shepp-logan phantom the visible human project

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Simulation of Breast Anatomy:

Anthropomorphic Software Phantoms

Predrag R. BakicUniversity of Pennsylvania,

Department of Radiology

Philadelphia, PA, USA

Imaging Symposium – 3D Breast Models AAPM/COMP 2011, Vancouver, Canada, August 1, 2011

Antoinette Flight Simulator(Paris, 1909)

Shepp-Logan Phantom

http://sites.google.com/

site/hispeedpackets/Home/shepplogan

www.mathworks.com/matlabcentral/

fileexchange/9416-3d-shepp-logan-phantom

The Visible Human Project®

http://www.nlm.nih.gov/

research/visible/visible_human.html

http://sites.google.com/site/hispeedpackets/Home/shepplogan



http://www.mathworks.com/matlabcentral/fileexchange/9416-3d-shepp-logan-phantom











http://www.nlm.nih.gov/research/visible/visible_human.html



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4D-NCAT Phantom

http://dmip.rad.jhmi.edu/

people/faculty/Paul/Segars_research.htm#NCAT

Octree-based NCAT

Imaging Simulation

Badal, Kyprianou, Badano, et al., SPIE 6510, 2007

Validation and Optimization of

Imaging Systems

Challenging due to system complexity;

Large number of parameters influence

performance.

Preferred approach: Imaging clinical trials.

Validation and Optimization of

Imaging Systems Limitations of clinical trials

Cost

Duration

Irradiation of volunteers

Preclinical alternative: Virtual Clinical Trialsbased upon models of anatomy and image acquisition.

http://dmip.rad.jhmi.edu/people/faculty/Paul/Segars_research.htm



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Simulate tissue structures which make up

anatomical noise in clinical images

Simulate consistently images of the same

anatomy while varying acquisition specs

Provide the ground truth info about simulated

tissues for quantitative validation

Cover anatomic variations by providing flexibility

to modify phantom composition

Rationale for Developing

Computer PhantomsMultimodality Breast Imaging

Has been developed since 1996; now used by

15+ research labs worldwide

Provides ground truth, which is not available

clinically

Based on rules for modeling tissue

structures, providing flexibility to cover

anatomical variations

Penn Anthropomorphic

Software Breast PhantomAnatomical Correlation

(A.P. Cooper, On the Anatomy of the Breast, 1840)

(S.R. Wellings,

J Natl Cancer Inst, 1975)

(Bakic, et al.

Med Phys, 2011)

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Adipose Compartments in

Clinical Breast Images

(L. Tabar et al., Breast Cancer,

Thieme, Stuttgart, 2005)

Regions of predominantly adipose and

predominantly fibro-glandular tissue

Adipose compartments simulated

by region growing

Breast CT of mastectomy specimen

(Glick, U. Massachusetts)

n

(Zhang et al. SPIE 2008)

Software Breast Phantom Composition

Simulated Ductal Network

Shown five (out of 12)

ductal lobes

(Bakic et al., Med Phys 2003)

Flexibility of the Phantom Design

Cross-sections and projections of

phantoms with different glandularity.Cross-sections of phantoms corresponding

to different breast size.

(Bakic et al., Med Phys, 2011)

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Phantom Section

w/ Simulated Breast

Compression

DBT Reconstructed ImageX-ray Projection

(Bakic et al., SPIE 2010)

Simulation of Phantom Image Acquisition

Simulated T1-weighted MRI slices

( with 6 and 3mm slice thickness) Simulated ultrasound tomography

images of the phantom

(Bakic et al., SPIE 2011;

Collaboration w/ Duric Lab @ Karmanos)

Simulation of Phantom Image Acquisition

Simulation of Breast Lesions

Spiculated Masses Microcalcifications

Phantom Applications

Mammography:

Estimate dose to simulated tissue (using MC)

Validate registration of temporal images

Simulate scatter contribution (using MC)

Test image compression methods

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Dose Estimation

Phantom Thicknes,

Glandularity

DG

(Voxel Phantom)

DG

(Simple phantom)

Difference

(%)

4cm, 100% 0.185 0.188 1.6%

4cm, 53% 0.205 0.228 10%

5cm, 69% 0.138 0.168 17%

“Simple” Phantom Voxel Phantom

DG = the mean glandular dose for 1 mGy incident air kerma

(Hunt, Dance, Bakic, et al, UKCR 2003)


Digital Breast Tomosynthesis:

Assess geometric accuracy

Optimize reconstruction algorithms

Task based: e.g., breast density estimation, calcs

Analyze power spectrum of anatomical noise

Design observer studies using detailed

detector model (via MC) and observer models

Reconstructed ImageDBT Projection

(Bakic et al., IWDM 2010;

Collaboration w/ Real-Time Tomography)

Geometric Accuracy of DBT Methods: Supersampling

We reconstructed a series of 10 images with sub-pixel shifts within

the plane of reconstruction and combined them to form a

supersampled image.

