time series mri core analysis, modeling - toward dynamic surrogates of disease
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Time Series MRI Core Analysis, Modeling - toward Dynamic Surrogates of Disease. Dominik S. Meier, Ph.D. Center for Neurological Imaging BWH Radiology & Neurology. TSA Paradigm: Capture Processes. - PowerPoint PPT PresentationTRANSCRIPT
Neuroimage Analysis Centerhttp://www.spl.harvard.edu/nac
An NCRR National Resource Center
Time Series MRI Core Analysis, Modeling -toward Dynamic Surrogates of Disease
Dominik S. Meier, Ph.D.
Center for Neurological ImagingBWH Radiology & Neurology
Neuroimage Analysis Centerhttp://www.spl.harvard.edu/nac-2-
An NCRR National Resource Center
TSA Paradigm: Capture Processes
Reversible focal tissue
changes
Irreversible focal tissue
changes
Reversible diffuse tissue changes
Irreversible diffuse tissue
changes
Irreversible tissue loss
Total global irreversible tissue
loss
ATROPHY
Reversible mass effect and therapy-related shrinkage
Reversible focal tissue
changes
Irreversible focal tissue
changes
Reversible diffuse tissue changes
Irreversible diffuse tissue
changes
Irreversible tissue loss
Total global irreversible tissue
loss
ATROPHY
Reversible mass effect and therapy-related shrinkage
• Research and technology development for longitudinal studies of neurodegenerative disease involving MRI morphometry as outcome measure.
• Core work will explore the ability of serial in vivo MRI to illuminate the timing and sequence of the individual pathological processes underlying neurodegenerative disease.
• Segmentation of Change vs. Change of Segmentation
•Current/Common paradigm: Segmentation -> Trend Analysis
•TSA paradigm: Trend Analysis -> Segmentation
Neuroimage Analysis Centerhttp://www.spl.harvard.edu/nac-3-
An NCRR National Resource Center
Aims
•Aim 1: Time Series Fusion•Develop integrated methods for serial image data fusion•concatenates multiple 3-D MRI datasets into a single coherent 4-D space. •spatial and intensity normalization •voxel-based "chronobiopsy"
•Aim 2: Time Series Change Detection•Develop a new hierarchical framework for change detection and delineation. •3-level hierarchy of (1) detection, (2) delineation, and (3) segmentation.•specificity in detection and precision in segmentation•Detection requires high levels of expert knowledge•enhanced precision for delineation requires automation
•Aim 3: Time Series Modeling•Develop a framework for change characterization and visualization•parametric models of MRI intensity change•on each voxel time-series profile within the areas of change•investigate the serial MRI data from the viewpoint of a specific biological or clinical hypothesis•"temporal differentiation before spatial integration"
•Aim 4: Time Series Validation•Investigate ways to obtain error estimates and sensitivity to change. •scan-rescan data, automated calculation of residual from the fused 4D set•confidence intervals on the model parameters•areas of reference with no pathologic change •sensitivity analyses:sensitivity to change in both the spatial and the intensity domain
Neuroimage Analysis Centerhttp://www.spl.harvard.edu/nac-4-
An NCRR National Resource Center
Prelim. Results
Application: In preparation
Neuroimage Analysis Centerhttp://www.spl.harvard.edu/nac
An NCRR National Resource Center
Spectrum of Serial Morphometry
Differentiation -> Classification:“new/enlarging” (red), “stable” (green)“resolving” (blue)
V(t1) V(t2) V(t3)
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Serial Volumetry Differential Morphometry Segmentation of Change Time Series Modeling
- Spatially nonspecific - Sensitive to Registration Error
-Greater Expert Input-+segmentation of change+ Controlled Sensitivity
-Model Required-+ Controlled Sensitivity+Segmentation implied
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C (Ix ) 1. Classifier/Segmentation 2. Differentiation 2. TS Modeling
2. Integration
4. Integration 4. Integration 3.Classifier +Integration
3. Differentiation3.Classifier / Segmentation
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Ix a Ix' 1. Registration
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Ix a I 'x' 1. Normalization 1. Normalization
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Ix a I 'x'
0 R2=0.670R2=0.943R2=0.943
weeks
T2 intensity
5 10
15 20 30 40 50
2. Classifier/Segmentation
3. Differentiation
Neuroimage Analysis Centerhttp://www.spl.harvard.edu/nac-6-
An NCRR National Resource Center
Technological Biological / Clinical
•can dynamic metrics derived from serial MRI provide
surrogates with stronger pathological specificity
(inflammatory, degenerative, reparatory processes ) ?
