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Development of Breast Models for Use
in Simulation of Breast Tomosynthesis
and CT Breast Imaging
Stephen J. Glick*
J. Michael O’Connor**, Clay Didier**, Mini Das*,
* University of Massachusetts Medical School
** University of Massachusetts, Lowell
Some BT and CTBI system design
and acquisition parameters
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CsI thickness
Pixel size
Electronic noise levels
kVp setting
X-ray tube filter
Reconstructed voxel size
Reconstruction filter, # of iterations
Magnification (focal spot blurring)
Background
• CT Breast Imaging (CTBI) and Breast
Tomosynthesis (BT) imaging systems are
currently being developed and studied by a
number of researchers and commercial vendors.
• Preliminary evidence suggests that these
tomographic breast imaging systems have
potential for improving visualization of breast
masses at approximately equivalent dose to
mammography.
Objective assessment of image quality
Barrett and Myers (Foundations of Image Science),
any meaningful approach to optimizing an imaging
system must include definitions of
1.
2.
3.
4.
the specific task to be performed,
the observer,
an object model representing the objects to be
imaged, and
a figure-of-merit used to evaluate task
performance
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3D Digital Breast Models
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3D power-law noise, (Burgess et al, 2001, Reiser et al 2008)
Clustered lumpy background (Rowland et al, 1992).
Bakic 3D breast phantom (Bakic et al, 2002, Zhang et al, 2008)
- Geometrical shapes
Bliznakova 3D breast phantom (Bliznakova et al, 2003, 2010)
- Geometrical shapes, power law noise etc.
Li et al, 3D breast phantom (Li et al, 2009)
- Based on clinical breast CT images
UMMS prototype bench-top CTBI
system
Varian 2520 FlatPanel Detector
Varian Rad94
X-ray tube
Bow-tie filter
Specimen
Proposed UMASS model – based on surgical mastectomy
specimens
Power-law
Rotary Stage
noiseClustered
Lumpy
Background
UMMS Benchtop CT Imaging System
•Mastectomy Specimen Imaging
Varian 2520 FlatPanel Detector
Pendant Breast Holder
Uncompressed
Breast Holder
Compressed
Breast Holder
Varian Rad94
X-ray tube
Bow-tie filter
Specimen
Rotary Stage
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Specimen #19 - Invasive Ductal Carcinoma
Goal of Study
Use CT images of mastectomy specimens
to develop an ensemble of 3D breast
phantoms that can be used as input to
computer simulation software to generate
realistic simulated CT slices
Simulation Methodology
Specimen Reconstruction
Phantom Generation
Processing Steps
3D Breast Model
Generate Model
Simulation Reconstruction
CTBI or BT Simulation
1.
2.
3.
4.
Projection Averaging
Cupping artifact compensation
Anisotropic diffusion filtering
Segmentation - classification of voxels
a. Binary method
b. Fuzzy mixture method
CT Reconstruction
Simulated Projections
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Projection Averaging
Acquire 10 projections per angle
Cupping Correction by Altunbas
Method*
RATP (Radial Adipose Tissue Profile)
Sample Radials to determine RATP
* Med. Phys., 2007. 34(7)
Weighted Fit to RATP
Clinical Dose = 2.3 mGy
High Dose = 23.0 mGy
Preprocessing Step
Anisotropic Diffusion Filtering*
I
t
   (p (  (G   I) )  I)
RATP fit treated as additive noise. Assuming circular symmetry, the correction is
applied to entire slice in order to mitigate intraslice variation
Example Histograms
Clinical Dose = 2.3 mGy
High Dose = 23.0 mGy
Specimen Reconstruction
After ADF Filtering
*Perona and Malik et al, 1990
High Dose with ADF Filter
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Method 1 : Classify each Voxel as
Adipose or Fibroglandular Tissue
(Binary Method)
Method 1 : Classify each Voxel as
Adipose or Fibroglandular Tissue
(Binary Method)
Histogram after ADF Filtering
Adipose
Tissue
Fibroglandular
Tissue
Specimen reconstruction
after ADF filtering
Method 2 : Voxels can be Weighted Mixture of
Adipose and Fibroglandular Tissue
(Fuzzy Mixture Method)
Breast object model
using Binary Method
Method 2 : Voxels can be Weighted Mixture of
Adipose and Fibroglandular Tissue
(Fuzzy Mixture Method)
Histogram after TV Filtering
Weighted
Mixture
Adipose
Tissue
Fibroglandular
Tissue
Specimen reconstruction
after TV filtering
Breast object model
using Fuzzy Mixture Method
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Validation
Using Breast Object Models to Simulate
Cone-beam Projections
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UMMS Cone Beam Simulation Software (CBSS) - (Vedula et. al.