6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.81

1.5

2

2.5

3

3.5

4

x 104

x (mm) from Chest Wall

Su

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rsa

mp

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tru

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d Im

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e In

ten

sit

y

6.52 6.53 6.54 6.55 6.56 6.571.5

2

2.5

3x 10

4

x (mm) from Chest Wall

Su

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Methods: Supersampling

We performed 10× supersampling in the scanning (y) direction.

19.820

20.220.4

34567

1

2

3

4

5

x 104

x (mm) from Chest Wally(mm) along Chest Wall

(from Detector Center)

Su

pe

rsa

mp

led

Re

co

ns

tru

cte

d Im

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e In

ten

sit

y

x

y



Results: Marker Position Error

0.00

0.05

0.10

0.15

0.20

0.25

0 10 20 30 40 50

Reconstructed Plane Depth z(mm)

Err

or

(mm

)

(xC-xT)(yC-yT)(zC-zT)Ep

• EP were averaged over all markers at the same depth in the phantom. (Error

bars = one SD.)

• Shown separately are the errors along each coordinate.




Other Modalities:

Optimize non-ionizing ultrasound tomography

Task based

Optimize radiation therapy

Future

Validate breast CT

Compare CE-DBT vs. DCE-MRI

Validate Mammo PETCoronal Section of a

Phantom with VBD=20%UST Reconstructed Image

UST Validation



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Phantom VBD = the volume fraction occupied by dense (non-adipose) tissues.

0

2

4

6

8

10

12

14

16

18

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85

Phantom VBD(%)

Co

un

t

Phantom VBD roughly follows the distribution calculated from >2800 women (Yaffe, 2009).

(Yaffe et al, “The Myth of the 50-50 Breast”, Med Phys 2009)

UST Validation Simulated Phantom DM Images

Vertical Section of a

Phantom with VBD=40%

Phantom DM Image



Results: Cumulus PD vs. UST VBD

ρ = 0.78

y = 1.21x - 3.70

R2 = 0.61

0

10

20

30

40

50

0 10 20 30 40 50

UST VBD (%)

Cu

mu

lus P

D (

%)

r = 0.78

ρ = 0.59

ρ = 0.75

Boyd et al., JNCI 2010



Currently Used

Software Phantoms

• Based upon models of breast anatomy• Taylor et al. /UWA: IWDM 1998, 2000

• Bakic et al. / Penn: CBMS‟98; IWDM‟98-‟10; SPIE‟99, ‟07-11; RadProtDosimetry‟05; MedPhys‟02-03, ‟11

• Bliznakova et al. / Patras, GR: PMB‟03, ‟06, MBEC‟07, IFMBE‟08, MedPhys‟10

• Ma et al./ Dexela: PMB‟09

• Reiser & Nishikawa / UChicago: Med Phys‟10

• Shorey et al. / Duke: AcadRadiol‟11

• Based upon individual clinical images• Hoeschen, Zankl et al. / GSF: RadProtDosimetry‟05

• Glick et al. / UMass: IWDM‟08; SPIE‟08-‟09; MedPhys‟09, TMI‟10

• Hsu (nee Li) et al. / Duke: SPIE‟08-„09, MedPhys‟09

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Univ. of Patras, Greece

3D breast model

Duct system

Background texture

Cooper ligaments

AbnormalityLymphatic system

Breast shape

Courtesy of K. Bliznakova, Patras

Simulation of mammographic background

Random walks

Empty matrix ++

Dilation

+

++

Gaussian FilteringFit to the shape

Low Pass Filtering

Texture matrix


Examples of projection images


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examples of ROI for evaluation

real simulated

real

simulated

real simulated

real

simulated


Univ. Mass., Worcester, MA, USA

Simulated mammogram

Courtesy of S. Glick, UMass, USA

Duke Univ., Durham, NC, USA

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Image Generation

Simulated Mammogram

Christina M.L. HsuBiomedical Engineering

Duke University

Physical Version of the

Penn Software Phantom

Physical Version of the Software

Phantom*

(Carton, Bakic et al, Med Phys 2011, * Patent Pending)

