric concurrencies for healthy spines……………….…………...……….63
Table 5.4: Volumetric concurrencies for osteolytic spines………….…………..…………..65
Table 5.5: Average stress and strain from damaged and undamaged regions (healthy….…..70
Table 5.6: Average stress and strain from damaged and undamaged regions (metastatic).…71
Table 5.7: Comparison of local stresses and strains in healthy and osteolytic models…..….72
Table 5.8: Local stresses and strains in BaSO4 contrast enhanced damaged sites (healthy)...73
Table 5.9: Local stresses and strains in BaSO4 contrast enhanced damaged sites
(metastatic)…………………………………………………………………………………..74
Table 5.10: Comparison of local stresses and strains in healthy and osteolytic models
(BaSO4)…..…………………………………………………………………………………..74
Table 5.11: Comparison of local stresses and strains within regions of damage identified by
fuchsin and BaSO4 in healthy models………….……..…….……….……….………….…..75
Table 5.12: Comparison of local stresses and strains within regions of damage identified by
fuchsin and BaSO4 in metastatic models………….…………....……….……….…………..75
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List of Figures
Figure 2.1: Difference between the structures of cortical and trabecular bones. The inner
trabecular bone is much more porous than the outer compact bone. (Public domain image
from Wikimedia Commons)……………………...………….......……………………...........5
Figure 2.2: Anatomy of human spine; (left) all levels of the vertebral column, (centre) an
individual vertebra with vertebral body and posterior elements, (right) series of 3 vertebrae
displaying facet joints and intervertebral discs (Public domain image from Wikimedia
commons)………………………………………………….………………………………….8
Figure 2.3: Various types of spinal metastasis. A: Osteolytic, B: Osteoblastic, C: Mixed
Focal, D: Mixed diffuse (Skrinskas 2009)………………………………….....………………9
Figure 2.4: µCT image slice showing cross section (frontal plane) of a rat L1 vertebral body,
with a cortical shell surrounding the trabecular centrum…………………………..…...……12
Figure 2.5: High resolution micro-CT image of trabecular specimen (left), converted into a
voxel based micro finite element mesh (right) (Keaveny 2001)…………………………….19
Figure 2.6: Deformable image registration algorithm determines strain by aligning and
comparing scans of the same sample with and without load (Hojjat 2011)…………………20
Figure 3.1: Micro-CT compatible loading jig used to apply an axial compressive load to the
6th caudal vertebra in the rat tail……………………………………...……………………..25
Figure 3.2: Mean axial strains (µm/µm) obtained from deformable registration (* represents
p-value<0.016). a) Strains obtained from all loaded-unloaded and all loaded-
loaded/unloaded-unloaded comparisons. b) Comparison of live and dead strains. c) Live and
dead strains for scans under equivalent loading conditions…………………………..…...…28
Figure 3.3: Strain patterns in loaded-unloaded strain registrations. The strain is concentrated
mostly around the endplate regions. a) Dead-loaded to dead-unloaded comparison b) Live-
loaded to dead-unloaded comparison…………………………………..……………………28
Figure 3.4: Strain patterns generated by comparison of scans under equivalent loading
conditions. a) Live-loaded to dead loaded b) Dead-loaded to dead-loaded c) Dead-unloaded
to dead-unloaded………………………………………..…………………………...……….29
Figure 3.5: Deformation maps generated form for live-dead images (a – vertical, b – lateral)
and dead-dead images (c – vertical, d – lateral)…………………………...……...…………30
Figure 4.1: Loading device used to load samples inside the micro-CT scanner……..……...38
Figure 4.2: Micro-CT images of unloaded rat vertebrae stained for 1 day (left), 2 days
(centre) and 3 days (right). The bright spots within the vertebral body represent BaSO4. It can
be observed that staining for more than 1 day causes overstaining (arrows indicate regions of
overstaining)……………………..……………………………………………….………….39
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Figure 4.3: BaSO4 is able to stain microdamage in osteolytic spine, without pooling. Arrows
indicate damaged regions of high intensity, consisting of BaSO4……………………..…….40
Figure 4.4: Fluorescence (a, b), brightfield (c) and µCT (d) images of a pre-existing
microdamage site highlighted by fuchsin (a, c), calcein (b) and BaSO4 (d) staining. Arrow
indicates a region of preexisting microdamage observed in fluorescent images, but not on the
bright field image.………………………………………..…………………………………..41
Figure 4.5: Fluorescence (a, b), brightfield (c) and µCT (d) images of a load induced
microdamage site highlighted by fuchsin (a, c), and BaSO4 (d) staining, but not by
calcein (b)…………………………………….……………………………………….……..42
Figure 5.1: Experimental design……………………………..………………………………48
Figure 5.2: Coronal histology slide from a healthy vertebral body imaged under fluorescence
to identify calcein stained pre-existing damage (a), and under plain light to detect fuchsin
stained load induced damage (b)…………………………….………………………….…...55
Figure 5.3: a) Bright field image of a histology slide from a healthy sample. b) Fuchsin
stained load induced damage on a trabecula at 20x magnification……………...…………..56
Figure 5.4: a) Pre-existing damage labelled by both calcein and fuchsin b) Load induced
microdamage stained only by fuchsin, and not calcein…………………………..………….57
Figure 5.5: a) Coronal histology slide of osteolytic spine demonstrating reduced trabecular
number and increased fuchsin accumulation in the osteolytic regions. b) Multiple
microdamaged sites observed near osteolytic tumor tissue……………………………...….58
Figure 5.6: Coronal slices demonstrating strain fields obtained for healthy (a) and metastatic
(b) whole vertebrae. Red and blue areas experience high and low strains respectively. Arrow
denotes area osteolytic destruction under high strain………………………….…………….59
Figure 5.7: Displacement vectors generated using deformable image registration, represented
by blue arrows generated for healthy (a) and metastatic (b) vertebrae ……………………...60
Figure 5.8: Bright field image of a histology slide from a healthy slide (a) and surface
generated from µCT image of the same slide (b)……… …………………….……………..61
Figure 5.9: Surface generated from registered µCT scans of the block (posterior elements)
and three slides superimposed on unloaded µCT scan from a healthy sample………...……62
Figure 5.10: Segmented unloaded µCT images of the same healthy sample, each containing a
histology slide identified as a separate material……..………..…………….…………...…..63
Figure 5.11: µCT scan of a slide from healthy sample (yellow), along with the region of
intersection between the slide and the unloaded scans (blue). The VC for this slide was
66%...…………………………...……………………………………………...………….....64
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Figure 5.12: µCT scan of a slide from metastatic sample (yellow), along with the region of
intersection between the slide and the unloaded scan (green). The VC for this slide was
60%..…………………...…………………………...…………………………………..........65
Figure 5.13: µFE grid generated form whole bone µCT scan of a metastatic sample with
elements corresponding to a histology slide highlighted in red. One such model was
generated from each histology slide……………….……………………………...…………66
Figure 5.14: a) µCT scan of a spinal motion segment under load. b) Surfaces on the endplates
and facet joints were selected as loading surfaces for the middle vertebra (highlighted in
pink)………………...…………………………………………………………………….….67
Figure 5.15: a) Bright field image of a coronal histology slide from a healthy sample stained
with fuchsin to identify load induced microdamage b) Results showing tissue level maximum
principal stress distribution in the elements within the µFE model corresponding to the
histology slide. Arrows show correspondence between the histology slide and the model…68
Figure 5.16: a) Axial compressive load induced microdamage identified by fuchsin staining.
b) Elements corresponding to the damaged site selected in the undeformed µFE model. c)
Completed µFEA demonstrates elevated maximum principal stress in the region of
microdamage………………………………….……………………………………………..69
Figure 5.17: Comparison of stresses (a) and strains (b) obtained from damaged and
undamaged regions in healthy samples. * represents significant differences (p-
value<0.016)………………………………..………………………………………………..70
Figure 5.18: Comparison of stresses (a) and strains (b) obtained from damaged and
undamaged regions in metastatic samples. * represents significant differences (p-
value<0.016)…………………………………………………………………………………71
Figure 5.19: Comparison of stresses (a) and strains (b) obtained from damaged regions in
healthy versus metastatic samples. * represents significant differences (p-value<0.016)…..72
xii
List of Appendices
Appendix 1: Copyright Permissions…………………………………………………………99
1
Chapter 1: Introduction and Thesis layout
Skeletal metastasis is commonly diagnosed in breast, prostate and lung cancer patients. The
vertebral column is the most common bone affected. Metastatic disease in the bony spine can
be bone destroying (osteolytic), bone forming (osteoblastic) or a mixture of both (mixed).
Spinal metastasis results in severe pain and poses an increased risk of fracture, impacting
physical mobility. Compromising the mechanical stability of the spine can significantly
affect patients’ quality of life. Advanced cases of spinal metastasis can lead to pathologic
fracture and neurologic complications. Development of more advanced techniques is
imperative to identify patients at an elevated risk of vertebral fracture, recognizing when
there is a need for intervention and how decisions on intervention can be optimized.
The decreased quality and architecture of trabecular bone in the metastatic spine leads to a
rapid accumulation of unrepaired microdamage, culminating in an elevated risk of bone
failure. Concentrations of local stresses and strains in response to the load applied stimulate
the initiation and propagation of microdamage. The distribution of these stresses and strains
within the bone matrix is governed by the local microarchitecture and bone quality. A
compromised bone microstructure may lead abnormal stress/strain concentrations leading to
bone microdamage. However, the exact relationship between trabecular level damage events
and local stresses and strains is not well characterized. Quantifying the tissue level structural
behavior of healthy and metastatically-involved vertebrae will enhance our understanding of
the local mechanical environment at microdamage initiation, leading to better diagnostic
criteria for fracture risk assessment.
Biomechanical analyses of the metastatic spine have included computational, image-based
and experimental techniques. Specifically, finite element (FE) modeling has demonstrated
success in predicting fracture patterns and failure loads in bony structures including bones
with metastatic defects. The overall objective of this project was to develop imaging methods
and computational models that can accurately identify microdamage and quantify
microstructural stresses and strains in whole healthy and osteolytic rat spines.
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The overall objective was subdivided into the following three specific aims:
Aim 1
Motivation: To date, strain based assessment of bone has been primarily limited to ex vivo
specimens. In translating findings from ex vivo strain based studies, it is hypothesized that ex
vivo µCT-image-based strain analysis represents the in vivo quasi static behavior of whole
bones.
Research question: Does ex vivo analysis represent the in vivo mechanical behavior in the
spine?
Hypothesis: Ex vivo µ-image-based strain analysis represents in vivo quasi static vertebral
behavior.
Specific aim: Use deformable image registration to demonstrate the ability of ex vivo
modeling to represent the in vivo behavior of the vertebral column through image-based
strain analysis.
Aim 2
Motivation: Contrast enhanced μCT of whole bone provides a 3D alternative to traditional
destructive 2D histologic microdamage analysis. However, demonstration of the ability and
accuracy of this method to highlight microdamage in whole vertebrae is essential prior to
further utilization of this technique.
Research question: How can we best visualize/quantify microdamage in whole bone?
Hypothesis: μCT contrast enhanced (BaSO4) imaging accurately represents vertebral
microdamage identified by whole bone calcein/fuchsin staining.
Specific aim: Generate a robust protocol for μCT contrast enhanced (BaSO4) imaging and
calcein/fuchsin staining, which allows the direct comparison of these techniques to represent
load induced damage within whole vertebrae
Aim 3
Motivation: µFE modeling is an important technique which shows promise to accurately
represent the structural integrity of vertebrae at the trabecular level within the spine.
3
Generation of robust models and analysis techniques may allow for development of a better
understanding of the stability of healthy and osteolytic vertebrae.
Research question: How can µFE analysis represent microdamage in whole healthy and
osteolytic vertebrae?
Hypothesis: µFE analysis generates computational models that yield consistent damage
initiation thresholds in healthy and osteolytic vertebrae from athymic rats.
Specific aim: Generate µFE models that accurately represent damage initiation and failure of
whole healthy and osteolytic vertebrae based on histological, strain based and contrast
enhanced μCT damage quantification. Determine thresholds for damage initiation based on
these models.
1.1 Layout of the Thesis
The second chapter of this document includes a background of key concepts as well as
current research relevant to this dissertation. The third chapter investigates the ability of ex
vivo modelling to represent the in vivo behavior using image-based strain analysis (aim 1). It
is modelled after a typical scientific paper, with an abstract, a brief introduction, methods,
results and discussion, including comparisons with similar studies, strengths and weaknesses
and significance of the findings. The fourth chapter again follows a paper based format and
presents a technical evaluation of microdamage visualization techniques in whole bone (aim
2). The fifth chapter uses the protocol developed in chapter 4 to evaluate the ability of micro
finite element modeling to represent microdamage in healthy and metastatic vertebrae (aim
3). The sixth chapter summarizes the most important conclusions of the project as a whole
and provides recommendations for future research.
4
Chapter 2: Literature review
2.1 Bone structure and composition
Bone is the primary structural component of the human skeletal system. In addition to
supporting the body, bone serves many important functions, such as protection of vital
organs, providing framework for movement and flexibility, sheltering the bone marrow and
acting as a calcium reservoir. The strength of bone and its ability to resist fracture depends
upon its quality, which is determined by tissue level material properties and architecture. The
bone matrix is composed of organic and inorganic (mineral) phases, which include
hydroxyapatite, collagen and small amounts of proteoglycans, non-collagenous proteins and
water (Olszta 2007). The exact composition of bone varies with age, sex, type of bone and
pathological conditions (Doblare 2004). The organic phase, the matrix of the bone tissue, is
composed of 90% type 1 collagen along with other proteins and proteoglycans in addition to
bone cells, growth factors and cytokines. The inorganic phase is predominantly made up of
hydroxyapatite, precipitated on the organic bone matrix (Post 2010). The organic matrix and
the inorganic phases together give the bone its characteristic strength and toughness.
Based on its structural properties, bone can be characterized into two types – cortical and
trabecular. Cortical bone is dense with a very low porosity (less than 10%). High cortical
bone density provides resistance to compression, bending and torsion. Cortical bone is
arranged in layers of lamellae (Figure 2.1). In larger animals, concentric lamellae
surrounding Haversian canals form cylindrical osteons, which are the structural and
functional units of cortical bone in the diaphysis of long bones. Each osteon is surrounded
cement line, made primarily of minerals, which resists fracture progression in cortical bone.
The osteons are densely packed together along the longitudinal axis of the cortical bone
(Olszta 2007). Such an arrangement makes cortical bones stronger along the longitudinal
axis, as compared to the transverse axis. Cortical bone has also been found to be stronger in
compression than in tension. Cortical bone demonstrates viscoelastic behavior under load
and is stiffer and stronger at higher loading rates (Olszta 2007).
5
Figure 2.1: Difference between the structures of cortical and trabecular bones. The
inner trabecular bone is much more porous than the outer compact bone. (Public
domain image from Wikimedia Commons).
Trabecular bone is a three dimensional matrix comprised of interconnecting plates and rods,
which are individually termed as trabeculae (Donnell 2006). Trabecular bone is located at
the ends of long bones (tibia, femur, etc.) and within irregular bones (spine and pelvis). The
highly porous trabecular matrix houses vascularized and nutrient rich bone marrow (Figure
2.1), where blood cells undergo differentiation. Trabecular bone has lamellar structure
similar to cortical bone, with the lamellae running parallel to the trabeculae. The porous
nature of trabecular bone yields an optimized weight to strength ratio for bone (Keaveny,
2001). The high porosity of the trabecular bone allows large plastic deformations under
compressive loads. Owing to its porous matrix filled with liquid marrow, trabecular bone
demonstrates poroelastic behavior under load and is stiffer at higher strain rates (Ochoa
1991). The mechanical properties of trabecular bone depend on the bone quality and
morphology, and vary with the location within the body (Keaveny, 2001).
6
2.2 Bone remodeling
The structure and composition of bone is sensitive to its mechanical environment. Bone is
continuously in a state of flux, being remodeled by a process of absorption and formation.
According to the Wolff’s law, bone remodels itself over time to adapt to biomechanical
stimuli (Frost 1996, Wolff 1892). This process is accomplished by the coupling of three
types of specialized cells which live within the bone matrix (Clarke 2008).
Osteocytes: reside within the matrix to regulate bone remodeling
Osteoblasts: bone synthesizing cells
Osteoclasts: bone dissolving cells
Osteocytes are terminally differentiated osteoblast cells, which become embedded in the
bone matrix as it is being formed. These cells play an important role in maintenance and
regulation of the bone microenvironment. Osteocytes convert the mechanical sensations of
stress or bone damage to electro-chemical signals through mechanotransduction, which
recruit mononuclear osteoclast precursors from circulation, to start remodeling of the bone.
These precursor cells combine to form large multi-nucleated osteocytes, which digest old
bone matrix using enzymes. For the subsequent bone formation, regulatory factors initiate
the differentiation of mesenchymal progenitor cells to osteoblast. Osteoblasts are then
recruited to fill the cavities with new bone minerals and matrix proteins. Osteoblasts buried
within the newly formed matrix differentiate to become osteocytes which are connected to
bone surface lining cells and other osteocytes through an extensive canalicular network
(Clarke 2008, Manolagas 2000). This network plays an essential role in monitoring the bone
tissue and initiating bone remodeling. The perpetual remodeling of the bone preserves the
structural and mechanical integrity of bone throughout life. Factors such as age and
pathological conditions (i.e. osteoporosis or metastasis) can interfere with natural bone
remodeling process to alter the structure and mineral composition of bone, thus affecting its
mechanical integrity.
7
2.3 Anatomy of Vertebral column
The vertebral column (spine) is one of the most important sections of the human skeletal
system. The series of vertebrae, connected through soft tissue, ligaments and intervertebral
discs together form the flexible vertebral column, which houses the spinal cord, provides
structural support for the maintenance of proper posture and enables flexible motion of the
upper body (Seeley 2006). Being a primary load bearing component of the body, the
vertebral column has been a key area of research in field of Biomechanics.
The human spine is divided into three segments – cervical, thoracic and lumbar. The cervical
section, consisting of 7 vertebrae (C1-C7), lies in the neck region and is mainly responsible
for the protection of the brain stem and support and motion of the head. Beneath the cervical
section, lies the thoracic spine made up of 12 vertebrae (T1-T12). The main function of this
section is to protect heart and lungs with the help of the rib cage. The 5 lumbar vertebrae
(L1-L5) below the thoracic region are the greatest weight bearing components of the spine
(Figure 2.1).
Although the individual vertebrae of the spine vary in shape and size, the overall structure is
similar. The primary load bearing component of a vertebra is the vertebral body. The load
bearing ability of the vertebral body depends on its size. The size of the vertebral body
progressively increases downward, with average strength of 2000N in the cervical spine to
8000N in the lumbar segment (Izzo 2013). The posterior elements of the vertebra form the
vertebral arch, which consists of pedicles, laminae and three types of processes. The
vertebral body, together with the pedicles and the laminae form a canal that protects the
spinal cord. The articular processes connect with those of adjacent vertebra to form facet
joints. Between two vertebrae lies the intervertebral disc which acts like a shock absorber.
The disc is made up of made up of two parts. The central portion of the disc, the nucleus
pulposus, is a gel like elastic substance made up of water, collagen (predominantly type 2)
and proteoglycans. Surrounding the nucleus pulposus is a layered composite structure called
the annulus fibrosis, made up of collagen fibers (predominantly type 1). The facet joints and
the intervertebral discs together allow for the flexible movement and load transfer in the
spine (Figure 2.2) (Seeley 2006).