•Different pathol. processes have different time
signatures, even if their morphological footprints
remain the same..
•E.g. Inflammation creates mass effect and occurs
rapidly.
Inflammation•Blood Brain Barrier breakdown•Edema•Cellular Infiltration
Degeneration•Demyelination•Axonal Damage
Repair•Macrophage activity•Astrocytosis•Remyelination•Axonal Repair?
~ weeks
~ months - years
~ months
The cross-sectionalconcept revisited
Avoid data reductioncompare first – reduce later
The longitudinalconcept revisited
Avoid data reductiondifferentiate first – integrate later
•Segmentation of Change vs. Change of Segmentation
Neuroimage Analysis Centerhttp://www.spl.harvard.edu/nac-7-
An NCRR National Resource Center
Data Fusion Pipeline
Effective spatial resolution loss in serial imaging
for tissue-specific normalization
Registration
Segmentation
Bias Field Correction
Partial Volume Filter
Intensity Normalization
Baseline Normalization
t1baseline
t2follow-up
t3follow-up
t4follow-up
coil sensitivity bias
variable head positioning
variable gain, scanner drift, upgrades etc.
Differential: detection of change
Neuroimage Analysis Centerhttp://www.spl.harvard.edu/nac-8-
An NCRR National Resource Center
Two-Process Time Series Model
0 10 20 30 40 50 weeks
Y1: Inflammation / Degeneration
Y2: Resorbtion / Repair
Y1 + Y2
MRI intensity
Example: New MS lesion formationWe model MRI intensity change as the superposition of two opposing processes, one causing T2 prolongation, another T2 shortening.
Neuroimage Analysis Centerhttp://www.spl.harvard.edu/nac-9-
An NCRR National Resource Center
Time Series Modeling Example: MS Lesion Formation
0
R2=0.670
R2=0.943
R2=0.943
weeks
T2 intensity
5 10 15 20 30 40 50
F1 = Level of hyperintensityF2 = Level or recoveryF3 = Duration
weeks
complete recovery
partial recovery
no recovery
F1
F1
F1
F2
F2
F2=0
F3
F3
F3
MRI intensity
Neuroimage Analysis Centerhttp://www.spl.harvard.edu/nac-10-
An NCRR National Resource Center
Example: Feature Maps of Change
F1: Hyperintensity , F2: residual damage , F3: duration [weeks]
Neuroimage Analysis Centerhttp://www.spl.harvard.edu/nac-11-
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• sensitivity to change• precision of trend assessment
• estimated error in measuringnew lesion change
Differentiation before Segmentation
Neuroimage Analysis Centerhttp://www.spl.harvard.edu/nac-12-
An NCRR National Resource Center
Error Accumulation / Sensitivity Analysis / Pipeline Design
1 dimension of variation: add and show all results
1009080706050403020100
N=191 N=59 N=43 N=39
p=0.25 p=0.003* p=0.33
DataPreprocessing VolumetryModeling
How one parameter at last step of pipeline affects results is easily tested.
The effect of a parameter early in the pipeline is much more difficult to assess.
Neuroimage Analysis Centerhttp://www.spl.harvard.edu/nac-13-
An NCRR National Resource Center
Conclusions:
•Repair does occur in MS, varying in extent by location & subject
•MRI intensity dynamics provide reliable metrics of activity
•Short-term T2 lesion recovery shows links to progression in both
atrophy and disability
•SPMSS shows trends to different lesion patterns than RRMS
•Dissociation between new lesion size and residual damage“big lesion small damage”, NO equivalence in total lesion burden
•Spatial patterns that match histopathological observations