Customized inserts

simulating iodinated lesions


Phantom*


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Phantom*

Customized inserts

simulating iodinated lesions



Phantom*

HE LEDual energy

CE-DM


New Penn Phantom Design**

Upgrade of our region growing concept

Collaboration w/ D. Pokrajac, DSU

Based upon octree recursive partitioning

Advantages

Low (close to minimal) complexity; fast

Scalable

Allows thickness control of skin and

Cooper‟s ligaments

(Pokrajac, Maidment, Bakic, AAPM 2011, ** Patent Pending)

0.1

1

10

100

1000

10000

100000

10 100 1000 10000

Voxel Size (μm)

Sim

ula

tio

n T

ime (

min

ute

s)

Region Growing

New Design

New Design vs. Region Growing**

333 compartments

Region Growing: y ~ x -4.17

New Design: y ~ x -2.04


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400um phantom, 333 compartments,

target thickness 0.6mm








“Even Newer” Phantom Design

Partial Volume Simulation

Simulate voxels which contain 2+ tissues

or materials

Improves phantom image quality w/o need

to reduce voxel size

Phantom Section Detail

(no Partial Volume)Phantom Section Detail

(with Partial Volume)

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“Even Newer” Phantom Design

Shape Analysis of Simulated Anatomy

Phantom Section Fitted Ellipsoids

Fit ellipsoids to tissue

compartments

Help validate control

over phantom shape

Analyze clinical data

to refine simulation

Shape Analysis by Ellipsoidal Fitting

Class I

(a) (b)

(c) (d)

Class II

Class III Class IV

Mean Dice Coefficient

0.7

0.75

0.8

0.85

1 2 3 4

Phantom Classes

Class I Class II Class III Class IV

Dice Coefficient Variance

0

0.005

0.01

0.015

0.02

0.025

0 1 2 3 4

Phantom Classes

Class I Class II Class III Class IV

Strengths of the Full Simulation

of Breast Anatomy

Availability of the ground truth Reliable – unlike with clinical data

Needed for quantitative validation

Flexibility to cover wide anatomical variations Not limited to clinically available cases

Scalability Allows control of simulated tissue structures at different scales

Convenient for performing virtual clinical trials Affordable pre-clinical validation of imaging systems

Areas of Potential Improvement

Allow real time generation of many hi-res phantoms. Ongoing research on parallelization.

Future aim: Fast & cheap fabrication of physical phantoms

Improve the realism. Models from clinical data have superior realism; their ground

truth depends on segmentation/classification.

Is that necessary? An optimal phantom should rank the analyzed methods comparable to clinical performance.

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Antoinette Flight Simulator (Paris, 1909)

Space Shuttle Mission Simulator (Cmdr. Mark Kelly, 2011)

… Beyond the Space Shuttle Program?

Orion Multi-Purpose Crew Vehicle Development

@ Space Operations Simulation Center (Colorado)

Penn Radiology Physics Lab Dr. Andrew Maidment, Director

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Acknowledgement

Kristina Bliznakova, Christina (Li) Hsu, and Stephen Glick provided slides on their breast phantom designs.

David Pokrajac created slides on the new phantom design.

Penn breast phantom research has been funded by• Lehigh Univ., NSF Grant (during PhD studies)• Univ. Mass, NIH R01 (subcontract)• Del State Univ., NIH INBRE • RTT, NIH R44 (subcontract)• DoD HBCU DSU/Penn Partnership Training Award • NSF Collaborative ISS • Univ. Chicago (subcontract)• NIH/NIBIB R01 (w/ Barco)

Perelman Center for Advanced Medicine,

UPenn, 2008

Sava & Tesa Bakic, 11 mos

Kosta Bakic, 3.5 years

Thank You!

“Clothespin”, Philadelphia

C. Oldenburg, 1976

See you at the IWDM 2012

in Philadelphia July 8-11!

shepp-logan phantom the visible human project

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