8
Figure 2.2: Anatomy of human spine; (left) all levels of the vertebral column, (centre)
an individual vertebra with vertebral body and posterior elements, (right) series of 3
vertebrae displaying facet joints and intervertebral discs (Public domain image from
Wikimedia commons)
2.4 Spinal metastasis
Metastasis is the migration of cancer from its origin to a new location in the body. Bone is
one of the most frequent sites of metastasis. Breast, lung, prostrate and renal cancers are the
most common types of cancers to metastasize to bone. The spine is the most frequent site of
metastasis in the skeleton owing to its excessive and nutrient rich trabecular network.
Approximately 1/3 of all cancer patients are diagnosed with spinal metastases (Naishadham
2012). Such patients suffer from significant consequences in terms of morbidity and pain
originating from microfractures, burst fractures, nerve root infiltration, bone distortion and
collapse (Raele 2001).
Metastatic involvement in the spine results in the decoupling of healthy osteolytic and
osteoblastic cell interaction, which not only alters the bone turnover, but also affects the bone
density and architecture (Guise 2001). The disturbance of the intricate balance of remodeling
can lead to alterations in the structural and material properties of bones, which can severely
affect their load bearing properties. Osteolytic tumors up-regulate the production of
9
Figure 2.3: Various types of spinal metastasis. A: Osteolytic, B: Osteoblastic, C: Mixed
Focal, D: Mixed diffuse (Skrinskas 2009)
osteoclasts, which leads to an increase in bone resorption. This results in increased porosity,
reduced bone density and the replacement of mineralized bone by soft tumor tissue, with a
consequent increase in the risk of fracture. Osteoblastic tumors increase the genesis of
osteoblasts, which leads to excessive bone deposition. Although osteoblastic tumor growth
leads to increased amounts of bone, abnormal bone quality and micro structure leads to lower
yield strength (Coleman 2001). Osteolytic tumors are characteristic to breast and lung cancer
metastasis while osteoblastic lesions are frequently diagnosed in prostate cancer metastasis
(Coleman 2001). However, many lesions are mixed exhibiting both osteolytic and
osteoblastic components, with both diffuse and focal damage (Figure 2.3).
2.4.1 Fracture risk
Skeletal related events (SRE’s) is a collective term for describing conditions such as
hypercalcemia, pathological fracture, severe pain, spinal cord compression and bone
instability, which occur as a result of metastatic involvement in bone (Von Moos, 2013).
Close to two-thirds of patients with bone metastases develop at least one SRE (Lipton 2000).
Recent advancement in cancer diagnostics and treatments has increased the life expectancy
of cancer patients. However, this also leads to increased chances of complications from the
metastatic involvement (Von Moos, 2013). Given the importance and high incidence of
SREs, strategies to reduce the burden of SREs, and in particular fractures, are imperative.
The most common fracture type resulting from osteolytic disease in the spine is a
compression or wedge fracture, which results in the collapse of the anterior wall of the
vertebral body (Wong 2013). Burst fractures, which can occur under high impact loading in
10
normal spines, cause the collapse of the posterior wall of the vertebral body. Burst fractures
can also occur under normal physiologic loading conditions in osteolytic vertebrae, and may
lead to neurologic complications arising from bone fragments or tumor tissue penetrating
into the spinal canal (Whyne 2003). From 5-10% of all cancer patients suffer from spinal
cord and nerve root injuries originating from spinal metastasis (Constans 1983).
2.4.2 Clinical interventions for spinal metastasis
Currently, bone metastasis is treated by utilizing a multimodal approach that may include
radiation therapy, chemotherapy, surgical removal and systemic treatment with
bisphosphonates (BP) (Bilsky 2005, Rades 2010). Historically most breast cancer metastases
were diagnosed to be osteolytic in nature; however, with the introduction of modern cancer
therapies (i.e. BP’s) the relative incidence of osteolytic, osteoblastic, and mixed vertebral
lesions is changing (Curtis 2007). The implications of new systemic therapies, on the pattern
of disease in the metastatic spine are important in focusing new initiatives aimed at structural
analysis and fracture risk assessment to ensure relevancy in today’s patients with metastatic
breast and other types of cancers.
Vertebroplasty and kyphoplasty are performed to provide stabilization to the diseased spine
to prevent fractures. These techniques include percutaneous insertion of
polymethylmethacrylate in the vertebral body, which hardens to alleviate pain and provide
structural support to the compromised spine (Bhatt 2013). Strength assessment and fracture
risk prediction in the metastatic spine are of significant clinical importance as prevention of
SREs in high risk patients may be possible through use of external bracing or surgical
stabilization.
Bone mineral density (BMD) is the standard clinical measure of mechanical strength of the
bone. BMD is primarily measured using dual X-ray absorptiometry (DXA), and more
recently using quantitative Computed Tomography (qCT) (Engelke 2012). In recent years,
advanced image-based techniques such as finite element analysis (see section 2.6), hip
structural analysis and trabecular bone score have been developed, which account for bone
quality and architecture, and are used in combination with BMD to provide more accurate
biomechanical analysis of the metastatic spine (Engelke 2012).
11
2.4.3 Pre-clinical models of spinal metastasis
Numerous pre-clinical in vivo and in vitro models have been developed to study spinal
metastasis. Researchers have used human and animal cadaveric spine samples with simulated
focal lesions to represent spinal metastasis (Dimar 1998, Ebihara 2004). Pre-clinical animal
models of skeletal metastases may be utilized to represent pathological changes in bone, but
these do not fully represent human anatomy or pathology (Blouin 2005, Cossigny 2012,
Goldstein 2010, Singh 2005, Yoneda 1999). These models, however, can yield close to
realistic estimates of the impact of tumor burden in the skeleton. Since the behavior of
different tumor cells varies widely, the use of growing human cancer cells in animals may
best represent the behavior of the particular cells in the human body and their subsequent
response to treatment. Recent work in our group has successfully employed bioluminescence
transfected HELA cells (previously thought to be human breast cancer cells) to produce
osteolytic metastasis in rnu/rnu nude athymic rats. This model, along with an ACE 1 canine
prostate cancer model for osteolytic and mixed metastatic disease, has been used in our
laboratory to examine novel treatments and associated effects on fracture risk (Hojjat 2011,
Herblum 2013, Hojjat 2012, Lo 2012).
2.5 Trabecular biomechanics of the vertebral body
Being the primary load bearing component, the vertebral body is the biggest and the most
important section of the vertebra (Izzo 2013). The vertebral body is composed of a thin
cortical shell, wrapped around a trabecular meshwork, called trabecular centrum (Figure
2.4). The loads on the vertebral body are distributed between the cortical shell and the
trabecular centrum. Although various factors (age, disease, shape of the vertebrae, etc.)
dictate the load sharing between the shell and the centrum, recent studies have shown that
cortical shell accounts for only about 10% of vertebral strength (Prakash 2007, Silva 1997).
Microarchitecture of trabecular centrum has been shown to play an important role in
mechanical resistance, especially in the spine (Cortet, 2001). In a study by Fields et al., the
coefficient of determination of vertebral strength between finite element model and
biomechanical testing improved from r2 = 0.57 to r2 = 0.85, when accounted for both BMD
12
Figure 2.4: µCT image slice showing Cross section (frontal plane) of a rat L1 vertebral
body, with a cortical shell surrounding the trabecular centrum
and microarchitecture, as opposed just BMD (Fields, 2009). This underlines the importance
of the trabecular morphology to the load bearing capacity of the vertebral body. Although
multiple studies have shown the importance of microarchitecture, its exact contribution is not
very well understood due to variability in vertebral shape, size and bone mass.
2.5.1 Trabecular microarchitecture in vertebral body
The vertebral trabecular centrum is comprised of interconnected rod-like and plate-like
structures. The ratio of rods to plates in the trabecular bone is site specific, and influences
local mechanical properties and failure mechanisms (Keaveny 2001). Failure in rod-like
structures occurs mainly due to bending and buckling, followed by collapse. On the other
hand, failure can occurs instantaneously in plate-like structures, without bending or buckling
(Muller, 1998). Human vertebral trabecular bone is more prone to bending and buckling,
given its higher concentration of rod-like trabecular elements (Hildebrand, 1999). The
trabecular structure of the human vertebral body is anisotropic, and has a heterogeneous
13
architecture and density in anterior-posterior and vertical-transverse directions (Banse 2001),
although some left-right symmetry is observed in the lumbar vertebrae (Simpson 2001). The
trabeculae are thinner and more densely packed near the end-plate regions, compared to the
central region of the vertebral body (Simpson 2001). These features can also be generally
observed in rat vertebral bodies (Figure 2.4).
Various quantitative parameters have been developed to study the structural morphology of
the bone. Microarchitectural measurements include the width, number, separation of
trabeculae, in addition to their spatial organization (Carbonare 2005). Most common
morphological measurements include trabecular bone volume, trabecular thickness,
trabecular number, trabecular spacing and degree of anisotropy (Donnell 2007). These
stereological parameters have been extensively studied through the use of 2-D
histomorphometry (Carbonare 2005). Alternatively, non-invasive imaging techniques such as
high resolution micro-CT and micro-MR imaging allow non-invasive measurement of the
trabecular structure in 3D (Rizzoli 2010). In a recent investigation, Hojjat et al. implemented
automated algorithm on micro-CT images to quantify and compare microarchitectural
parameters in healthy and osteolytic vertebrae. Significant decrease in trabecular bone
volume, trabecular thickness and trabecular number and significant increase in trabecular
spacing were observed (Hojjat 2011), demonstrating compromised microarchitecture in
metastatic vertebrae.
The trabeculae are preferentially aligned along the axis of loading, to adapt to the load
applied. Consequently, human vertebral body consists of higher number of vertically aligned
trabeculae (Keaveny, 2001). The axial compressive loads on the vertebral body are first
accepted by the vertical struts, which transmit loads between the end-plates. The horizontal
trabeculae allow dispersion of the loads, and prevent the buckling of vertical ones. However,
in the presence of osteolytic metastasis, the vertical columns are progressively thinned as
well as elongated due to the resorption of the horizontal lamellae (Hojjat 2011). The
resistance of a column decreases by the square of increasing length and by the square of
decreasing cross section (Izzo, 2013). Similarly, bone loss also leads to a decreased
connectivity and increased trabecular spacing. This causes an overall degradation of the
trabecular microarchitecture, leading to an increased risk of bone failure. Osteoblastic tumors
14
lead to increased bone deposition and bone density; however, decreasing bone material
properties and abnormal microstructure result in weaker structural properties (Hojjat 2012).
2.5.2 Microdamage in trabecular bone
Under sufficient loads, damage to the trabecular bone can be observed in the form of
microdamage or microfractures. Microdamage can present as linear (parallel cracks along
lamellar surface or cross-hatched patterns across the lamellae) or diffuse damage (Fyhrie,
1994). Microfractures, or complete fractures of trabeculae, occur much less frequently and
are the end result of the accumulating microdamage spanning across the trabecular thickness
(Yeh, 2001). Linear microdamage is caused by compressive stress, while diffuse damage is
generally a result of tensile loading (Vashishth, 2000; Wenzel, 1996). The occurrence and
repair of microdamage is a part of the healthy bone remodeling process. However,
unrepaired bone microdamage has been demonstrated to be a contributing factor to skeletal
fragility and the accumulation of microdamage with increasing age or disease condition
(metastasis or osteoporosis) causes weakening of the bone and an increased risk of fracture
even during normal physical activities (Frost, 1960). According to a study by Yeh et al.,
widespread accumulation of microdamage within trabeculae, and not microfractures, is a
more likely explanation for the reduction of apparent strength and stiffness of the trabecular
structure after an isolated overload (Yeh, 2001). Burr et al. have also demonstrated that
diffuse damage correlated linearly with modulus loss; whereas linear microcracks had a
quadratic relationship with modulus loss (Burr, 1998). Clinically, microdamage
accumulation has also been identified as a major risk factor for bone fracture (Iwata 2014). In
spite of the significance of trabecular microdamage to the mechanical health of the bone,
much is still to be understood about the relationship between local damage events and
microstructural stresses and strains.
2.5.3 Mechanical properties of trabecular bone
Apparent or continuum level analyses take into account whole bones or specimens with
multiple trabeculae (Morgan 2005). From an engineering perspective, continuum trabecular
bone forms a composite cellular solid with viscoelastic properties. The density and structure
of the trabecular bone varies across age, species and anatomical site, leading to heterogeneity
15
in mechanical properties (Keaveny 2001). The elastic modulus and yield stress of the
trabecular bone are directly proportional to the apparent bone density. Other important
factors include trabecular orientation and anisotropy ratio (Keaveny 2001). Strain, being the
ratio of yield stress and elastic modulus, forms a much simpler criterion for bone failure.
Bone in general has been shown to yield consistently at an apparent strain of approximately
1% (Keaveny 2001). As a result, strain based analysis is very important for biomechanical
study of bones.
The mechanical behavior at trabecular or tissue level is different from the apparent level in
the absence of architectural considerations. The tissue level properties can be calculated
using various techniques such as nanoindentation (Zysset 1999), atomic force microscopy
(Kinney 2000) and back calculation (Rietbergen 1995). The local compressive yield stresses
and strains for trabecular bone are included as table 2.1. Various micro-mechanical studies
have found the tissue modulus for bone to lie between 11-18 GPa (Keaveny 2001).
Table 2.1: Local compressive yield stresses and strains for trabecular bone
Authors Bone type Yield stress (MPa) Yield strain (%)
Bayraktar 2004 Human femoral
trabecular bone
133.6 0.83
Niebur 2000 Trabecular bone
cores from steers
188.9 1.01
Verhulp 2008 Trabecular bone from
bovine tibia
201 3.02
Bevill 2006 Human femoral
trabecular bone
- 0.81
Bevill 2008 Human Vertebral
trabecular bone
- 0.69
2.6 Biomechanical analysis of bone
Biomechanical investigation of diseased spines from the pre-clinical animal models includes
experimental, image-based and computational analyses (Dimar 1998, Ebihara, 2004, Hong
2004, Snyder 2006).
16
2.6.1 Mechanical testing
Mechanical testing has been widely implemented for structural analysis of healthy and
diseased spines (pre and post-treatment). Over the course of mechanical testing, force-
displacement curves are obtained, which are then used to calculate mechanical parameters
such as ultimate force, stress and stiffness (Ebihara, 2004, Hong 2004, Lo 2012).
Compressive loading through the intervertebral discs better represents the healthy
physiologic loading scenario in the spine and allows for accurate load distribution to be
applied to the vertebral endplates. Vertebral motion segments containing three vertebrae and
intact posterior elements can be used to generate physiological loading in the middle
vertebra. Mechanical testing has shown trabecular bone of the vertebrae to be more prone to
initial failure as compared to cortical bone, with the regions near the endplates exhibiting the
highest risk (Eswaran 2007).
2.6.2 Microdamage analysis through staining
A variety of techniques have been developed to detect microdamage in bone (Lee 2003).
Healthy bone contains pre-existing microdamage, originating from normal physiological
loading. As such, sequential staining of two site specific stains is required to differentiate
pre-existing and test induced microdamage (Lee 2000). Microdamage in bone cleaves bonds
in the bone matrix, exposing new surfaces with charged ions, 55% of which are Ca2+ ions
(Lee 2000). Various stains can act as chelating agents, which can bind with Ca2+ to form
stable rings. Stains such as calcein, xylenol orange and alizarin complexone can bind with
Ca2+ ions to label the micro cracks. Bone samples can be labeled with different stains before
and after mechanical loading to detect load induced damage. Sequentially stained bone
samples can be fixed and sectioned followed by analysis with bright field and fluorescent
microscopy. These sequential techniques have been shown to successfully label new
microdamage (Lee 2000). However, use of multiple chelating agents for sequential staining
has been shown to cause dye replacement (Lee 2000). En bloc staining of bone specimens
using basic fuchsin hydrochloride has demonstrated success in microdamage identification
(Burr 1995). Sequential staining with a chelating agent and fuchsin staining can circumvent
17
the problem of stain substitution. Herblum et al. has illustrated the use of calcein green and
fuchsin staining to differentiate between pre and post loading damage (Herblum 2013).
Although robust, such staining techniques are two dimensional and destructive. Non-
destructive 3-D alternatives would enable spatial correlation of damage within whole
samples. However, micro cracks on their own cannot be resolved by commercially available
micro-Computed Tomography (µCT) systems (Landrigan 2011). Barium sulfate (BaSO4)
contrast enhanced µCT imaging has been demonstrated to detect accumulation of
microdamage in trabecular and cortical bone (Landrigan 2011, Turnbull 2011, Wang 2007).
Samples are immersed in barium chloride followed by sodium sulfate under vacuum for a
fixed amount of time. Barium and sulfate ions diffuse and collect in void spaces
(microcracks), where they precipitate to form BaSO4. Measurements of contrast enhanced
microdamage in bone have been validated using scanning electron microscopy (Wang 2007)
and basic fuchsin staining/histology (Landrigan 2011, Turnbull 2011). However in this,
separate specimens were used to compare BaSO4 and fuchsin staining (the two techniques
were not applied to the same samples). Compared to histology, BaSO4 demonstrated greater
variability in staining micro cracks, as it was also found to be collected in voids and on free
surfaces (Turnbull 2011). Optimization of parameters such as staining times and
concentrations can minimize such non-specific staining. Contrast enhanced µCT does
provide a non-destructive and 3D alternative to conventional histology.
2.6.3 Finite element analysis
Numerical solutions to very complex problems in structural mechanics can be obtained using
finite element (FE) analysis. FE analysis involves discretization of a complex model into
components or elements with simple geometry. The response of the overall mathematical
model is then approximated by summation of the responses obtained at each individual
element. Generation of FE models requires proper mesh geometry, material properties and
boundary conditions (Logan 2012). The FE method finds wide applications in skeletal
biomechanics, as it allows parametric representation and analysis of complex geometric and
material property distributions. FE analysis has also been used successfully to predict
fracture and damage in healthy and diseased bone matrix (Lotz 1991, Whyne 2003).
18
Conventional continuum models consider the bone sample as a whole. Tissue properties may
be assigned to be homogeneous or as a function of image-based density. However,
continuum models ignore the trabecular level microarchitecture. This limits the ability of
such models to resolve the mechanical behavior of individual trabeculae in healthy and
metastatic regions of a diseased bone.
Micro-FE (µFE) models can be generated by directly converting the voxels of µCT data sets
into finite elements representing trabecular architecture (Figure 2.5). This allows
incorporation of the actual trabecular morphology into the FE model. However, this leads to
a tremendous increase in memory requirements and computational time. As a result, µFE has
most often been applied to assess the mechanical properties of bone cores (Gong 2007, Kim
2007, Neiber 2000, Zauel 2006). A few authors have applied this technique to whole human
(Ito 2007) and rat bones (Herblum 2013). Models have utilized isotropic elasticity to
represent the bone tissue with marrow space modeled as void to generate meshes with 8-
noded hexahedral elements (Herblum 2013, Nagaraja 2007, O’Neal 2010, Rhee 2009,
Verhulp 2008). An element size of 1/4th of the mean trabecular thickness has been shown to
yield acceptable balance between accuracy and computational cost (Guldberg 1998, Ladd
1998, Neiber 1999). Simplification of loading and boundary conditions to minimize
computation expense has been accomplished in spinal motion segments using image
registration algorithms based on comparisons of loaded and unloaded μCT scans. These
algorithms can generate vector fields which can be utilized as boundary conditions at the end
plates and facet joints (Herblum 2013, Nagaraja 2005, 2007).
19
Figure 2.5: High resolution micro-CT image of trabecular specimen (left), converted
into a voxel based micro finite element mesh (right) (Keaveny 2001)
The output parameters from µFE models typically include von Mises and principal stresses
and strains. According to Von Mises failure criterion, the material under multi-axial loading
will yield when the distortional energy is equal to or greater than the critical value for the
material. However, von Mises cannot differentiate between tensile and compressive stresses
and works best under multi-axial loading (Nagaraja 2005). Principal stress and strain have
been used extensively in µFE studies based on axial compressive loading. A measure of
principal strain in bone is of particular importance owing to its independence with respect to
bone density.
2.6.4 Image-based strain analysis
Deformable image registration of micro imaging datasets (such as µCT) is another 3D
alternative for measurements of experimental strain and displacement fields in bone samples.
Such digital volume correlation methods track the deformation of microstructural features
and patterns within unloaded and loaded image volumes to yield full field strain
measurements (Roberts 2014). This approach has found applications in studying strains and
20
Figure 2.6: Deformable image registration algorithm determines strain by aligning and
comparing scans of the same sample with and without load (Hojjat 2011).
strain patterns in metal foams (Smith 2002), agarose gel (Franck 2007), rocks (Lenoir 2007)
and collagen (Roeder 2004). Hardisty et al. extended this technique to study strain
distributions within whole rat vertebrae. The multi-resolution algorithm compared small
subsets of image data from the loaded and unloaded images to generate displacement fields,
which were then used to calculate strain (Figure 2.6) (Hardisty 2009).
Image registration has several advantages over commonly used strain analysis techniques
such as strain gauges and FE modeling. Strain gauges are limited to measurement of surface
strains (Roberts 2014) and application of strain gauges to small bones and curved surfaces,
such as rat vertebrae, presents a challenge. In contrast, image registration provides
continuum level strain fields throughout a volume and can be used to analyze small samples.
In addition, unlike FE analysis, image registration does not require definition of a mesh,
material properties assumptions or boundary conditions to determine strain (Hardisty 2009).
However, image-based strain registration requires an extensive amount of computational
time and memory. The limited resolution of such algorithms also makes it challenging to
resolve strains at an individual trabecular level. Future studies directed towards improved
computing capabilities and developing more sophisticated feature tracking algorithms may
permit the employment of digital volume correlation to study the mechanics of bone at a
trabecular level.
21
These techniques have been used extensively and with great success in studying the ex vivo
biomechanics of healthy and diseased bones. However, the ability of such techniques to
accurately reflect the in vivo behavior still needs to be verified. Such a verification will
justify the use of the ex vivo analyses to study the physiological behavior of bone.
2.7 Summary
Osteolytic metastasis in the spine leads to a decreased bone content and deteriorated
microarchitecture, which results in an increased risk of microdamage accumulation and
fracture initiation. While the effects of bone mineral density and architecture have been
studied extensively with respect to fracture risk in the metastatic spine, the micromechanics
of yielding and failure (damage) have received less attention, representing an attractive area
of research bridging the current knowledge gap. A better understanding of local damage and
failure properties in the affected vertebrae is vital for the improvement of fracture risk
assessment. A combination of mechanical loading, histological damage labeling, µCT
imaging based techniques and µFE methods, which incorporate the trabecular level
morphology of the bone, have a potential to provide more robust models and be able to better
represent the complex mechanics of the metastatic spine at the tissue level. Quantifying the
structural behavior of healthy and metastatically-involved vertebrae through advanced
experimental, computational and imaging based methods forms the central theme of the
current project. This will enhance our understanding of the potential impact of such
techniques and their potential to describe the microstructural behavior of the metastatic
spine. Together these methods may elucidate the microstructural behavior of trabeculae
within healthy and metastatic environments, which will be helpful in guiding the future use
of computational analysis in structural assessment of vertebral strength.
22
Chapter 3: Post euthanasia micro-CT based strain analysis is able to represent quasi-static in vivo
behavior of whole vertebrae.
3.1 Abstract
3D strain measurement in whole bones allows representation of physiological, albeit quasi-
static, loading conditions, however such work to date has solely been performed on
specimens post mortem. The main purpose of this study is to verify the efficacy of post
euthanasia strain based analysis to characterize the in vivo mechanical behavior of rat
vertebrae. A μCT compatible custom loading device was used to apply 75N load to a 3-level
rat tail motion segment of a healthy rat. Multiple loaded and unloaded μCT scans were
acquired before and after sacrificing the rat. A 3-D volume correlation method which
employs registration of 2 µCT images of the same specimen under similar or different
loading conditions was used to calculate strains in live and post mortem vertebrae. No
significant difference was found in the in vivo strains (-0.011±0.001) and ex vivo strains
(-0.012±0.001) obtained from the comparisons of loaded and unloaded images (p=0.3).
Comparisons between unloaded-unloaded and loaded-loaded scans yielded significantly
lower axial strains as expected. Qualitatively, high strains were observed adjacent to growth
plate regions in comparing the loaded and unloaded images. Strain patterns in the loaded-
loaded and unloaded-unloaded scans were inconsistent as would be expected in representing
noise. Overall, live and dead loaded to unloaded comparisons yielded similar strain patterns
and magnitudes. This study demonstrated a proof of concept, suggesting that post euthanasia
µCT based strain analysis is able to represent the in vivo quasi static behavior of rat tail
vertebrae.
23
3.2 Introduction
Strain measurement has been extensively employed for ex vivo assessment of bone failure
(Doblare, 2004; Kopperdahl, 1998). Experimentally, strain has been primarily measured via
strain gauges attached to bone samples undergoing biomechanical testing (Cristofolini, 2013)
yielding measurements of local surface strains. Image registration is an alternative non-
destructive approach which can spatially resolve full strain fields in three-dimensions. 3D
image registration of micro-imaging data sets, such as µCT, has been utilized to calculate
strain in excised cortical bone and trabecular bone cores (Bay, 1999; Christen, 2012;
Nagaraja, 2011; Lynch; 2004; Waarsing, 2004). Such image registration algorithms have also
been validated to accurately measure and spatially resolve strain in 3D in rat whole bones
using µCT imaging (Hardisty, 2009). In a recent study, the efficacy of applying image
registration to whole human vertebrae was also investigated (Hussain, 2012). These
algorithms work under the premise that strain fields can be computed by aligning and
comparing images of a sample acquired under unloaded and loaded configurations (Figure
2.5). During registration, the unloaded image can be defined as a grid of nodes, each of
which has a spatial region defined around it. These nodes are then mapped to the
corresponding nodes in the loaded images by matching intensity of the spatial regions around
the nodes. The nodal displacements are then used to obtain the strain field (Roberts, 2014).
Prior researchers have used this approach to measure the effect of the growth plate stiffness
(Hardisty, 2010), metastasis (Hardisty, 2012) and treatment (Hojjat, 2011) on bone strain in
whole ex vivo rat vertebrae.
However, such algorithms have primarily been utilized to study cortical and trabecular bone
specimens. Application to whole bones allows the representation of more physiological,
albeit quasi static, loading conditions. Also, most biomechanical analyses to date have solely
been performed on specimens after sacrifice. The aim of this study is to demonstrate the
ability of post euthanasia modeling to represent the in vivo quasi static behavior of whole
vertebrae through image-based strain analysis. It is hypothesized that post euthanasia µCT
image-based strain analysis represents the in vivo quasi static behavior of whole bones.
24
3.3 Methods
3.3.1 Deformable image registration algorithm
An intensity matching deformable image registration routine was previously developed and
validated (Hardisty, 2009). This method uses registration of loaded and unloaded µCT
images of the same sample to calculate strain under an applied load. The algorithm was
coded in C++ using the Insight Toolkit (ITK) and was implemented as a plug-in to
AmiraDEV (FEI Visualization Science Group, Burlington, USA). In the first step of the
algorithm, the unloaded and the loaded scans are registered using affine mapping, which
facilitates transformation using rotation, scaling, shearing and translation (12 degrees of
freedom). The initial deformable registration proceeds iteratively, optimizing the fit based
upon normalized mutual information metric. The unloaded scan is then split along the three
axes, yielding 8 pieces. Each piece is individually registered to the loaded scan, using the
affine transform from the previous registration as the initial guess. The division and
registration of sub-pieces then continues until a user defined maximum level is reached.
After the final level of registration, the affine transform is used to calculate the displacement
of the center of each registered sub-region. Continuum level displacement and strain fields
are then calculated using the following set of equations.
A(x,y,z) = TP(x,y,z,) – P(x,y,z)
e = ½ (∇AT + ∇A)
where e is the strain matrix, A is the displacement vector, T is the affine transform found
from the registration, and P is the voxel location.
3.3.2 Animal model
One healthy female Sprague Dawley rat (17 weeks old, 300g) was used for this study. In
vivo loaded μCT imaging was performed on the live rat under general anesthesia (4%
isoflurane/oxygen). After euthanasia (via intercardiac injection of 120mg/kg Euthanyl), dead
loaded and unloaded μCT images were acquired. All animal work was performed under
institutional animal care committee approval (University Health Network, Toronto, Canada).
25
Figure 3.1: Micro-CT compatible loading jig used to apply an axial compressive load to
the 6th caudal vertebra in the rat tail.
3.3.3 Loading
A μCT compatible custom loading device was used to apply live and dead axial compressive
load of 75 N to a 3-level vertebral motion segment in the rat tail. This load was previously
demonstrated to induce detectable strain in the vertebrae without fracture (Hojjat 2011). A
radiolucent jig, made from polycarbonate tube (2.5cm diameter and 20cm long), was
attached to the 5th to 7th caudal vertebrae in the anaesthetized rat via percutaneous pins (2 per
level) and attached to loading rings (Figure 3.1). The loading rings fit within a pre-calibrated
spring based loading device. The device was calibrated by loading select specimens in line
with an ELFM-T2M, 500 N capacity load cell (Entran Devices Inc., Fairfield, USA) and via
calibration within a material testing machine (MTS Bionix 858, Eden Prairie, USA)
instrumented with a 250 lb (113.4 kg). load cell in a specialized calibration set up. Load was
applied to the rat tail manually through the threaded tube. The loading rate was not
continuous, but rather, simulated as quasi-static. Prior to the load application,
preconditioning was performed using increasing load amounts, until the desired load (75N)
was stabilized. After every load application or removal, the load on the tail was allowed to
stabilize prior to scanning
26
3.3.4 Imaging
With the device in place, μCT scans of the vertebral motion segment were acquired adjacent
to bone density phantoms, with an average scan time of 90 minutes (X-ray source 80kV,
13.3μm isotropic voxel size, Inveon CT, Siemens Healthcare, Erlangen, Germany). A total of
6 μCT scans were acquired in the given sequence and configurations: live under load (1),
dead under load (2, 3), dead unloaded (4, 5) and dead under load (6). Post euthanasia scans
were acquired after sacrifice.
3.3.5 Strain calculations and data analysis
The μCT images were reconstructed and the intensities rescaled based on the bone phantoms.
The middle vertebra from each scan was cropped and the bone regions segmented using an
intensity threshold. The resultant removal of background noise from the segmentation
allowed for better registration. Each loaded scan was registered with each unloaded scan to
determine the load induced strain. Registration of scans under similar load configurations
(loaded-loaded and unloaded-unloaded) was also performed to determine the error in the
method.
Prior to registration each pair of scans was first manually aligned in 3D. A built-in module
(AffineRegistration) in AmiraDEV was then applied to the data sets to achieve accurate
alignment. Deformable registration was then implemented using four levels of subdivision.
Axial (zz) strain (percent) was evaluated as the primary outcome variable. The axial strain
distributions in the rat-tail vertebrae were characterized by calculating the mean strain,
median strain and the 10th (minimum) and 90th (maximum) percentile of the strain. T-tests
were used to evaluate differences between mean, median and 10th and 90th percentile strain
values in considering live to dead and dead to dead images. Since multiple T-tests were
performed, Bonferroni correction was applied (significance level adjusted to α=0.016).
Qualitative comparisons were also performed based on the generated strain contours within
the vertebrae.
27
3.4 Results
The mean, median, 10% and 90% strains obtained from the registration of the acquired
images are presented in Tables 3.1 and 3.2. In comparing loaded-unloaded scan
configurations (1-4, 1-5, 2-4, 2-5, 6-4 and 6-5) the applied load generated mean axial
compressive strains with an average value of -0.012±0.001. Comparisons between unloaded-
unloaded (4-5) and loaded-loaded (1-2, 1-3, 1-6, 2-3, 2-6 and 3-6) scans yielded significantly
lower mean axial strains as expected, representative of the error in the method (Figure 3.2a).
No significant difference was found in comparing live (1-4, 1-5) or dead (2-4, 2-5, 3-4, 3-5,
6-4 and 6-5) images with respect to the mean strains generated in the loaded vs. unloaded
configurations (p=0.21) (Figure 3.2b). In comparing images acquired under equivalent
loading conditions, (loaded-loaded and unloaded-unloaded) similar strain errors were found
in assessing dead to dead (2-3, 3-6 and 2-6) images vs. dead to live (1-2, 1-3 and 1-6) images
(Figure 3.2c). However, loaded-loaded comparisons yielded significantly higher mean strains
than the unloaded-unloaded comparison. The median and 90% strain values were observed to
follow the same pattern as the mean strains. The average 10% strains for all comparisons
were not significantly different.
Table 3.1: Strains (µm/µm) obtained from the comparisons of loaded-unloaded images
Mean strain 10% strain Median strain 90% strain
Live-loaded to dead-
unloaded
-0.011±0.001 0.004±0.002 -0.013±0.002 -0.023±0.001
Dead-loaded to dead-
unloaded
-0.012±0.001 0.004±0.001 -0.013±0.002 -0.027±0.004
Table 3.2: Strains (µm/µm) obtained from the comparisons of images under similar
loading conditions
Mean strain 10% strain Median strain 90% strain
Live-loaded to dead-
loaded
-0.003±0.002 0.005±0.002 -0.003±0.002 -0.010±0.004
Dead-loaded to dead-
loaded
-0.003±0.002 0.007±0.003 -0.003±0.002 -0.012±0.003
Dead-unloaded to
dead-unloaded
-0.00006 0.004 -0.0001 -0.004
28
Figure 3.3: Axial strain patterns in loaded-unloaded strain registrations. The strain is
concentrated mostly around the endplate regions. a) Dead-loaded to dead-unloaded
comparison b) Live-loaded to dead-unloaded comparison
-0.014
-0.012
-0.01
-0.008
-0.006
-0.004
-0.002
0
Mea
n A
xia
l st
rain
(µ
m/µ
m)
Loaded-loaded and Unloaded-
unloaded
-0.014
-0.012
-0.01
-0.008
-0.006
-0.004
-0.002
0
Mea
n a
xia
l st
rain
(µ
m/µ
m)
Live loaded-dead unloaded
Dead loaded-dead unloaded
-0.014
-0.012
-0.01
-0.008
-0.006
-0.004
-0.002
0
Mea
n a
xia
l st
rain
(µ
m/µ
m)
Live to dead
Dead to dead
Figure 3.2: Mean axial strains (µm/µm) obtained from deformable registration
(* represents p-value<0.016). a) Strains obtained from all loaded-unloaded and all
loaded-loaded/unloaded-unloaded comparisons. b) Comparison of live and dead
strains. c) Live and dead strains for scans under equivalent loading conditions
*
a) b) c)
Qualitatively, the strain patterns were similar in comparing live and dead loaded-unloaded
images, with high strains adjacent to the inferior and superior growth plates (Figure 3.3).
Strain patterns in the loaded-loaded and unloaded-unloaded scans were inconsistent as would
be expected since it is strain error represented in these images (Figure 3.4).
a) b)
29
Figure 3.4: Axial strain patterns generated by comparison of scans under equivalent
loading conditions. a) Live-loaded to dead loaded b) Dead-loaded to dead-loaded c)
Dead-unloaded to dead-unloaded
a) b) c)
From the lateral vertical displacement map (3.5 b, d), it can be observed that there were
minor horizontal deformations generated due to the load applied. However, since the load is
primarily applied along the z-axis, the vertical deformation is more pronounced (3.5 a, c). As
a result, subsequent analysis was focused on strain in the z direction.
30
Figure 3.5: Deformation maps generated form for live-dead images (a – vertical, b –
lateral) and dead-dead images (c – vertical, d – lateral).
a) b)
c) d)
31
3.5 Discussion
In vivo application of load to the rat tail vertebra was successfully accomplished within a live
animal µCT scanner allowing the comparison of live and post euthanasia strain
measurement. No significant difference between live and dead comparisons was found in
comparing the loaded-unloaded average axial strain measurements. The average strains were
in the range of previously reported strains in rat tail vertebrae loaded in a similar
configuration (Hardisty, 2010). The difference between the analyses of live and dead images
was within the variance found within each group. The strain was primarily concentrated
around the growth plate regions of the vertebra (Figure 3.3). Growth plates have previously
been shown to absorb the majority of strain in rat tail vertebrae (Hardisty, 2010). Growth
plates have much lower elastic modulus compared to the trabecular bone (Fuji, 2000;
Keaveny, 2001), resulting in greater deformation under load. The stiffness of growth plate in
rats increases with maturity, but was still relatively compliant in this 17-week old rat
(Villemure, 2009).
The strain obtained from the registration of loaded-loaded images (-0.003) was found to be
50 times higher as compared to that of unloaded-unloaded images (-0.00006). Hardisty et al.
found the error in strain calculation to increase, as the magnitude of strain applied increased.
This may explain part of the strain differences observed in loaded-loaded and unloaded-
unloaded comparisons. Another factor may be actual changes occurring in the load
distribution due to reapplication of the load. Changes in load between comparisons of
loaded-loaded configurations were minimized by keeping the in vivo applied load fixed
during euthanasia and for the repeated post euthanasia loaded scans (scans 2 and 3).
However, reapplication of the load for the final scan (subsequent to unloading) did not alter
the findings. Small amounts of stress relaxation in the tails in response to the applied load
may have caused variations in the strain generated across the multiple loaded images.
Nonetheless, the strains obtained from comparisons of images under similar loading
conditions are in the range of standard deviations of the strains obtained from loaded-
unloaded comparisons.
As well, measured error in strain calculation, represented by comparison of unloaded-
unloaded images, was found to be -0.00006 (accuracy), with a standard deviation of 0.004
32
(precision). Previous work utilizing this deformable registration routine was able to measure
strain in whole ex vivo rat vertebrae with accuracy and precision of 0.0003 and 0.0004
respectively, based on an unloaded zero strain case (Hardisty, 2009). The average final
window of registration used in this study was 22x22x30 pixels. The size of the region
registered influences the accuracy and precision of strain calculations. As such, increase in
the size of the registered region leads to increased precision in the strain field and a
decreased accuracy (Hardisty, 2010). The differences of accuracy and precision in the two
studies are owing to the differences in the size of the regions registered. The scale of the size
of the individual regions registered lie between the apparent and trabecular level. The
apparent strains determined in this study (~1.2%) are similar to apparent strain found
previously in trabecular bone (Keaveny, 2001).
The deformable image registration method used in this study has several advantages over
some of the traditional methods for resolving bone strain. Biomechanical testing of whole
bone samples can only resolve bulk strain and strain gage application is limited on small
bone structures. Finite element analysis (FEA) has been used to determine strain patterns in
whole vertebrae; however, physiologic loading of the spine (via intervertebral discs) greatly
increases the complexity and computational expense of such models. Accurate knowledge of
loading, geometry, material properties and boundary conditions is essential for a successful
FEA (Tsafnat, 2011). Image registration requires no assumptions, can directly be applied to
two sets of images to determine strain (Harsidty, 2009) and be used in combination with FEA
to set bone boundary conditions without modeling the intervertebral discs (Herblum, 2013).
A limitation of the study was lack of comparison of that loaded and unloaded scans with an
in vivo unloaded scan. The initial experimental design did include this acquisition; however,
the in vivo unloaded image was rendered unusable due to a motion artifact in the µCT scan.
As well, the study was focused on a single sample. The repeated measures analysis, however,
required considerable computational effort. This work on a single specimen demonstrated a
proof of concept and subsequent work can evaluate variability among multiple specimens
and loading conditions.
The image registration method used in this study relies on contrast between the bone and the
marrow/soft tissue, to accurately register the images (Hardisty, 2009). Yet it is the structure
33
size required to accurately calculate the displacement field that in fact limits the resolution
(Verhulp, 2004). The limited resolution cause the trabeculae adjacent to the relatively much
more compliant growth plates to appear highly strained. The limited spatial resolution of the
deformable registration algorithm results in measuring only the average strain within
individually registered regions based on the pattern within the region. This smooths the strain
field and yields a strain resolution that is much lower than the voxel size of the image. This
limits the ability of the technique to accurately locate small regions of high strains at an
elevated risk of damage. Efforts are currently being made in our group to improve the spatial
resolution of the algorithm using feature based registration.
3.6 Conclusion
The intensity based image registration module demonstrated that live and dead loaded to
unloaded comparisons yielded similar strains, concentrated along the inferior and superior
growth plates. Small errors in strain calculation were represented by loaded-loaded and
unloaded-unloaded comparisons. This technical proof of concept study suggests that post
euthanasia µCT based strain analysis is able to represent the in vivo quasi static behaviour of
rat tail vertebrae.
34
Chapter 4: Barium sulfate contrast enhanced μCT imaging to identify microdamage in whole rat
vertebrae
4.1 Abstract
Barium sulfate (BaSO4) acts as a contrast agent for µCT imaging. Recent studies have
exhibited the ability of BaSO4 to highlight microdamage regions within trabecular and
cortical bone sections, as well as whole femurs. Microdamage identified by BaSO4 in µCT
images has been validated against computational modeling, histologic staining and scanning
electron microscopy. BaSO4 staining provides a viable 3D and non-destructive alternative to
the lengthy 2D process of histomorphometry. In this study a series of pilot experiments were
performed to generate a robust protocol for μCT contrast enhanced (BaSO4) imaging and
histological staining, to allow the direct comparison of these techniques to represent load
induced damage within whole vertebrae. These experiments yielded staining times for whole
healthy vertebrae, established compatibility of BaSO4 with histological stains (calcein and
fuchsin) as well as demonstrated the success BaSO4 staining on whole osteolytic vertebrae.
35
4.2 Introduction
Microdamage accumulation has been identified as a risk factor for fracture initiation (Iwata
2014). A number of studies have focused their efforts towards understanding mechanisms of
microdamage initiation and propagation (Herblum 2013, Nagaraja 2007, O’Neal 2010).
However, central to these studies is the capability to accurately identify pre-existing and load
induced microdamage within the bone samples being analyzed. Histological staining has
been the primary method to characterize and quantify regions of microdamage (Lee 2003).
However, contrast enhanced µCT imaging through barium sulfate (BaSO4) staining has
recently been utilized as a 3D alternative to study microdamage accumulation in cortical
bone (Leng 2008, Landrigan 2011), trabecular bone (Wang 2007) and whole femurs
(Turnbull 2011). These studies have implemented various approaches, such as FE analysis
(Leng 2008, Turnbull 2011), scanning electron microscopy (Wang 2007, Leng 2008,
Turnbull 2011) and histomorphometry (Landrigan 2011, Wang 2007) to validate the ability
of BaSO4 to detect microdamage. While BaSO4 microdamage staining has been proven
effective, the staining parameters vary with the type of bone. The following table includes
the staining concentrations and staining times used by these four studies.
Table 4.1: BaSO4 staining parameters used for various bone types to date
Authors Type of bone Staining
time
Staining
concentration Leng 2008 Bovine Cortical bone 7 days 1 M
Landrigan 2011 Human cortical bone (old)
olihb()(old)
3 days 0.5 M
Turnbull 2011 Whole rat femur 3 days 0.5 M
Wang 2007 Bovine trabecular bone 2 days 0.5 M
Table 4.1 suggests that optimization of the BaSO4 staining times and concentration is
required as they are dependent on the type of bone to being analyzed. The current study is
aimed at performing a series of pilot experiments to adapt the BaSO4 staining protocol for
accurate detection of microdamage build up in whole healthy and osteolytic rat spines.
Calcein and Fuchsin based sequential staining (Herblum 2013) and histology will be used to
qualitatively validate BaSO4 staining. BaSO4 is not seen in fluorescent or optical microscopy
36
(Landrigan 2011) and there is no chemical limitation to this process, yet it is not known if the
physical presence of the BaSO4 may influence the calcein/fuchsin staining. As such, the
current study also aims to investigate the compatibility of BaSo4 and calcein/fuchsin.
4.3 Methods
4.3.1 Staining protocols
Calcein staining: Calcein green staining was performed to identify pre-existing trabecular
damage. 1% calcein green solution (J.T. Baker, Centre Valley, PA), prepared in distilled
water with 0.9% NaCl and 2% NaHCO3, was used for staining (Herblum 2013). Vertebral
samples were immersed in the calcein solution and placed under vacuum for 16 hours. Since
calcein undergoes photo bleaching, the vials containing the stain and stained samples were
wrapped in aluminum foil. Post staining, the samples were washed in distilled water for 1
hour on top of a shaker to remove excess stain. Excitation under blue light causes calcein to
fluoresce green.
Basic fuchsin staining: Mechanical loading induced microdamage was stained using Fuchsin
stain. The samples were immersed in a 70% ethanol solution for a minimum of 24 hours
before staining. The samples were stained in one hour sequential steps by 1% solution of
basic fuchsin hydrochloride (J.T. Baker, Centre Valley, PA) in a series of graded ethyl
alcohols (80%, 80%, 95%, 95%, 100% and 100%) under vacuum (Burr 1995). Bright field
imaging can be used to analyze histology slide stained with fuchsin.
Barium sulfate staining: For BaSO4 staining, the samples were first soaked in a solution of
equal parts by volume of distilled water, acetone and BaCl2.2H2O (certified ACS crystal,
Fisher Scientific, Fair Lawn, NJ) for the desired amount of staining time. The samples were
then immersed in a solution containing equal parts by volume of PBS, acetone and Na2SO4
for the same time as the previous step (Leng 2008, Turnbull 2011, Wang 2007). Each step
was followed by 1 hour washes in distilled water to remove excess ions.
37
4.3.2 BaSO4 staining of rat vertebrae
Intact rat spines were salvaged from healthy rats. The initial staining concentration was
selected to be 0.5M (Turnbull 2011). BaSO4 staining time was evaluated by staining 3 whole
vertebrae for 1, 2 and 3 days each. The stained samples underwent µCT scanning using an X-
ray source of 55kV at 5 μm isotropic voxel size (µCT-100, Scanco Medical, Brüttisellen,
Switzerland).
The original protocol also suggested use of phosphate bovine serum (PBS) while preparing
BaCl2 solution. However, a precipitate was observed when preparing the solution, which
suggested a possible reaction between barium and phosphate. PBS was replaced with water
in order to solve this problem.
The BaSO4 staining protocol was then applied to an osteolytic vertebra from an rnu/rnu rat.
The whole spine sample was stained with BaSO4 at a concentration of 0.5M for a staining
time of 1 day.
4.3.3 Histological validation of BaSO4 staining
Spinal segments containing 3 vertebrae were excised from two rnu/rnu healthy rats. The
segments were stained with calcein to mark pre-existing microdamage accumulation. A µCT
compatible loading device (Figure 4.1) (µCT-100, Scanco Medical, Brüttisellen,
Switzerland) was used to induce microdamage in the healthy and metastatically-involved
spine samples. The unique design of the loading device allows for accurate control of loading
parameters within the the µCT scanner. The top and bottom vertebrae of each 3 level motion
segment were potted with PMMA to prevent slipping and lateral movement of the sample
while loading. A custom jig that fits inside the loading device was designed to allow potting
of the spinal segments. The segments were preconditioned under uniaxial compression for 3
cycles at 40N at a strain rate of 3µm/s. Axial compressive loading of 100 N was then applied
to the motion segments and held static for 3 hours (Herblum 2013).
38
Figure 4.1: Loading device used to load samples inside the micro-CT scanner
After loading, the middle vertebrae were extracted from the motion segments, and were
stained with BaSO4, followed by µCT scan acquisition. The samples were then stained with
fuchsin and embedded in methyl methacrylate for subsequent histological sectioning.
Histological sections were scanned at 20x magnification to obtain bright field and green
filtered fluorescence images using the Mirax slide scanning system (Hospital for Sick
Children, Toronto). Red filtered fluorescence images were also obtained as fuchsin has been
shown to fluoresce red (Lee 2003). µCT scans of the histological slides were also obtained.
Pre-existing microcracks (calcein stained) and load induced microdamage sites (stained with
both calcein and fuchsin) were identified on the fluorescence and bright field images for
direct comparison with of BaSO4 staining of corresponding regions in the µCT scans.
Loading chamber:
Samples potted in PMMA
using a loading jig which
fits inside the chamber
Loading piston: Compressive
load applied from the bottom
Enclosure containing the
load cell and the motor for
the piston
Interface with the
scanning system
39
Figure 4.2: Micro-CT images of unloaded rat vertebrae stained for 1 day (left), 2 days
(centre) and 3 days (right). The bright spots within the vertebral body represent
BaSO4. It can be observed that staining for more than 1 day causes overstaining
(arrows indicate regions of overstaining).
4.4 Results
4.4.1 BaSO4 staining protocol for rat vertebrae
The µCT images for vertebrae stained for 1, 2 and 3 days are included as Figure 4.2. Staining
of the samples for more than 1 day resulted in nonspecific accumulation of BaSO4 in the rat
vertebrae.
The µCT image of the metastatic spine stained with BaSO4 is included as Figure 4.3. The
bright spots within the trabecular lattice are the BaSO4 stained regions of damage. No
anomalous buildup of BaSO4 was observed, confirming a successful staining.
40
Figure 4.3: BaSO4 is able to stain microdamage in osteolytic spine, without pooling.
Arrows indicate damaged regions of high intensity, consisting of BaSO4.
4.4.2 BaSO4 and Calcein/Fuchsin compatibility
BaSO4 and calcein/fuchsin were found to overlap in the selected regions. This suggested an
agreement and compatibility between BaSO4 and calcein/fuchsin staining. Figure 4.4 shows
fluorescence, brightfield and µCT images of a pre-existing damage region. Figure 4.5
includes similar images for a load induced damage region. BaSO4 can be observed to stain
both the pre-existing and load induced damage, in conjunction with calcein and fuchsin.
41
Figure 4.4: Fluorescence (a, b), brightfield (c) and µCT (d) images of a pre-existing
microdamage site highlighted by fuchsin (a, c), calcein (b) and BaSO4 (d) staining.
Arrow indicates a region of preexisting microdamage observed in fluorescent images,
but not on the bright field image.
a) b)
c) d)
42
Figure 4.5: Fluorescence (a, b), brightfield (c) and µCT (d) images of a load induced
microdamage site highlighted by fuchsin (a, c), and BaSO4 (d) staining, but not by
calcein (b)
a) b)
c) d)
The µCT images in the above figures have been created by superimposition of the
thresholded scan on the regular scan of the histology slide. BaSO4, being a contrast agent has
a higher threshold than bone, allowing the BaSO4 stained bone regions to be separated from
the unstained regions. Such an overlay allows visualization of the damaged regions as well as
the surrounding trabecular meshwork.
4.5 Discussion
The selection of initial concentration of 0.5 M for staining whole vertebrae was based upon
concentration used previously to stain whole femurs (Turnbull 2011). The staining times (1,
2, and 3 days) were based also on those used in the recent studies (Table 4.1). BaSO4 was
found to reach the trabecular bone much faster in the vertebrae since the cortical bone of rat
femur is much thicker compared to the cortical shell of the spine. Staining the spines for 1
43
day provided sufficient penetration of the stain to label the microdamage, without surplus
pooling (Figure 4.2). Based on these results, it was decided that the rat spine samples in
subsequent experiments would be stained in BaSO4 for 1 day at 0.5M concentration.
Additionally, distilled water would replace PBS in preparing BaCl2 solution.
The µCT and the bright field images (Figure 4.4 and 4.5) show that the selected damage
regions were highlighted by both BaSO4 and calcein/fuchsin sequential staining. However, it
can be noticed that despite the general locations of the damaged regions on the µCT and the
bright field images are the same, the shapes and sizes of the regions of interest differ. Similar
observations can be made with regard to the fluorescence images. These differences are not
unexpected, since the type of stains and imaging methods differ. Bright field imaging only
captures the information on the surface of the histology slide. In contrast, the µCT scan
provides 3D information about the damaged area. Higher resolution µCT images can be used
to increase the accuracy of spatial correlation of the damaged regions.
An interesting observation can be made from Figure 4.4, in that a pre-existing microcrack
(arrow), although stained with fuchsin, was not observed in the bright field image. Bright
field imaging only captures the surface of the histology slide. Fluorescent imaging has better
penetration, capturing sub-surface regions of microdamage. However, this also leads to an
increased background signal, resulting in blurrier images. Based on these observations, both
bright field and fluorescence images of the histology slides were used to identify fuchsin
stained regions of microdamage.
It can also be observed from Figures 4.4 and 4.5, that the µCT images show multiple
damaged regions surrounding the regions of interest, which were not stained by fuchsin. A
possible reason for this discrepancy could be the different size of the stain molecules. The
barium and sulfate ions are much smaller in size compared to the calcein and fuchsin
molecules. This allows BaSO4 precipitation to take place in very small microdamaged areas,
where calcein and fuchsin cannot enter. This may be an added advantage of using BaSO4
over such dyes and chelating agents.
The compatibility of calcein, fuchsin and BaSO4 originates from variance in staining
mechanisms. Calcein is a chelating agent, which combines with exposed Ca2+ ions at the
44
damaged regions (Lee 2000). Fuchsin has been reported to stain the collagen fibers of the
bone (Lee 2003). BaSO4, on the other hand, undergoes precipitation reaction to stain
microdamage in bone (Wang 2007). Since all these reactions take place independently, the
three stains are able to function without interference.
4.6 Conclusion
No studies have previously utilized sequential staining (calcein and fuschin) to identify load
induced microdamage in conjunction with BaSO4. A series of experiments were used to
establish BaSO4 staining parameters for future use in the project. Overall, this study
demonstrates that BaSO4 staining can be used in conjunction with sequential histology
staining and may be a robust method for non-destructive and 3D evaluation of microdamage
accumulation in whole vertebrae.
45
Chapter 5: Evaluation of tissue level stresses and strains under uniaxial compression of whole healthy
and osteolytic rat spines
5.1 Abstract
In this study, µCT imaging based µFE models were used to determine tissue level damage
criteria in whole healthy and osteolytic vertebrae. T13-L2 spinal segments were excised from
osteolytic (n=3) and healthy (n=3) female athymic rnu/rnu rats. Osteolytic metastasis was
generated by intercardiac injection of HELA cancer cells. Micro-mechanical axial loading was
applied to the spinal motion segments under μCT imaging. Vertebral samples underwent
BaSO4 staining and sequential calcein/fuchsin staining to identify load induced
microdamage. μCT imaging was used generate specimen specific μFE models of the healthy and
metastatically-involved whole rat vertebrae. Model boundary conditions were generated
through deformable image registration of loaded and unloaded scans. Elevated stresses and
strains were detected in regions of microdamage identified through histological and BaSO4
staining within healthy and osteolytic vertebral models, as compared to undamaged regions.
Additionally, damaged regions of metastatic vertebrae experienced significantly higher local
stresses and strains than those in the damaged regions of healthy specimens. The range of
maximum principal stresses and strains for microdamage initiation was 80±27Mpa and
0.71±0.17% in healthy samples and 127±61Mpa and 0.98±0.44%. The experimental, image-
based and computational techniques used in this study demonstrated success in identifying
and characterizing local stresses and strains in the regions of trabecular microdamage.
Knowledge gained in this work forms a strong platform for more advanced techniques for
biomechanical analyses of healthy and diseased bones.
46
5.2 Introduction
Microdamage formation within the skeleton during routine physiological loading serves as a
stimulant for bone remodeling. However abnormal buildup of microdamage leads to skeletal
fragility, especially in cancellous bone (Burr 1998). Accumulation of unrepaired
microdamage is usually the consequence of damaged microstructure and inferior bone
quality, resulting from age-related changes or skeletal pathology (i.e. osteoporosis or bone
metastasis) (Iwata 2014). Despite its significance to the mechanical properties of the bone,
the trabecular stresses and strains experienced at the initiation of microdamage are not well
characterized. Evaluation of stresses and strains associated with microdamage initiation at
the local level may offer a better platform for improvement of fracture risk assessment
techniques and the development of therapeutic methods for treatment of skeletal fragility
diseases such as metastasis.
Microdamage sites can be experimentally identified through sequential staining and
histomorphometry (Lee 2000). Microdamage staining along with high resolution micro-finite
element (µFE) models has been utilized to study local damage initiation properties of
trabecular bone. µFEA (Herblum 2013, Nagaraja 2005) has the capability to model the
trabecular morphology, allowing calculation of stresses and strains at histologically
identified damage sites (Keaveny 2001). Nagaraja et al., in a series of experimental studies,
used this approach to determine variations in microdamage initiation parameters in response
to age related changes to human and bovine trabecular bones (Nagaraja 2005, 2007, 2011,
Green 2011). However, these studies were performed in trabecular bone cores. Cores were
essential due to the massive sizes of the µFE models and the associated computational time
and memory requirements. µFE analysis of whole bone allows loading of the bone
specimens through joints and/or soft tissues, simulating more physiological loading
conditions. Herblum et al. recently demonstrated successful application of µFEA to show
elevated stresses and strains in regions containing mechanically induced microdamage within
whole healthy rat vertebrae (Herblum 2013). A summary of local stresses and strains
determined by these studies is included as table 5.1. µFEA has also been employed to study
local yield properties of cortical bone (Bayraktar 2004), as well as effects of therapeutics on
47
bone architecture (Boyd 2011). Although these two studies were not aimed at studying
microdamage, the use of µFE modelling was demonstrated.
Table 5.1: Trabecular stresses and strains at locations of microdamage determined
under axial load by previous studies
Authors Bone type Stress range (MPa) Strain range (%)
Nagaraja 2005 Bovine trabecular
bone cores
88-121 0.46-0.63
Nagaraja 2007 Bovine trabecular
bone cores
100-144 (young)
89-219 (old)
0.86-1.24 (young)
0.34-0.86 (old)
Nagaraja 2011 Bovine trabecular
bone cores
92-136 (young)
81-110 (old)
0.82-1.21 (young)
0.38-0.64 (old)
Green 2011 Human trabecular
bone cores
53-83 (young)
50-84 (old)
0.48-0.81 (young)
0.51-0.83 (old)
Herblum 2013 Whole rat vertebrae 85-128 0.9-1.4
Spinal metastasis progressively degrades the trabecular architecture of the vertebral body,
leading to an increased risk of fracture (Kurth 2001). The initiation and propagation of
unrepaired microdamage, which precede fracture, has not been quantified in metastatic
spines. This study was aimed at the development µFE models to determine the thresholds for
histologically identified damage within whole healthy and osteolytic vertebrae. The efficacy
of BaSO4 contrast agent to highlight microdamage in 3D within healthy and osteolytic spines
was also evaluated.
5.3 Methods
The workflow of this study is included as Figure 5.1. Briefly, the healthy and osteolytic
whole vertebrae were first stained with calcein to identify pre-existing microdamage. µCT
images of the unloaded samples were acquired. The samples were subjected to axial
compressive load for 3 hours and were µCT scanned while under load. The specimens were
stained with BaSO4, µCT scanned again and stained with fuchsin, followed by embedding
and histologic sectioning. Load induced microdamage sites were identified on the histology
slides through bright field, fluorescence and contrast enhanced imaging. µCT images of the
histology slides were aligned with the unloaded µCT scans of the samples. These µCT
48
Figure 5.1: Experimental design
datasets were converted directly into µFE models, which were analyzed using ABAQUS.
Deformable image registration of loaded and unloaded µCT scans was used to obtain
boundary conditions for the µFEA. Finally statistical analysis was performed to analyze the
local stress/strain data obtained from these models.
49
5.3.1 Animal models:
A previously described rat tumor model was used to investigate the biomechanical
implications of osteolytic metastasis on the trabecular bone within the spine (Hardisty 2011,
Lo 2012). Luciferase transfected HELA human cancer cells (previously described as human
MT1 breast cancer cells) were introduced in 3 rnu/rnu female nude rats (4-5 week old)
(Harlan, Indianapolis, IL, USA) via intercardiac injection under general anesthesia. Two
weeks later, bioluminescence imaging was performed to quantify tumour burden in all
tumour cell injected rats under general inhalation anaesthesia. Bioluminescence signal was
acquired using the IVIS Bioluminescent Imaging system (+ Corp., Alameda, CA, USA). The
rats were euthanized via intercardiac injection (120mg/kg euthanyl) under general anesthesia
14 days post injection. The age and weight of the three rats were between 7-8 weeks and
160-170g respectively. 3 healthy rnu/rnu female rats of similar age and weight were also
sacrificed. Spinal motion segments from T13-L2 were extracted from the healthy (n=3) and
osteolytic (n=3) rats for further analysis, with L1 as the vertebra of interest.
5.3.2 Microdamage Evaluation using calcein/fuchsin staining and
contrast enhanced μCT
Intact spinal motion segments were stained with calcein green (as described in section 4.3) to
identify pre-existing microdamage. µCT images of unloaded and loaded spinal motion
segments were acquired at an isotopic voxel size of 11.4 microns at 55 KeV and 200µA
(µCT-100, Scanco Medical, Brüttisellen, Switzerland). Following load application (see next
section), the middle vertebrae (L1) were excised and stained with BaSO4 (as described in
section 4.3) to identify load induced damage, and additional μCT scans were acquired. The
specimens were then stained with 1% basic fuchsin (as described in section 4.3), to
sequentially label de novo microdamage. The middle vertebra of each segment was stained as
a whole bone (the bone marrow was not drained and end plates and the posterior elements
were left intact). The stained samples were embedded in methylmethacrylate for histology.
Coronal sections of 60-80μm thickness were prepared from each of the embedded samples
for histology. The order of each slide was recorded, which was necessary for registration
later in the study. All the histology work was performed in the Histology Lab at the Faculty
50
of Dentistry, University of Toronto.
The histology slides were scanned at 20x magnification using bright field and green filtered
fluorescence imaging using the Mirax digital slide scanning system at the Hospital for Sick
Children, Toronto. Locations of linear microcracks were identified on the basic fuchsin
histology slide images to indicate the presence load induced microdamage on individual
trabeculae. Fuchsin stained damage regions also containing calcein were overlooked as load
induced microdamage was the focus of the current study. Although diffuse damage could be
visualized with fuchsin staining, only locations of linear microdamage were selected for
analysis. Damage regions were identified prior to µFE model creation blinded to the
individual conducting the FEA in order to prevent any bias in the results.
µCT scans of the histology slides were also acquired in order to validate BaSO4 staining
against histological staining. The presence and absence of BaSO4 in the histologically
identified damaged regions selected was examined. As well, the slides were examined to
determine the presence of BaSO4 stained sites not containing fuchsin or calcein.
5.3.3 Loading
The µCT compatible loading device (Figure 4.1) was used to induce microdamage in the
healthy and metastatically-involved spine samples. The unique design of the loading device
allows for µCT scanning of the samples while under load. The top and bottom vertebrae of
each 3 level motion segment were potted in a custom jig with bone cement to prevent
slipping and lateral movement of the sample while loading. The motion segments of the
healthy and osteolytic rats were preconditioned under uniaxial compression for 3 cycles at
40N at a constant strain rate of 3µm/s. Axial compressive loading of 100 N for healthy rat
vertebrae and 50 N for metastatic rat vertebrae were applied to the spinal motion segments
for 3 hours. These loads were experimentally found to create microdamage in the respective
types of samples without fracture (Herblum 2013, Hardisty 2011). The use of 3 motion
segments along with posterior elements was selected to mimic physiological albeit quasi-
static loading conditions.
51
5.3.4 Strain fields and boundary conditions
The deformation registration algorithm (refer to section 3.3.1 for more details) was used to
extract strain patterns by aligning and comparing the loaded and unloaded scans of healthy
and osteolytic spines (AmiraDEV 3.1) (Hardisty 2009). These strains were not directly
comparable to µFEA because of differences in resolution and hence were not used to validate
the µFE models.
A modified version of the registration algorithm (MultiResolutionRegistration) was used to
determine boundary conditions for the FE models. Using the image registration of the
loaded/unloaded scans, fifty-eighty thousand displacement boundary conditions were
extracted for the FE analysis. The calculated vector displacement fields were used to assign
FE displacement boundary conditions at surfaces of the endplates of the vertebral body and
the facet joints under axial compressive load (Nagaraja 2005, Nagaraja 2007, Herblum
2013).
5.3.5 Alignment of histology slides
All µCT scans were thresholded to segment the bone from surrounding non-bone areas. The
3D segmented µCT images of the histology slides were registered to the respective unloaded
µCT scans of the whole samples. Additional µCT images of the remaining blocks were
acquired to facilitate accurate registration of histologic slides with unloaded µCT data. The
histology blocks were first aligned using a built in automated routine in AmiraDEV 5.2.2
(Affine Registration). Having more volumetric data within the blockss (as compared to the
slide sections) makes automated alignment of the blocks simpler to perform. Once the blocks
were aligned, they revealed approximate locations of the corresponding slides (this
information was recorded while the sections were cut). Using the blocks as a reference point,
the slides were first aligned to the unloaded scan manually. Following manual alignment,
Affine Registration was employed to improve the alignment of the slide. Once a slide was
aligned, the region within the unloaded µCT dataset corresponding to the hisology slide was
identified as a separate material. The accuracy of the alignment was assessed by calculating
the volumetric concurrency (VC) of the two scans. VC was evaluated as the ratio of bone
overlap between the histology slide and the unloaded scan to the bone volume of the
52
histology slide. Post alignment, the segmented scans were downsampled to a voxel size of 35
µm. Registration of the slides allowed each histology slide to be identified within the
unloaded scan prior to µFE model generation. This allowed extraction of µFEA results for
the elements corresponding to the histology slides, for direct comparison with histologically
identified microdamage.
To register the locations of BaSO4 staining in the µFE models, the µCT scans of the
unloaded and BaSO4 stained vertebrae were utilized. As above, extraction of µFEA results
for the elements corresponding to the BaSO4 staining was limited to the area contained in the
excised slides, for direct comparison with identified microdamage.
5.3.6 Creating µFE models
The µFE models were generated in AmiraDEV 5.2.2 from the unloaded µCT scans of the
middle vertebra (with histology slides defined as separate element sets). A voxel based
meshing algorithm (Voxelator) has been implemented in AmiraDEV 5.2.2, which was used
to generate a mesh of 8-noded hexahedral elements from the segmented µCT scans. Bone
marrow and osteolytic tumor regions were left as void spaces in the models.
Abaqusinputwriter module was then used to create an Abaqus input file (ABAQUS,
Pawtucket, RI). In order to create loading surfaces, a custom built algorithm (GridToSurface)
was used to create surfaces from the voxel based mesh. The end plates and facet joints were
manually selected as loading serfaces. The vectors from the deformation field (see section
5.3.4 for more details) corresponding to the selected surfaces were assigned as boundary
conditions. DisplacementBC was used to assign the displacement vectors to the appropriate
nodes within the model. Each final Abaqus input file included the generated mesh, loading
surfaces and boundary conditions. Abaqus Standard 10-1 was used to run the finite element
models. A Young’s modulus and a Poisson’s ratio of 12.5 GPa and 0.3 respectively were
assigned as material properties for bone (Herblum 2013, Kinney 2000). Each of the model,
containint over a million elements were executed as isotropic, homogeneous, linear, static
models using the supercomputing facility at SciNet, University of Toronto.
53
5.3.7 Statistics and data analysis
The slices within the µFE models corresponding to the histology slides were isolated for
comparison with histology. For quantitative analysis, regions (n = 20) of microdamage
highlighted by the fuchsin were selected from both groups (healthy and metastatic).
Surrounding undamaged regions (n = 20) were also selected for comparison, in accordance
with previous studies (Nagaraja 2005, Nagaraja 2007, Herblum 2013). The finite elements
corresponding to these regions were selected within the models. Stress (Von Mises and
maximum principal) and strain (max principal) parameters from the damaged and
undamaged regions from both healthy and osteolytic models were extracted for comparison.
Normal distribution of this data was verified through One-Sample Kolmogorov-Smirnov Test.
T-tests were then used to compare stress/strain values in the damaged and undamaged
regions of the same group, as well as those in osteolytic and healthy bones.
Ten additional microdamage sites stained only by BaSO4 (and not calcein and fuchsin) were
selected for both healthy and metastatic models. Similar analysis was performed to compare
the stresses and strains in the damaged and undamaged regions. Additional t-tests were
performed to compare the local stresses and strains in the damaged regions stained by these
two techniques. A significance level of α=0.016 was determined using Bonferroni correction,
to adjust for multiple t-tests.
5.4 Results
5.4.1 Microdamage identification using histology
Healthy:
Figure 5.2 shows green filtered fluorescence and bright field images of the same slide from a
healthy rat. These images were visualized using the Mirax viewer, which allows zooming in
and out on the high resolution microscopy images (Figure 5.3). Both calcein and fuchsin
demonstrated the ability of staining microdamage in whole healthy vertebrae. Using these
stains before and after loading, allowed the separation of pre-existing microdamage from
damage accumulated from loading. Fuchsin was observed to stain the pre-existing as well as
54
the mechanically induced microdamage. However, since calcein only stained a priori defects,
fuchsin stained regions not containing calcein were considered to be post mechanical loading
damaged areas. This has been demonstrated in Figure 5.4. Twenty such microdamaged
regions, containing only fuchsin, were selected for analysis. Fuchsin was also found to
fluoresce red under green excitation. However damaged regions in fuchsin fluorescence
images looked similar to the edges of trabeculae. As a result, fuchsin labelling was primarily
analyzed in the bright field images.
Fuchsin stain was found to pool in the bone marrow regions (Figure 5.2). Thicker sections
increased buildup of fuchsin in the marrow; making it challenging to identify damaged
regions near the endplate. Thin sections allow much better identification of bone damage.
However, the thickness of the sections is necessary for automated alignment with the
unloaded µCT images. As found in previous studies, slices between 50-80µm thick allow
proper damage visualization, while providing enough information for 3D alignment
(Herblum 2013). However, since the bone samples are hard embedded in PMMA such
accuracy in thickness is difficult to achieve, without advanced apparatus. The thickness of
the slices acquired as a part of this project ranges between 50-140µm. The slide in Figure 5.2
is one of the thicker sections, while that in Figure 5.3 is one of the thinner sections. The
amount of pooling, and the associated difficulty in recognizing microdamage with respect to
section thickness is evident from these two images.
55
Figure 5.2: Coronal histology slide from a healthy vertebral body imaged under
fluorescence (a) to identify calcein stained pre-existing damage, and under plain light
(b) to detect fuchsin stained load induced damage
56
Figure 5.3: a) Bright field image of a histology slide from a healthy sample. b) Fuchsin
stained load induced damage on a trabecula at 20x magnification
57
Figure 5.4: a) Pre-existing damage labelled by both calcein and fuchsin b) Load
induced microdamage stained only by fuchsin, and not calcein.
a)
b)
Metastatic:
Microdamage identification in osteolytic samples was much simpler compared to the healthy
vertebrae. Better stain penetration and increased removal of excess stain was achieved as a
consequence of decreased bone density (Figure 5.5a). Lower trabecular number in osteolytic
vertebrae also allowed better definition of trabecular bone near the endplate regions. A
higher concentration of microdamaged trabeculae was observed near the endplate (Figure
5.5b), as observed in previous studies (Eswaran 2007). In general, the number and severity of
damaged sites in the metastatic samples was much greater compared to the healthy samples,
as expected.
Basic fuchsin was not found to stain the tumor tissue itself, but rather accumulated in the
surrounding regions. However, this did not affect the ability of fuchsin to highlight trabecular
microdamage (Figure 5.5b). No pooling of calcein was observed.
58
Figure 5.5: a) Coronal histology slide of osteolytic spine demonstrating reduced
trabecular number and increased fuchsin accumulation in the osteolytic regions. b)
Multiple microdamaged sites observed near osteolytic tumor tissue.
b)
a)
5.4.2 Image registration to determine strain fields and boundary
conditions
The deformable image registration described in chapter 3 of this document was used to
determine the strain fields by comparing the loaded and unloaded µCT images of healthy and
osteolytic samples. Vertical strain patterns obtained in response to uniaxial compressive
loading healthy and osteolytic samples are included as Figure 5.6. As before, the higher
strains were primarily concentrated near the growth plate area for both samples (Hardisty
2010). However, higher strains were also observed in the osteolytic regions of low trabecular
number (Figure 5.6b) well below the end plates, similar to Hojjat 2011.
59
Figure 5.6: Coronal slices demonstrating strain fields obtained for healthy (a) and
metastatic whole vertebrae. Red and blue areas experience high and low strains
respectively. Arrow denotes area osteolytic destruction under high strain.
a) b)
The axial strain distributions in the vertebrae were characterized by calculating the mean,
median and the 10th and 90th percentile strains. The average strain values for healthy and
metastatic specimens are included as table 5.2. The strain values were compared using
independent T-tests. No significant differences in strain values were observed. However, it
should be noted that the osteolytic samples were loaded to 50N while the healthy vertebrae
were loaded to 100N.
Table 5.2: Average strains (µm/µm) obtained from the comparisons of
loaded/unloaded images of healthy and osteolytic spines
Mean Median 10% 90%
Healthy -0.018±0.002 -0.012±0.002 0.0027±0.0008 -0.052±0.01
Metastatic -0.016±0.02 0.0096±0.01 0.002±0.001 -0.045±0.01
T-test p-value 0.51 0.75 0.61 0.56
60
Figure 5.7: Displacement vectors generated using deformable image registration,
represented by blue arrows generated for healthy (a) and metastatic (b) vertebrae
A modified version of the algorithm, MultiResolutionRegistration, was implemented in
AmiraDEV 5.2.2 to determine the displacement fields to be used as boundary conditions for
the µFE models. This algorithm also registers loaded and unloaded scans of a sample, to
yield a vector deformation field. Deformation fields for axial compression healthy and
osteolytic samples along the Z-direction are shown in Figure 5.6. The deformation field is
represented by a set of arrows signifying the direction and magnitude of the displacement of
the registered sub-region. Regions near the endplates show displacement primarily along the
Z axis, as expected, since it was the principal direction of axial loading. The displacement
vectors are also consistent with the axial strain patterns in Figure 5.6, where regions of high
axial strains were observed around the growth plate area
a) b)
61
Figure 5.8: Bright field image of a histology slide from a healthy slide (a) and surface
generated from µCT image of the same slide (b)
5.4.3 Alignment of histology slides with unloaded scans
Healthy:
µCT images from the slides were registered to the unloaded scans using manual and rigid
affine registration. Figure 5.8 shows a histologic image and a surface generated from µCT
scan of a slide from a healthy sample. Such surfaces were utilized during the registration
process. A registration of the block together with the histology slides is demonstrated in
Figure 5.9. The histology slides were approximately parallel to the block, making the
alignment procedure much simpler once the block was registered. A separate model was
created for each slide, to allow easy extraction of the data from the µFE models (Figure
5.10).
a) b)
62
Figure 5.9: Surface generated from registered µCT scans of the block (posterior
elements) and three slides superimposed on unloaded µCT scan from a healthy sample
63
Figure 5.10: Segmented unloaded µCT images of the same healthy sample, each
containing a histology slide identified as a separate material
The effectiveness of the combined manual and automated loading was quantified using
volumetric concurrency (VC). A built in AmiraDEV module (Quantification) was used to
measure the volume of the histology slides and intersecting areas between the slides and the
unloaded µCT scan. Segmentation allows µCT scans to be represented in binary units, where
all bone voxels were assigned a value of 1 and all non-bone regions were assigned a value of
0. Multiplication of segmented aligned histology slide and unloaded scans yielded the
intersecting regions as all the non-overlapping regions was assigned a value of 0. Volumetric
concurrencies for all slides, calculated as a ratio of the overlapping volume over the volume
of the histology slide, are included in table 5.3. The concurrencies averaged 68% for healthy
rats, with a range from 63-77%. These values are in agreement with the amount of alignment
previously seen using this technique (Herblum 2013). Figure 5.11 includes visuals of µCT
scan of the histology, along with the overlapping volume, to demonstrate successful
alignment.
Table 5.3: Volumetric concurrencies for healthy spines
Sample Number of slides Average VC for the sample (%)
Healthy 1 1 63
Healthy 2 3 68
Healthy 3 3 69
64
Figure 5.11: µCT scan of a slide from healthy sample (yellow), along with the region of
intersection between the slide and the unloaded scans (blue). The VC for this slide was
66%
Metastatic:
A similar process of registration was followed for the osteolytic samples. Alignment of the
slides with the whole bone scans was more difficult in the metastatic samples. Lower bone
volume hindered the ability of automated registration to accurately register the images. The
volumetric concurrencies for metastatic slides are included in table 5.4. Compared to healthy
samples, a lower VC of 64% was obtained, with a range from 59-79%. The overlap of a
65
Figure 5.12: µCT scan of a slide from metastatic sample (yellow), along with the region
of intersection between the slide and the unloaded scan (green). The VC for this slide
was 60%
representative histology slide and whole bone scan of a metastatic vertebra is presented in
Figure 5.12.
Table 5.4: Volumetric concurrencies for osteolytic spines
Sample Number of slides Average VC for the sample (%)
Osteolytic 1 2 62
Osteolytic 2 3 68
Osteolytic 3 3 63
66
Figure 5.13: µFE grid generated form whole bone µCT scan of a metastatic sample with
elements corresponding to a histology slide highlighted in red. One such model was
generated from each histology slide
5.4.4 µFE modeling of healthy and metastatic spines
A series of steps utilizing multiple custom built algorithms were implemented to process the
healthy and osteolytic µCT scans to create µFE grids with elements corresponding to the
histology slices localized separately. A separate model was created for every histology slide.
A single model incorporating all the slices can also be generated. However, individual
models allow easier extraction and visualization of the µFE results for elements
corresponding to the slide. Figure 5.13 demonstrates a µFE mesh containing the histology
slide from a metastatic sample identified as a separate set of elements. The displacement
vectors acquired through deformable registration (section 5.4.2) were used as displacement
boundary conditions. Loading surfaces were manually selected at the regions of direct load
transfer through the motion segment (Figure 5.14). Based on µCT scans of the loaded
samples (Figure 5.14a), facet joints and endplates were chosen to be the surfaces of direct
contact between the middle and peripheral vertebrae (Figure 5.14b). Vector displacements
overlapping the loading surfaces were inserted into the model to mimic the loading
conditions.
67
Figure 5.14: a) µCT scan of a spinal motion segment under load. b) Surfaces on the
endplates and facet joints were selected as loading surfaces for the middle vertebra
(highlighted in pink)
b)
a)
µFE models thus generated were assigned material properties to the bone elements (Section
5.3.6). The models were run at the SciNet supercomputing facility. Each model consisted
between 1-1.5 million elements and 50-80 thousand boundary conditions. 18 CPU’s, each
containing 2GB RAM, were used to run the models, for a runtime of 2-3 hours. Upon
completion of a µFEA, the elements representing the histology slides were extracted from
the whole µFE model for further analysis. Figure 5.15 shows a microscopic image of a
histology slide, along with corresponding region from the µFE model.
68
Figure 5.15: a) Bright field image of a coronal histology slide from a healthy sample
stained with fuchsin to identify load induced microdamage b) Results showing tissue
level maximum principal stress distribution in the elements within the µFE model
corresponding to the histology slide. Arrows show correspondence between the
histology slide and the model.
a) b)
5.4.5 Determining tissue-level stresses and strains in histologically
damaged and undamaged regions
Healthy:
Altogether 20 regions of load induced microdamage were selected from the fuchsin stained
histology slides of healthy samples along with 20 adjacent undamaged areas (Herblum 2013,
Nagaraja 2005). The elements corresponding to these regions of interest were identified
within the µFE models (Figure 5.16). The figure shows the maximum principal stress
69
Figure 5.16: a) Axial compressive load induced microdamage identified by fuchsin
staining. b) Elements corresponding to the damaged site selected in the undeformed
µFE model. c) Completed µFEA demonstrates elevated maximum principal stress in
the region of microdamage
distribution across the selected elements and stress concentration within the elements
pertaining to the damage site.
a)
b) c)
70
0
50
100
150
200
Von Mises Max Principal
Shre
ss (
MP
a)
Healthy spines - Stresses in
damaged and undamaged regions
Damaged Undamaged
0
0.2
0.4
0.6
0.8
1
Max Principal
Str
ain (
%)
Healthy spines - Strain in
damaged and undamaged
regions
Damaged Undamaged
Figure 5.17: Comparison of stresses (a) and strains (b) obtained from damaged and
undamaged regions in healthy samples. * represents significant differences (p-
value<0.016). Error bars represent one standard deviation from the mean.
Stress (von Mises and maximum principal) and strain (maximum principal) values at the
elements selected for damaged regions were extracted and compared to those from
undamaged regions using t-tests. As t-tests require the data to be normally distributed, a one
sample Kolmogorov-Smirnov (KS) test was utilized to verify the normal distribution of the
stress and strain values (SPSS Statistic, IBM, Armonk, NY, USA). For the assumption of
normal distribution to be true, the KS p-values must be greater than 0.05. All the µFE
stresses and strains were found to be normally distributed (table 5.5).
Paired T-tests revealed that the von Mises stresses, maximum principal stresses and strains in
the damaged regions were significantly greater than those in the undamaged regions. The
average stress/strain values in the damaged and undamaged regions and the results of T-tests
are included in table 5.5, and visually represented in Figure 5.17.
Table 5.5: Average stress and strain from damaged and undamaged regions (healthy)
Damaged (KS p-value) Undamaged (KS p-
value)
Paired T-test
Von Mises stress
122±32 MPa
(0.783)
88±30 MPa
(0.952)
0.00003
Maximum principal
stress
80±27 MPa
(0.978)
53±16 MPa
(0.956)
0.00006
Maximum principal
strain
0.71±0.17%
(0.769)
0.47±0.11%
(0.987)
0.00001
a) b)
*
71
0
50
100
150
200
250
300
Von Mises Max Principal
Str
ess
(MP
a)
Metastatic spines - Stresses in
damaged and undamaged regions
Damaged Undamaged
0
0.5
1
1.5
Max Principal
Str
ain (
%)
Metastatic spines - Strains in
damaged and undamaged
regions
Damaged Undamaged
Figure 5.18: Comparison of stresses (a) and strains (b) obtained from damaged and
undamaged regions in metastatic samples. * represents significant differences (p-
value<0.016). Error bars represent one standard deviation from the mean.
Metastatic:
Similar to the healthy samples, 20 damaged and undamaged regions were selected from a
total of 8 metastatic slides. The stress/strain values were verified for independent distribution
using one sample KS tests. As before, von Mises stress and maximum principal stresses and
strains in the damaged regions were found to be significantly higher than those in undamaged
regions. These results are listed in table 5.6 and plotted in Figure 5.18.
Table 5.6: Average stress and strain from damaged and undamaged regions
(metastatic)
Damaged (KS p-value) Undamaged (KS p-
value)
Paired T-test
Von Mises stress 156±96 MPa
(0.655)
108±35 MPa
(0.976)
0.03
Maximum principal
stress
127±61 MPa
(0.154)
78±32 MPa
(0.990)
0.0002
Maximum principal
strain
0.98±0.44%
(0.586)
0.66±0.25%
(0.877)
0.0006
a) b)
72
0
50
100
150
200
250
300
Von Mises Max Principal
Str
ess
(MP
a)
Stress in damaged regions - Metastatic
vs Healthy
Metastatic Healthy
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Principal Strain
Str
ain (
%)
Strain in damaged regions
- Metastatic vs Healthy
Metastatic Healthy
Figure 5.19: Comparison of stresses (a) and strains (b) obtained from damaged regions
in healthy versus metastatic samples. * represents significant differences (p-
value<0.016). Error bars represent one standard deviation from the mean.
The results from the healthy and metastatic models were compared using independent t-tests,
and are included in table 5.7 and Figure 5.19. The average maximum principal stress in the
damaged regions of metastatic vertebrae was significantly higher than that in healthy ones.
Similarly maximum principal strain in metastatic models was also found to be significantly
higher. Although not significant, metastatic models were found to have higher von Mises
stresses. Similar trends were observed for undamaged regions.
Table 5.7: Comparison of local stresses and strains in healthy and osteolytic models
Metastatic Healthy Unpaired T-test
Damaged Von Mises (MPa) 156 ± 96 122 ± 32 0.15
Max Principal Stress
(MPa)
127 ± 61 80 ± 27 0.004
Max Principal Strain (%) 0.98 ± 0.44 0.71± 0.17 0.02
Undamaged Von Mises 108 ± 35 88 ± 30 0.06
Max Principal Stress
(MPa)
78 ± 32 53 ± 16 0.004
Max Principal Strain (%) 0.66 ± 0.25 0.47 ± 0.11 0.006
a) b)
* *
73
5.4.6 Stresses and strains in damage regions identified by BaSO4
The BaSO4 staining protocol established in the pilot studies was tested at a broader scale in
healthy and osteolytic intact vertebral samples. Each of the 40 damage sites, selected from
the histology slides of healthy and metastatic samples, were compared with the
corresponding µCT scans for presence of BaSO4. Out of the 40 fuchsin stained damage sites,
35 were found to be enhanced by BaSO4 within the µCT scans, yielding an efficiency of
87.5%.
BaSO4 was found to stain regions of bone not stained by either calcein or fuchsin. These
regions were assumed to be finer microcracks, where only smaller barium and sulfate ions
can penetrate. Regions containing only BaSO4 (and not calcein and fuchsin) were selected in
healthy (n=10) and osteolytic (n=10) models. Same number of undamaged regions were also
selected in both models. The local damage initiation properties for microdamage sites
identified using BaSO4 contrast enhanced µCT imaging are included as tables 5.8 (healthy)
and 5.9 (metastatic). Similar to the histologically identified microdamage sites, the regions
on BaSO4 stained microdamage demonstrated elevated maximum principal stresses and
strains in the damaged regions for both healthy and osteolytic models. Average Von Mises
stresses were higher in healthy and metastatic models, but this finding was not significant
(p=0.16, 0.04).
Table 5.8: Local stresses and strains in BaSO4 contrast enhanced damaged sites
(healthy)
Damaged
(KS p-value)
Undamaged
(KS p-value)
Paired T-test
Von Mises stress 95±35 MPa
(0.998)
79±28 MPa
(0.931)
0.16
Maximum principal stress 70±26 MPa
(0.834)
42±21 MPa
(0.471)
0.002
Maximum principal strain 0.58±0.19 %
(0.822)
0.40±0.16 %
(0.453)
0.003
74
Table 5.9: Local stresses and strains in BaSO4 contrast enhanced damaged sites
(metastatic)
Damaged
(KS p-value)
Undamaged
(KS p-value)
Paired T-test
Von Mises stress 136±84 MPa
(0.222)
92±36 MPa
(0.902)
0.04
Maximum principal stress 101±29 MPa
(0.896)
65±17 MPa
(0.730)
0.001
Maximum principal strain 0.86±0.25 %
(0.323)
0.57±0.14 %
(0.937)
0.002
Regions of microdamage metastatic models were found to have higher principal stresses and
strains in the damaged and undamaged regions, as compared to healthy models. Average
Von Mises stresses were also higher in metastatic models, but this finding was not significant
(p=0.09, 0.2) (table 5.10).
Table 5.10: Comparison of local stresses and strains in healthy and osteolytic models
(BaSO4)
Metastatic Healthy Unpaired T-
test
Damaged Von Mises (MPa) 136±84 95±35 0.09
Max Principal Stress (MPa) 101±29 70±26 0.01
Max Principal Strain (%) 0.86±0.25% 0.58±0.19% 0.006
Undamaged Von Mises 92±36 79±28 0.2
Max Principal Stress (MPa) 65±17 42±21 0.008
Max Principal Strain (%) 0.57±0.14% 0.40±0.16% 0.009
Local stresses and strains in the regions of microdamage identified by the two staining
techniques are included as table 5.11. The von Mises and principal stresses and principal
strains were generally higher in the damaged and undamaged regions identified using
calcein/fuchsin sequential staining, but the differences were not significant.
75
Table 5.11: Comparison of local stresses and strains within regions of damage
identified by fuchsin and BaSO4 in healthy models
Fuchsin BaSO4 Unpaired T-
test
Damaged Von Mises (MPa) 122 ± 32 95±35 0.05
Max Principal Stress (MPa) 80 ± 27 70±26 0.3
Max Principal Strain (%) 0.71± 0.17 0.58±0.19 0.08
Undamaged Von Mises 88 ± 30 79±28 0.4
Max Principal Stress (MPa) 53 ± 16 42±21 0.2
Max Principal Strain (%) 0.47 ± 0.11 0.40±0.16 0.2
Similar trends were observed in the metastatic models (table 5.12).
Table 5.12: Comparison of local stresses and strains within regions of damage
identified by fuchsin and BaSO4 in metastatic models
Fuchsin BaSO4 Unpaired T-
test
Damaged Von Mises (MPa) 156 ± 96 136±84 0.6
Max Principal Stress (MPa) 127 ± 61 101±29 0.1
Max Principal Strain (%) 0.98 ± 0.44 0.86±0.25 0.4
Undamaged Von Mises 108 ± 35 92±36 0.2
Max Principal Stress (MPa) 78 ± 32 65±17 0.2
Max Principal Strain (%) 0.66 ± 0.25 0.57±0.14 0.3
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5.5 Discussion
Reductions in the density and quality of metastatic bone, in addition to architectural
weaknesses cause increased occurrence of microdamage and microfractures. Understanding
the initiation and the progression of microdamage to more acute forms of bone defects is
important to improve existing techniques aimed at mechanical assessment of bone. Previous
studies in trabecular bone cores and whole bones have demonstrated elevated tissue level
stresses and strains in regions of damage (Nagaraja 2007, Herblum 2013). However the
implications of osteolytic metastasis on the micromechanics of trabecular vertebrae are yet to
be investigated. In this study, specimen specific µFE models were applied to determine and
compare trabecular damage parameters in healthy and metastatically-involved vertebral
specimens.
5.5.1 Microdamage analysis using sequential staining
A sequential staining routine previously developed and optimized was used to identify
regions of pre and post loading microdamage. Calcein acts as a site specific dye for
microdamage detection, as it chelates and combines with exposed calcium ions on
microdamaged surfaces (Lee 2000). Calcein molecules absorb energy in the form of light
(blue excitation), and then emit light at a higher wavelength (green emission), which can be
captured by a fluorescence microscope. The intensity of fluorescence is proportional to the
amount of dye present. As a result regions of pre-existing damage are characterized by bright
green spots in fluorescence images (Figure 5.4). The staining protocol for calcein is well
established, and has been used widely in in vivo and in vitro studies (Lee 2003). Although
staining of whole bones is more challenging than staining of trabecular cores, excellent dye
penetration in whole vertebrae was achieved. One shortcoming of calcein green is that it can
undergo significant amount of photo bleaching when exposed to light. Also, some of the
unrepaired in vivo pre-existing microdamage sites may remain unstained as proteins and
other biological molecules may bind to the exposed Ca2+ ions.
1% fuchsin staining with graded alcohols was used to identify de novo damage in response to
uniaxial compressive loading (Burr 1995). Fuchsin labels microdamage partially by diffusing
and accumulating in the damaged region and partially by binding to exposed collagen (Lee
77
2003). As observed previously by Burr et al., fuchsin was found to stain the osteocyte
lacunae, while leaving the bone matrix unstained (Figure 5.3b). Fuchsin is a both a dichrome
and a fluorochrome – it can be visualized under bright light as well as under fluorescence.
However, microdamage appeared clearer on the bright field, compared to the fluorescence
images. One difficulty in staining with fuchsin is overstaining (Figure 5.2b). Fuchsin can
pool inside the bone marrow, making microdamage identification difficult. The most
dominant cause of overstaining is insufficient rinsing of the samples post staining (Burr
1995). Excess stain easily diffuses out of trabecular and cortical bone sections. However, the
problem of overstaining is more pronounced in whole vertebrae, as the cortical shell
surrounding the trabecular centrum inhibits the elimination of surplus stain. Increasing post
staining rinse time in future studies may facilitate increased diffusion of the extra stain out of
the bone, yielding improved definition of microdamage sites. Removal of endcaps prior to
fuchsin staining would allow even better penetration and removal of excessive stain and
allow elimination of bone marrow, which have been shown to improve results of fuchsin
staining (Nagaraja 2007). Since fuchsin staining is performed after mechanical loading and
µCT imaging, removal of endcaps will not affect the loading conditions or “whole bone”
assumption for subsequent FE models. However removal of endcaps can cause additional
damage to the bone, which would be stained in subsequent staining procedures.
5.5.2 Alignment of histology slides
Nagaraga et al. had previously implemented a two-dimensional automated registration
program to align bone histology slides with µCT images. The program used iterative rotation
and translation of the slides till optimum alignment was achieved (Nagaraja 2007). However,
the authors experienced difficulty achieving perfect alignment due to thresholding and
sectioning artifacts originating from histological processing as well as the use of different
imaging modalities. Also, they were not able to quantify the efficiency of registration. In the
current study, µCT imaging of thick histology slides allowed for volumetric registration
between the histology slides and the unloaded µCT scans. While the increased thickness of
the histological sections (50µm-150 µm) increased the complexity of fuchsin stained
microdamage identification, use of thick bone sections facilitated the use of automated rigid
volumetric registration routine in AmiraDEV for improved efficiency of the alignment
78
process. The registration of posterior elements provided initial estimates for the location of
the histology slides to guide alignment.
The 3D slice registration also allowed quantification of the efficiency of alignment. The
volumetric concurrencies were determined as the ratio of the volume of the slide and the
volume of overlap between the slide and the unloaded scan. The volumetric concurrencies
achieved in this study (68% for healthy samples and 64% for metastatic samples) were
deemed acceptable according to those attained previously (Herblum 2013). The alignment
was enough to identify identical trabeculae on the slide and the unloaded scan (Figures 5.11
and 5.12). Higher volumetric concurrencies could not be achieved because of artifacts
introduced by histological processing. Some of the histological sections had variable
thickness across their lengths. Use of more advanced equipment to cut more uniform section
may help to increase the degree of alignment. The efficiency of alignment suffered in the
metastatic samples as a result of decreased bone content resulting from osteolytic activity.
Thicker histology slices may lead to better registration, albeit at the cost of accuracy of
microdamage identification. Use of higher resolution µCT images for registration have the
potential to yield better alignment, without increasing the slice thickness. A two-fold increase
in resolution provides eight-fold volumetric data for automated registration.
Accurate registration of the histology slide to the unloaded µCT images of whole vertebrae
represents an important component of this procedure. This allowed direct comparison of
histologically identified load induced microdamage with the results from µFE analysis.
Registration of the slide to the unloaded scans also allowed for verification of BaSO4 staining
against the gold standard histological staining.
5.5.3 Trabecular stresses and strains in histologically identified
microdamage
The primary objective of the current study was to determine and compare trabecular level
stresses and strains in the damaged and undamaged regions of healthy and osteolytic
vertebrae. Von Mises stress, maximum principal stress and maximum principal strain were
considered as output variables. These parameters are widely used in FEA and µFEA
(Keaveny 2001). Von Mises criterion is based on distortion energy failure theory, which
79
gives equivalent stress at an element in the FE acted upon by normal and sheer stresses from
all directions (Shigley 2001). However, von Mises stress does not distinguish between tensile
and compressive stresses and as a result is inadequate to describe the tensile/compressive
anisotropy of bone failure originating from uniaxial compressive loading conditions. The
principal strain failure criterion presumes that failure occurs when the greatest principal
strain (axial strain in this particular case) surpasses the yield strain (Shigley 2001). With
known material properties, maximum principal stress can be directly calculated from the
principal strain. Strain based failure criteria have been widely employed in the study of bone,
as bone has been consistently shown to yield at strain values near 1% at a continuum level
(Keaveny 2001). Local stresses and strains are reported in this dissertation in terms of
maximum principal stresses and strains similar to previous studies researching trabecular
failure under uniaxial loading (Herblum 2013, Nagaraja 2005, Nagaraja 2007).
Significantly elevated maximum principal stresses and strains were witnessed in the
damaged regions as compared to undamaged ones for the whole healthy vertebrae.. Damage
initiation was considered to occur at stress/strain values between those found in the
undamaged and damaged regions. Trabecular microdamage for healthy vertebrae initiated
between stress and strains in the range of 53-80 MPa and 0.47-0.71% respectively. Using a
similar methodology on whole vertebrae, Herblum et al. found damage to initiation at 85-128
MPa stress and 0.9-1.4% strain. However, Herblum et al. used Wistrar rats, which are much
bigger in size compared to rnu/rnu rats (Herblum 2013). Use of different strains of rats may
imply differences in microarchitecture, which may account for the variations in local stresses
and strains observed in the two experiments. Using 2D registration of histology slides to the
µFE models, Nagaraja et al. demonstrated that damage initiated between 100-144 MPa and
0.86-1.24% in young bovine trabecular cores, and between 89-219 MPa and 0.34-0.86% in
older bovine trabecular sections (Nagaraja 2007). These results were later adjusted to 92-
132MPa and 0.82-1.21% for younger specimens and 81-110 MPa and 0.38-0.64% to older
specimens (Nagaraja 2011). The alterations in local mechanical properties were attributed to
age related mineral and architectural changes in trabecular bone. Another study by the same
group revealed that microdamage in human trabecular bone cores initiated in the range of 53-
83 MPa and 0.48-0.81% for younger donors. Interestingly, they found no significant
differences in local damage initiation thresholds for older specimens (50-84 MPa and 0.51-
80
0.83%) (Green 2011). It can be inferred from these studies that there exists a great intra and
inter-specimen diversity in local damage initiation properties.
Just like in healthy samples, damaged regions exhibited significantly higher maximum
principal stresses and strains in metastatic vertebrae. Damage in osteolytic specimens
initiated between stress and strain range of 78-127 MPa and 0.66-0.98% respectively.
Herblum reported microdamage initiation in a mixed metastatic model in an rnu/rnu rat
vertebra at 224-447 MPa and 2.5-4.4% (Herblum 2012) - much higher than those observed in
this study. However, local bone mechanics vary significantly with the type of tumor, giving
rise to differences in failure initiation properties. Compared with healthy samples, the
metastatic trabeculae experience significantly higher stresses (127 MPa vs 80 MPa) and
strains (0.98% vs 0.71%) in the damaged regions. This is in spite of the fact that healthy
samples experienced twice the axial load than the metastatic ones. However, table 5.2 reveals
that despite differences in loads applied, the axial strains generated in the two groups were
similar. Metastatic samples have lower trabecular number, as well as trabecular thickness
(Hojjat 2011). This implies that at similar strains, the individual trabecular struts in osteolytic
spines have regions of higher stress concentrations compared to healthy spines. This has been
observed in a clinical setting, where patients suffering from metastasis experience fractures
even at normal physiological loads (Whyne 2003). Also, since the metastatic spine is a
weaker structure compared to healthy spine, it is closer to bulk yield when loaded under
similar strain. Damaged regions have been shown to experience higher stresses and strains
when loaded to strains closer to the yield strain (Nagaraja 2005). Additionally, damaged
regions in metastatic models had much higher standard deviations associated with local
stresses and strains. Varying degrees of bone destruction was observed across the three
metastatic models. Models with higher bone destruction (lower trabecular density and
number), were found to have higher damage initiation ranges, giving rise to increased
variations in local stress and strain values.
The undamaged regions of metastatic samples experienced much higher principal stresses
and strains compared to the healthy ones (78 MPa and 0.66% vs 53MPa and 0.47%), which
was initially not expected. One possible explanation for this discrepancy is that, the
undamaged regions selected were adjacent to the damaged regions. The trabeculae of interest
81
within the metastatic model were already under a high strain. As a result, the undamaged
regions selected on such trabeculae were predisposed to have higher stresses and strains. A
recent study by Boyd et al. demonstrated that increase in trabecular number and density in
response to a drug treatment led to a better distribution of stress within the trabecular
network, lowering local stresses in response to the load applied (Boyd 2011).
The study did not aim to predict the stress/strain values at which microdamage initiation
occurs; but rather define a range of values where the damage may begin. The selection of
microdamage sites in the healthy and metastatic slides was not exhaustive, but represented a
subset of overall microdamage within the samples since not all microdamage sites on the
histology slides could be visualized. Future work may establish the cut-off stress/strain
values for microdamage initiation using µFE models that incorporate the overall presence of
microdamage within the whole samples using BaSO4 staining.
5.5.4 Microdamage identification using BaSO4 contrast enhanced
imaging
The effectiveness of BaSO4 as a possible 3D alternative to sequential staining and histology
was evaluated. The protocol for BaSO4 staining is much simpler compared to that of
histomorphometry. Samples are simply immersed in solutions containing barium and sulfate
ions that diffuse and accumulate in void spaces, which act as nucleation sites for BaSO4
precipitation. Buildup of BaSO4 within microcracks results in increased intensity in the µCT
scans due to a higher X-ray attenuation than the surrounding undamaged bone. BaSO4
staining provides enhanced contrast for the identification of microdamage that is otherwise
not able to be recognized using standard µCT scanners available in the market (Landrigan
2011).
In this study, BaSO4 staining was not used as the primary damage criteria. Rather, the
adjusted protocol (chapter 4) was tested at a larger scale on healthy and osteolytic samples.
Excellent correlation was detected between regions of microdamage identified by BaSO4 and
fuchsin. Out of the 40 histological regions of microdamage selected from the healthy and
metastatic samples, only 5 were not recognized by BaSO4 conceding an efficiency of 87.5%.
The lack of complete concurrency may be due in part to non-ideal registration of the slides to
82
the µCT scans of the whole vertebrae (as quantified by the volumetric concurrency
measurements of 68% and 64% in healthy and metastatic vertebrae respectively). As well,
the shape and size of the damaged regions categorized by the two staining modalities were
different. BaSO4 also labelled calcein stained pre-existing microdamage, and thus is not
specific to load induced damage. Sequential staining with BaSO4 could overcome this
challenge. Overall, successful implementation of BaSO4 staining in whole healthy and
osteolytic vertebrae represents a significant step forward to establish this technique as a
powerful 3D volumetric complement to microscopic methodologies.
BaSO4 was found to stain some regions of bone not stained by calcein or fuchsin. µFE results
for such regions from the healthy and metastatic models, were similar to the results for
histologically identified microdamage. Significantly greater stresses and strain were observed
in these damaged regions compared to undamaged regions. Again, the stresses and strains in
the damaged regions in the metastatic models were higher than those in healthy models. In
comparing stresses and strains in the regions identified by histology and BaSO4, the stresses
and strains were consistently lower in BaSO4 enhanced damage sites within healthy and
metastatic models, but these differences were not significant. These regions may represent
finer microcracks, since they were unstained by both calcein and fuchsin. Nagaraja et al. had
demonstrated that more severe regions of damage yield higher stresses and strains within the
µFE model (Nagaraja 2005). As such, the calcein/fuchsin stained regions of microdamage
represent more severe form of damage and consequently yield slightly higher stresses and
strains. It should be noted, though, that some of the BaSO4 enhanced microdamage sites may
be pre-existing, as BaSO4 staining was performed only after the load was applied. This may
be another reason for lower stresses and strains in the BaSO4 highlighted regions of damage.
Although robust, BaSO4 staining was found to be non-specific for microdamage
identification, i.e. all void spaces including lacunae, canaliculae, free surfaces and
vasculature were the sites for precipitation (Leng 2008). This led to increase in the median
intensity in the undamaged regions as well. Non-specific buildup of BaSO4 may be identified
separately from regions of microdamage using morphological criteria (Landrigan 2010)
Also, BaSO4 staining is more of a qualitative assessment, rather than a quantitative measure.
Determination of conventional quantitative histomorphometric measures such as microcrack
83
density, crack length, etc. would require a resolution greater than 10 microns (Landrigan
2010). Yet this method is capable of identifying damaged trabeculae. Additionally BaSO4
cannot distinguish between diffuse and linear microdamage at the current resolution (20
microns). Higher resolution images may allow better characterization of the morphology of
BaSO4 stained regions, allowing differentiation between diffuse and linear damage.
5.5.5 µFE modeling
Deformable registration was used to determine the boundary conditions for the µFE models.
A similar algorithm was used to determine strains and strain fields in the healthy and
metastatic vertebrae (Hardisty 2009). Despite differences in loads, similar bulk strains were
achieved for healthy and metastatic samples, thus emphasizing the weakness of metastatic
vertebrae. However, due to its limited resolution, the deformable registration algorithm is
unable to elucidate strain patterns within individual trabeculae. The significant differences in
the resolution of the deformable registration algorithm and the µFE models render their
results incomparable.
µFE models and deformable registration exhibited high stresses and strains in the growth
plate regions (Hardisty 2010, Herblum 2013, Hojjat 2013). Higher BaSO4 and fuchsin
stained damaged regions were observed in the end plate regions. The growth plates, which
remain unfused in rat vertebrae (Horton 2008), were not included in the µFE models used in
this project. Only bony regions were segmented to create the models, while the growth plate
regions were left as void spaces. Although the biology of growth plates and their roles in
bone development have been extensively studied, not much is known about how the presence
of growth plate affects the stresses and strains experienced by the surrounding trabeculae.
The elastic modulus of growth plates has been found to be much lower (0.57-1.41MPa) than
that of the surrounding trabecular bone (Cohen 1994, Fujii 2000). In order to capture the
complexity of the growth plate regions, higher resolution µCT scans as well as µFE models
will be necessary. With the implementation of high resolution image-based (µCT and µMR)
micro finite element modeling, future work may incorporate the role of growth plates to yield
a better representation of the mechanical behavior of whole rat vertebrae.
84
Although this study is an important step towards understanding the micromechanics of
healthy and pathologic vertebrae, further improvement in the µFE modeling approach may
provide more accurate results. µFE models were generated at a resolution of 35µm. This
element size provided sufficient convergence for µFE analysis as outlined in the literature
(Ladd 1998, Niebur 1999). However, the endplate region of the vertebrae contains a high
trabecular number, with a low trabecular thickness. As a result, the trabecular network near
the growth plates appear to be fused, and the microdamage within these regions cannot be
resolved. Lower resolution also causes the mechanical parameters in the damaged area to be
averaged over a larger volume of the individual element, thus underestimating the stresses
and strains experienced. µCT scanners can provide resolution greater than 5 microns; µFE
models at such high resolutions have the potential to address both these issues, leading to
more accurate damage initiation criteria. However, the size of the models in whole bones
remain a computational challenge. Reducing the resolution of non-critical regions of the
models may be a possible approach to reduce computational costs (Herblum 2013).
The current study used a linear elastic property assignment to represent bone. More advanced
non-linear µFEA may allow definition of more accurate range for microdamage initiation.
The stresses and strains for microdamage initiation identified in this study are within the
bounds of those observed previously (Table 5.1). However some of these values exceed the
local yield stresses and strains reported in human and bovine trabecular bone (Table 2.1).
Although, no such data is available for rat trabeculae, the local yield stresses and strains
included in table 2.1 provide a rough estimate. The assumption of linear elasticity of bone
even after the yield point may have overestimated the stresses and strains in the damaged
regions. Nonlinear µFE modeling may provide more realistic results by accounting for
material softening during damage initiation and stress redistribution to surrounding
undamaged elements (Nagaraja 2007). While homogeneous material properties were
assigned to the finite element meshes, intensity based specimen specific inhomogeneous
material property assignment have been shown to yield slightly more accurate local stresses
and strains in µFE models (Nagaraja 2007). Further, identical elastic moduli were utilized to
represent bone in both healthy and osteolytic vertebrae. However, metastasis has been shown
to alter material properties such as tissue density, mineralization and hardness which can be
represented by a decrease in elastic modulus as compared to normal bone (Nazrin 2008).
85
Using similar modulus of elasticity as in healthy models may have led to overestimation of
local stresses and strains in the metastatic models. Back-calculation can be used to determine
more accurate specimen specific elastic moduli to obtain more realistic local failure
parameters (Evans 2012). Alternatively, nanoindentation studies in healthy and metastatic
vertebrae can provide mechanical properties (Wolfram 2010). The implications of metastasis
on local material properties are currently being investigated in our research group.
Linear microcracks were the focus of the study, since BaSO4 staining could not distinguish
between linear and diffuse damage at the resolution used. However, significant amount of
diffuse damage originating from bending and buckling of trabecular struts may be present in
the healthy and osteolytic vertebrae. . A future study could involve comparing microdamage
initiation histologically identified linear and diffuse damage. Uniaxial compression was the
sole mode of loading considered in this study. Compressive loads were applied through
intervertebral discs to simulate “physiological loading”. Although such loading condition
does provide insight into damage initiation of trabecular bone, use of multi-axial loading
conditions could provide more clinically relevant results. Investigating trabecular failure
under multi-modal loading (compression, torsion, bending, etc.) and fatigue protocols will
provide a more comprehensive description of local failure. In vivo vertebrae are subjected to
multi-modal and multi-axial loads, and studying their effects on trabecular failure may lead
to a more complete understanding of physiological microdamage initiation.
86
5.6 Conclusion
In this research study, specimen specific µFE vertebral models were developed through
voxelation of µCT images. µFE models of healthy and metastatic (osteolytic disease)
vertebrae were successfully generated with accurate integration of histologically defined
regions based on volumetric image registration and displacement boundary conditions based
on loaded/unloaded µCT image registration. Microdamage was histologically identified
through a whole bone sequential calcein/fuchsin labelling technique. The ability of BaSO4
contrast agent to highlight regions of microdamage in intact specimens was verified against
histological staining. In comparing regions of damage identified by the two staining
techniques to the undamaged regions in the µFE models, significantly higher stresses and
strains were recorded in the damaged regions. This advanced modelling technique provides a
new robust method for validating the ability of µFE modeling to quantify damage in skeletal
structures. This technique has great potential for application to the analysis of healthy or
pathologic whole bones under complex physiologic loading conditions.
87
Chapter 6: Concluding remarks
Trabecular microdamage has been shown to play a vital role in physiological maintenance,
but its unrepaired accumulation in aged and diseased conditions is detrimental to skeletal
integrity. The experimental techniques developed as a part of this project have demonstrated
the ability of ex vivo loading to represent in vivo quasi-static behavior and allowed effective
multimodal identification of microdamage in healthy and metastatic whole bones.
Experimental and quantitative computational techniques were further utilized to characterize
trabecular level stresses and strains associated with microdamage. The work presented in this
dissertation is significant because it improves our understanding of trabecular bone
microdamage initiation in healthy and diseased bone and unlocks exciting future research
directions that may contribute to the development of strength assessment techniques for
fragility diseases such as metastasis and osteoporosis.
In future work, more accurate results may be achieved with the use of higher resolution μ-
imaging based μFE models, incorporating non-linear modelling and more accurate material
properties. The methods highlighted in this thesis project can be utilized to evaluate the
ability of μFEA to categorize microdamage resulting from other loading conditions (i.e.
bending, flexion/extension, torsion, and other complex loading patterns) in vertebrae and
other metastatic states (mixed and osteoblastic metastases). The microdamage identification
and modeling process may also be applied to analyze other whole bone structures. The
utilization of BaSO4 staining allows full field volumetric identification of microdamage. This
represents a critical step in determining the potential predictive ability of μFEA to identify
locations of load induced microdamage. Sequential staining with BaSO4 could be used for
such analysis. Ultimately, μFE-microdamage analysis may be applied to evaluate the
mechanical integrity of healthy bones, age related alterations, pathologically involved bones
and bones in the later stages of fracture healing.
Direct clinical application of μFE modeling to assessing human vertebrae or other bones is
challenging because of the huge computational and memory requirements. However, μFE
models could be used to focus on locations predisposed to high fracture risk. The techniques
developed in this dissertation may also be used to assess the efficacy of treatments for
88
metastasis (i.e. bisphosphonates, radiation therapy or photodynamic therapy). The direct
clinical applications of the computational techniques developed in this project may seem
distant; however, the robust experimental protocols and methodology of developing high
resolution finite element models may be used for ex vivo analysis of variety of bone
specimens. These techniques also form a solid platform for further development of optimal
methods for modeling the material and structural behaviour of skeletal tissues. A superior
understanding of the micromechanical behaviour of the bones may guide the development of
assessment methods allowing clinicians to evaluate treatments to prevent fractures and
identify patients at an elevated fracture risk.
89
Chapter 7: References
Banse, X., Devogelaer, J.P., Munting, E., Delloye, C., Cornu, O., Grynpas, M., 2001
Inhomogeneity of human vertebral cancellous bone: Systematic density and structure
patterns inside the vertebral body. Bone 28, 563–571
Bay, B.K., Smith, T.S., Fyhrie, D.P., Saad M., 1999. Digital volume correlation: three-
dimensional strain mapping using x-ray tomography. Experimental Mechanics 39,
217-226.
Bayraktar, H.H., Morgan, E.F., Niebur G.L., Morris, G.E., Wong, E.K., Keaveny, T.M.,
2004. Comparison of the elastic and yield properties of human femoral trabecular and
cortical bone tissue Journal of Biomechanics 37, 27–35
Bhatt, A.D., Schuler, J.C., Boakye, M.,Woo, S.Y., 2013. Current and emerging concepts in
non-invasive and minimally invasive management of spine metastasis. Cancer
Treatment Reviews 39-2, 142-152
Bilsky M.H., 2005. New therapeutics in spine metastases. Expert Reviews in
Neurotherapeautics. 5, 831.
Blouin, S., Basle, M. F., Chappard, D., 2005. Rat models of bone metastasis. Clinical and
Experimental Metastasis 22, 605-614.
Boyd, S.K., Szabo, E., Ammann, P., 2011. Increased bone strength is associated with
improved bone microarchitecture inintact female rats treated with strontium ranelate:
A finite element analysis study. Bone 48, 1109–1116
Burr, D. B., Hooser, M., 1995. Alterations to the en bloc basic fuchsin staining protocol for
the demonstration of microdamage produced in vivo. Bone 17, 431-433.
Burr, D.B., et al., 1998. Does microdamage accumulation affect the mechanical properties of
bone? Journal of Biomechanics 31(4), 337-45.
Carbonare, L.D., Giannini, S., 2004. Bone microarchitecture as an important determinant
of bone strength (Review). Journal of Endocrinological Investigation 27(1), 97-105.
Clarke B., 2008. Normal Bone Anatomy and Physiology. Clinical Journal of the American
Society of Nephrology 3, S131-S139.
Christen, D., Levchuk, A., Schori, S., Schneider, P., Boyd, S.K. , Müller, R., 2012.
Deformable image registration and 3D strain mapping for the quantitative assessment
of cortical bone microdamage. Journal of the Mechanical Behavior of Biomedical
Materials 8, 184-193.
Christiansen, B.A., Bouxsein, M.L., 2010. Biomechanics of Vertebral Fractures and the
Vertebral Fracture Cascade. Current Osteoporosis Reports 8, 198–204
90
Cohen, B., Chorney, G. S., Phillips, D. P., Dick, H. M., Mow V. C., 1994. Compressive
stress-relaxation behavior of bovine growth plate may be described by the nonlinear
biphasic theory. J. Orthop. Res. 12:804–813,
Coleman R.E., 2001. Metastatic bone disease: Clinical features, pathophysiology and
treatment strategies. Cancer Treatment Reviews 27, 165-176.
Constans J.P., deDevitiis E., Donzelli R., et al., 1983. Spinal metastases with neurologic
manifestations: Review of 600 cases. Journal of Neurosurgery 59, 111-8 (1983).
Cortet, B., Marchandise, X., 2001. Bone microarchitecture and mechanical resistance. Joint
Bone Spine 68, 297-305
Cossigny D., Quan G.M., 2012. In vivo animal models of spinal metastasis. Cancer
Metastasis Reviews. 31, 99–108.
Cristofolini, L., Brandolini, N., Danesi, V., Juszczyk, M.M, Erani, P., Viceconti, M., 2013.
Strain distribution in the lumbar vertebrae under different loading. The Spine Journal
13, 1281-1292
Curtis C., Skrinskas T., Clemons M., Whyne C.M., 2007. Changing disease patterns in breast
cancer metastasis to the spine. In proceedings of the 53rd Annual Meeting of the
Orthopedic Research Society, San Diego, USA.
Dimar J.R., Voor M.J., Zhang Y.M., Glassman S.D., 1998. A human cadaver model for
determination of pathologic fracture threshold from tumorous destruction of the
vertebral body. Spin. 23, 1209-14.
Doblare, M., Gracia J.M., Gomez, M.J., 2004. Modelling bone tissue fracture and healing: a
review. Engineering Fracture Mechanics 13-14, 1809-1840.
Donnell, P. E., Hugh, M.C., Mahoney D.O., 2007. Vertebral Osteoporosis and Trabecular
Bone Quality. Annals of Biomedical Engineering 35-2, 170-189.
Ebihara H., Ito M., Abumi K., Taneichi H., Kotani Y., Minami A., Kaneda K., 2004. A
biomechanical analysis of metastatic vertebral collapse of the thoracic spine: a sheep
model study. Spine 29, 994-9.
Engelke, K. 2012. Assessment of bone quality and strength with new technologies. Current
Opinion in Endocrinology, Diabetes and Obesity 19(6), 474-482
Eswaran, S. K., Gupta, A., Keaveny, T. M., 2007. Locations of bone tissue at high risk of
initial failure during compressive loading of the human vertebral body. Bone 41, 733-
739.
Evans, S.P., Parr, W.C.H., Clausen, P.D., Jones A., Wroe, S., 2012.Finite element analysis of
a micromechanical model of bone and a new 3D approach to validation. Journal of
Biomechanics 45, 2702–2705
91
Fields, A.J., Eswaran, S.K., Jekir, M.G., Keaveny, T.M., 2009. Role of trabecular
microarchitecture in whole-vertebral body biomechanical behaviour. Journal of Bone
and Minerl Research 24 (9), 1523-1530.
Frost, H.M., 1994. Wolff’s Law and bone's structural adaptations to mechanical usage: an
overview for clinicians. Angle Orthodontist, 64-3, 175-188.
Franck, C., Hong, S., Maskarinec, S. A., Tirrell, D. A., Ravichandran, G., 2007. Three-
dimensional Full-field Measurements of Large Deformations in Soft Materials Using
Confocal Microscopy and Digital Volume Correlation. Experimental Mechanics 47-
3, 427-438.
Frost, H.M., 1960. Presence of microscopic cracks in vivo in bone, Henry Ford Hospital
Med. Bul.
Fujii, T., Takai, S., Arai Y., Kim, W., Amiel, D., Hirasawa, Y., 2000. Microstructural
properties of the distal growth plate of the rabbit radius and ulna: biomechanical,
biochemical, and morphological studies. Journal of Orthopedic Research 18, 87–93.
Fyhrie, D.P., Schaffler, M.B., 1994. Failure Mechanisms in Human Vertebral Cancellous
Bone. Bone 15, 105-9
Goldstein, R. H., Weinberg, R. A., & Rosenblantt, M., 2010. Of mice and (wo)men:
Mouse models of breast cancer metastasis to bone. Journal of Bone and Mineral
Research 25, 461-436.
Gong H., Zhang M., Qin L., Hou Y., 2007. Regional variations in the apparent and tissue-
level mechanical parameters of vertebral trabecular bone with aging using micro-
finite element analysis. Annals of Biomedical Engineering 35, 1622-31.
Guise T.A., 2000. Molecular mechanisms of action of osteolytic bone metastases. Cancer 88,
2892-2898.
Guldberg, R. E., Hollister, S. J., Charras, G. T., 1998. The accuracy of digital image-based
finite element models. Journal of Biomechanical Engineering 120, 289-295.
Hardisty, M.R., Whyne, C.M., 2009. Whole bone strain quantification by image registration:
a validation study. Journal of Biomechanical Engineering 131, 064502.
Hardisty, M.R., Akens, M., Yee, A.J. Whyne, C.M., 2010. Image registration demonstrates
the growth plate has a variable effect on vertebral strain. Annals of Biomedical
Engineering 38, 2948-55.
Hardisty, M.R., Akens, M.K., Hojjat, S.P., Yee, A., Whyne, C.M., 2012. Quantification of
the Effect of Osteolytic Metastases on Bone Strain within Whole Vertebrae Using
Image Registration. Journal of Orthopedic Research 30, 1032-1039.
92
Herblum, R., 2011. Evaluation of micro level structural integrity of the spine through micro
finite element modeling and histological analysis. PhD. Thesis. Springer, Toronto
Herblum, R., Beek, M., Whyne, C.M., 2013. μfEA successfully exhibits higher stresses and
strains in microdamaged regions of whole vertebrae. Journal of Orthopaedic Research
31, 1653-1660.
Hernandez, C.J., Tang, S.Y., Baumbach, B.M., Hwu, P.B., Sakke, A.N., van der Ham, F.,
DeGroot, H., Bank, R.A., Keaveny, T.M., 2005. Trabecular microfracture and the
influence of pyridinium and non-enzymatic glycation-mediated collagen cross-links.
Bone 37 825–832.
Hildebrand, T., Laib, A., Muller, R., Dequeker, J., Ruegsegger, P., 1999. Direct three-
dimensional morphometric analysis of human cancellous bone: Microstructural data
from spine, femur, iliac crest, and calcaneus. Journal of Bone Mineral Research 14,
1167–1174, 1999.
Hojjat S.P., Won E., Hardisty M.R., Akens M.K., Wise-Milestone L.M., Whyne C.M. 2011.
Non-destructive evaluation of the effects of combined bisphosphonate and
photodynamic therapy on bone strain in metastatic vertebrae using image registration.
Annals of Biomedical Engineering 39, 2816-2822.
Hojjat S.P., Foltz W., Wise-Milestone L., Whyne C.M., 2012. Muti-modal μCT / μMR based
Semi- Automated Segmentation of Rat Vertebra Affected by Mixed Osteolytic /
Osteoblastic Metastases. Medical Physics 39, 2848-53.
Hojjat, S.P., Whyne, C.M., 2011. Automated quantitative microstructural analysis of
metastatically involved vertebrae: Effects of stereologic model and spatial resolution.
Medical Engineering & Physics 33, 188–194
Hong J., Cabe G.D., Tedrow J.R., Hipp J.A., Snyder B.D., 2004. Failure of trabecular bone
with simulated lytic defects can be predicted non-invasively by structural analysis.
Journal of Orthopedic Research 22, 479-86.
Horton, J. A., Bariteau, J. T., Loomis, R. M., Strauss, J. A., Damron, T. A., 2008. Ontogeny
of skeletal maturation in the juvenile rat. Anatomical Record 291, 283–292
Husseina, A.I., Barbonea, P.E., Morgana, E.F., 2012. Digital volume correlation for study of
the mechanics of whole bones. Procedia IUTAM 4, 116-125.
Ito M., Nishida A., Koga A., Ikeda S., Shiraishi A., Uetani M., Hayashi K., Nakamura T.,
2007. Contribution of trabecular and cortical components to the mechanical
properties of bone and their regulating parameters. Bone 31, 351-358.
Iwata, K., Mashiba, T., Hitora, T.,Yamagami, Y., Yamamoto, T., 2014. A large amount of
microdamages in the cortical bone around fracture site in a patient of atypical femoral
fracture after long-term bisphosphonate therapy. Bone 64183-186.
93
Izzo, R., Guarnieria, G., Guglielmib, G., Mutoa, M., 2013. Biomechanics of the spine. Part I:
Spinal stability. European Journal of Radiology 82, 118-126
Keaveny, T.M., Morgan, E.F., Niebur G.L., Yeh O.C., 2001. Biomechanics of trabecular
bone. Annual Review of Biomedical Engineering 3, 307–333.
Kim C.H., Zhang H., Mikhail G., Stechow D., Müller R., Kim H.S., Guo X.E., 2007. Effects
of Thresholding Techniques on µCT-Based Finite Element Models of Trabecular
Bone. Journal of Biomechanical Engineering 4, 481.
Kinney, J. H. et al., 2000. Three-dimensional morphometry of the L6 vertebra in the
ovariectomized rat model of osteoporosis: biomechanical implications. Journal of
Bone and Mineral Research 15, 1981-1991.
Kopperdahl, D.L., Keaveny, T.M., 1998. Yield strain behavior of trabecular bone. Journal of
Biomechanics 31, 601-608.
Kurth A.A., Muller, R., 2001. The effect of an osteolytic tumor on the three-dimensional
trabecular bone morphology in an animal model. Skeletal Radiology 30, 94–98
Ladd, A. J., Kinney, J. H., Haupt, D. L. & Goldstein, S. A., 1998. Finite-element modeling of
trabecular bone: comparison with mechanical testing and determination of tissue
modulus. Journal of Orthopedic Research 16, 622-628.
Landrigan, M.D., Li, J., Turnbull, T.L.,Burr, D.B., Niebur, G.L., Roeder, R.K., 2011.
Contrast-enhanced micro-computed tomography of fatigue microdamage
accumulation in human cortical bone. Bone 48, 443-450.
Lee T.C., Mohsin S., Taylor D., Parkesh R., Gunnlaugsson T., O’Brien F.J., Giehl M.,
Gowin W., 2003. Detecting microdamage in bone. Journal of Anatomy 203, 161–172.
Lee T. C., Arthur T. L., Gibson L. J., Hayes W. C., 2000. Sequential labelling of
microdamage in bone using chelating agents. Journal of Orthopedic Research 18,
322-325.
Lenoir, N., Bornert, M., Desrues, J., Bésuelle, P., Viggiani, G., 2007. Volumetric Digital
Image Correlation Applied to X-ray Microtomography Images from Triaxial
Compression Tests on Argillaceous Rock. Strain 43-3, 193-205.
Lo V.C., Akens M.A., Moore S., Yee A.J. Wilson B.J., Whyne C.M., 2012. Beyond radiation
therapy: photodynamic therapy maintains structural integrity of irradiated healthy and
metastatically involved vertebrae in a pre-clinical in vivo model. Breast Cancer
Research and Treatment 135, 391–401.
Logan, D. L., 2012. A first course in Finite Element Method. Cengage Learning.
94
Lotz J.C., Cheal E.J., Hayes W.C., 1991. Fracture prediction for the proximal femur using
finite element models: Part II--Nonlinear analysis. Journal of Biomechanical
Engineering 4, 361-5.
Lynch, J. A., Grigoryan, M., Fierlinger, A., Guermazi, A., Zaim, S., MacLean, D. B.,
Genant, H. K., 2004. Measurement of changes in trabecular bone at fracture sites
using X-Ray CT and automated image registration and processing. Journal of
Orthopaedic Research 22, 362–367.
Manolagas S.C., 2000. Birth and death of bone cells: basic regulatory mechanisms and
implications for the pathogenesis and treatment of osteoporosis. Endocrine Reviews
21, 115–37.
Morgan, E.F. , Yeh, O.C., Keaveny, T.M., 2005. Damage in trabecular bone at small strains.
European Journal of Morphology 42(2), 13-21
Muller, R., Gerber, S.C., Hayes W. C., 1998. Micro-compression: A novel technique for the
nondestructive assessment of local bone failure. Technology and Health Care 6, 433–
444
Nagaraja, S., Couse, T. L., Guldberg, R. E., 2005. Trabecular bone microdamage and
microstructural stresses under uniaxial compression. Journal of Biomechanics 38,
707-716.
Nagaraja, S., Lin, A. S., Guldberg, R. E., 2007. Age-related changes in trabecular bone
microdamage initiation. Bone 40, 973-980.
Nagaraja, S., Skrinjar, O., Guldberg, R.E., 2011. Spatial Correlations of Trabecular Bone
Microdamage with Local Stresses and strains Using Rigid Image Registration.
Journal of Biomechanical Engineering 133, 064502.
Naishadham D., Siegel. R., 2012. Cancer statistics, 2012. CA-A Cancer Journal for
Clinicians 62, 10-29.
Nazarian, A., von Stechow, D., Zurakowski, D., Müller, R., Snyder, B.D., 2008. Bone
volume fraction explains the variation in strength and stiffness of cancellous bone
affected by metastatic cancer and osteoporosis. Calcifies Tissue International 83(6),
368-79.
Niebur G.L., Feldstein M.J., Yuen J.C., Chen T.J., Keaveny TM., 2000. High-resolution
finite element models with tissue strength asymmetry accurately predict failure of
trabecular bone. Journal of Biomechanics 33, 1575-83.
Niebur, G. L., Yuen, J. C., Hsia, A. C., Keaveny, T. M., 1999. Convergence behavior of
high-resolution finite element models of trabecular bone. Journal of Biomechanical
Engineering 121, 629-635.
95
Green, J., Nagaraja, S., Diab, T., Vidakovic, B. Guldberg, R.E., 2011. Age-related changes in
human trabecular bone: Relationship between microstructural stress and strain and
damage morphology. Journal of Biomechanics 44, 2279–2285
O'Neal, J. M. et al., 2010. One year of alendronate treatment lowers microstructural stresses
associated with trabecular microdamage initiation. Bone 47, 241-247.
Ochoa, J. A., Heck, D. A., Brandt, K. D., Hillberry, B. M., 1991. The effect of intertrabecular
fluid on femoral head mechanics. Journal of Rheumatology 18, 580-584.
Olszta, M.J., Cheng, X., Jee, S.S., Kumar, R., Kim, Y.-Y., Kaufman, M.J., Douglas, E.P.,
Gower, L.B., 2007. Bone structure and formation: A new perspective (Review).
Materials Science and Engineering: Reports 3-5, 77-116.
Post, T.M., Cremers, S.C.L.M., Kerbusch, T., Danhof, M., 2010. Bone physiology, disease
and treatment: Towards disease system analysis in osteoporosis. Clinical
Pharmacokinetics 49-2, 89-118.
Prabhu, P.L.V, Saralaya, V.V., Pai, M.M., Ranade A.V., Singh, G., Madhyastha, S., 2007.
Vertebral body integrity: a review of various anatomical factors involved in the
lumbar region. Osteoporosis International 18, 891-903
Rades D., Schild S.E., Abrahm J.L. et al., 2010. Treatment of painful bone metastases.
Nature Reviews in Clinical Oncology 7, 220–229.
Reale C., Turkiewicz A., Reale C., 2001. Antalgic treatment of pain associated with bone
metastases. Critical Reviews in Oncology 37, 1-11.
Rhee, Y. et al., 2009. Assessment of Bone Quality using Finite Element Analysis Based
upon Micro-CT Images. Clinics in Orthopedic Surgery 1, 40-47.
Rizzoli, R., 2010. Microarchitecture in focus Osteoporosis International 21, S403–S406
Roberts, B.C., Perilli, E., Reynolds, K.J., 2014. Application of the digital volume correlation
technique for the measurement of displacement and strain fields in bone: A literature
review. Journal of Biomechanics 47, 923–934.
Roeder, B.A., Kokini, K., Robinson, J.P., Voytik-Harbin, S.L., 2004. Local, three-
dimensional strain measurements within largely deformed extracellular matrix
constructs (Article). 126-6, 699-708.
Seeley, R. R., Stephens, T. D., Tate P., 2006. Anatomy and Physiology. McGrawhill Higher
Education (2006).
Shigley, J., Mischke, C., 2001. Mechanical Engineering Design. 6th ed., New York: McGraw
Hill.
96
Skrinskas T., Clemons, M., Freedman, O., Weller, I., Whyne, C., 2009. Automated CT-based
analysis to detect changes in the prevalence of lytic bone metastases from breast
cancer. Clinical and Experimental Metastasis 26, 97-103.
Silva, M.J., Keaveny, T.M., Hayes, C.H., 1997. Load sharing between the shell and centrum
in lumbar vertebral body. Spine 22-2, 140-150
Simpson, E.K., Parkinson, I H., Manthey, B., Fazzalari, N.L. 2001. Intervertebral disc
disorganisation is related to trabecular bone architecture in the lumbar spine. Journal
of Bone Mineral Research 16, 681–687, 2001
Singh, A. S., Figg, W. D., 2005. In vivo models of prostate cancer metastasis to bone.
Journal of Urology 174, 820-826.
Smith, T.S., Bay, B.K., Rashid, M.M., 2002. Digital volume correlation including rotational
degrees of freedom during minimization. Experimental Mechanics 42-3, 272-278
Snyder B.D., Hauser-Kara D.A., Hipp J.A., Zurakowski D., Hecht A.C., Gebhardt M.C.,
2006. Predicting fracture through benign skeletal lesions with quantitative computed
tomography. Journal or Bone and Joint Surgery 88, 55-70.
Tsafnat, N., Weor, S., 2011. An experimentally validated micromechanical model of a rat
vertebra under compressive loading. Journal of Anatomy 2011, 40-46
Turnbull T.L., Gargac J.A., Niebur G.L., Roeder R.K., 2011. Detection of fatigue
microdamage in whole rat femora using contrast-enhanced micro-computed
tomography. Journal of Biomechanics 44, 2395-400.
Van Rietbergen, B., et al., 1995. A new method to determine trabecular bone elastic
properties and loading using micromechanical finite-element models. Journal of
Biomechanics, 28(1), 69-81.
Vashishth, D., et al., 2000. In vivo diffuse damage in human vertebral trabecular bone.Bone
26(2), 147-52.
Verhulp, E., Rietbergen, B.van, Huiskes, R., 2004. A three-dimensional digital image
correlation technique for strain measurements in microstructures. Journal of
Biomechanics 37, 1313-1320.
Verhulp, E., Van Rietbergen, B., Muller, R., Huiskes, R., 2008. Micro-finite element
simulation of trabecular-bone post-yield behaviour--effects of material model,
element size and type. Computer Methods in Biomechanics and Biomedical
Engineering 11, 389-395.
Villemure, I., Stokes, I.A.F., 2009. Growth plate mechanics and mechanobiology: A survey
of present understanding. Journal of Biomechanics 42, 1793–1803.
97
Von Moos, R., Sternberg, C., Body, J.J., Bokemeyer, C., 2013. Reducing the burden of bone
metastases: Current concepts and treatment options. Supportive Care in Cancer 21-6,
1773-1783.
Waarsing, J. H., Day, J. S., van der Linden, J. C., Ederveen, A. G., Spanjers, C., De Clerck,
N., Sasov, A., Verhaar, J. A., and Weinans, H., 2004. Detecting and tracking local
changes in the tibiae of individual rats: a novel method to analyse longitudinal in vivo
micro-CT data. Bone 34, 163–169.
Wang, X., Masse, D.B., Leng, H., Hess, K.P., Ross, R.D., Roeder, R.K., Niebur, G.L., 2007.
Detection of trabecular bone microdamage by micro-computed tomography. Journal
of Biomechanics 40, 3397-3403.
Wenzel, T.E., Schaffler, M.B., Fyhrie, D.P., 1996. In vivo trabecular microcracks in human
vertebral bone. Bone 19(2), 89-95.
Whyne, C. M., Hu, S. S., Lotz, J. C., 2003. Burst fracture in the metastatically involved
spine: development, validation, and parametric analysis of a three-dimensional
poroelastic finite-element model. Spine 28, 652-660.
Wolff J., 1986. The law of bone remodeling [translated from the 1892 original, Das Gesetz
der Transformation der Knochen, by P. Maquet and R. Furlong]
Wolfram, U., Wilke, H.J., Zysset, P.K., 2010. Valid micro finite element models of vertebral
trabecular bone can be obtained using tissue properties measured with
nanoindentation under wet conditions Journal of Biomechanics 43(9), 1731-7.
Wong, C.C., McGirt, M.J., 2013. Vertebral compression fractures: A review of current
management and multimodal Therapy. Journal of Multidisciplinary Healthcare 6,
205-214.
Yeh, O.C., Keaveny, T.M., 2001. Relative roles of microdamage and microfracture in the
mechanical behavior of trabecular bone. Journal of Orthopaedic Research 19(6),
1001-7.
Yoneda, T., Michigami, T., Yi, B., Williams, P.J., Niewolna, M., Hiraga, T., 1999. Use of
bisphosphonates for the treatment of bone metastasis in experimental animal models.
Cancer Treatment Reviews 25, 293-299.
Zannoni, C., Mantovani , R., Viceconti, M., 1998. Material properties assignment to finite
element models of bone structures: a new method. Medical Engineering & Physics 20
735–740
Zauel R., Yeni Y.N., Bay B.K., Dong X.N., Fyhrie D.P., 2006. Comparison of the linear
finite element prediction of deformation and strain of human cancellous bone to 3D
digital volume correlation measurements. Journal of Biomechanical Engineering 128,
1-6.
98
Zysset, P.K., Guo, X.E., Hoffler, C.E., Moore, K.E., Goldstein, S.A., 1999. Elastic modulus
and hardness of cortical and trabecular bone lamellae measured by nanoindentation in
the human femur. Journal of Biomechanics 32, 1005–12
99
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