SPIE 5030, 2003, Gong et al, Med Phys, 2006)
TASMIP (tungsten anode spectral model, Boone et al, Med. Phys.
24, 1997) or other spectral models.
Spectra scaled to provide specified mean glandular dose [DgN]
(Thacker et. al. Phys. Med. Biol. 49, 2004) to pendant breast
X-ray transport through breast modeled using Siddon’s ray-tracing,
and attenuation coefficients defined by Johns and Yaffe PMB 1987
Scatter component from Monte Carlo simulation
Signal and noise propagation through CsI indirect detector using
parameters from a serial cascade model
Reconstruct projections using either Feldkamp FBP or a penalized
maximum likelihood iterative reconstruction algorithm
Specimen
Reconstruction
FG
Adipose
Contrast
µ (cm -1) % Std Dev
0.0333 7.0%
0.0267
7.0%
24.8%
Simulated
Reconstruction -Binary
FG
Adipose
Simulation parameters approximate measured technique: 40kVp, 0.5mAs, 300
projections, no external filter, approximate Mean Glandular Dose of 2.3mGy
Contrast
26.2%
Validation - Quantitative
Simulated
Reconstruction - Fuzzy
µ (cm -1) % Std Dev
FG
0.0317 7.1%
Adipose
0.0251
6.4%
Contrast
Simulated
Reconstruction - Fuzzy
Reconstruction: Feldkamp FBP, no roll-off of ramp filter, voxel .254mm3
Validation - Quantitative
Specimen
Reconstruction
Simulated
Reconstruction -Binary
µ (cm -1) % Std Dev
0.0315 8.4%
0.0254
7.2%
24.2%
Specimen
Reconstruction
FG
Adipose
Contrast
µ (cm -1) % Std Dev
0.0324 8.1%
0.0253 10.9%
28.4%
Simulated
Reconstruction -Binary
FG
Adipose
Contrast
Simulated
Reconstruction - Fuzzy
µ (cm -1) % Std Dev
FG
0.0313 7.7%
Adipose
0.0250 10.5%
25.0%
Contrast
µ (cm -1) % Std Dev
0.0318 8.4%
0.0257
9.5%
23.5%
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Breast CT Simulation
Power Law Analysis P (f ) 
Validation of β for CT slices
a
f

Sagital
β
σ
2.83
0.35
Metheany 44
1.86
0.32
0.9
O’Connor
20
1.94
0.42
0.98
20
2.21
0.25
0.95
Research # of
Specs
Burgess
213
R2
Range
Freqend
Freqend
Avg.
1.0
1.0
0.9 - 2.6
0.22 - 0.5
0.45
0.88 - 2.59
0.22 - 0.5
0.35
1.6 - 2.72
0.5
0.5
0.99
βCT = βMammo - 1
Coronal
SPECIMEN
PHANTOM
SIMULATION
•Freqstart = .1
Generation of Compressed Breast Phantom
(for DBT simulations)
Reconstruction of compressed mastectomy specimen
Another approach for generating
compressed phantoms - use template
Compressed breast phantom (binary method)
• Template based on CIRS Stereotactic Needle Biopsy Training Phantom
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Simulated Mammograms from Compressed Breast Phantoms
Example Usage of Breast Phantoms
• 98 Simulated tomosynthesis reconstructions, half with calc cluster present
• Two reconstruction methods compared
• Four observers (physicist) selected location and confidence of presence
Average LROC Area Comparisons
1.2
PML
FBP
1 mGy aquisition
PML
FBP
1
0.8
0.6
0.4
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0.2
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0
1.5 mGy
p= 0.0007
1.0 mGy
p=0.014
0.7 mGy
p=0.028
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Summary
• Two methods for generating breast
objects based on high-dose CT imaging
of mastectomy specimens
• An ensemble of uncompressed and
compressed breast phantoms and
simulation software can be used to
explore effect of acquisition and design
parameters on image quality
3D Digital Breast Models
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3D power-law noise, (Burgess et al, 2001, Metheany et al, 2007)
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Clustered lumpy background (Rowland et al, 1992).
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Bakic breast phantom (Bakic et al, 2002, Zhang et al, 2008)
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Proposed UMASS model – based on CTBI specimens
Acknowledgements
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This work is supported by NIH/NIBIB – EB02133 (“Feasibility of CT
Mammography Using Flat-Panel Detectors”) and NIH/NCI – CA102758
("Iterative Reconstruction for Breast Tomosynthesis“). The contents are
solely the responsibility of the authors and do not necessarily represent the
official views of the National Institutes of Health.
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Patients (who consent during difficult period of their life).
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Clinical Team of Comprehensive Breast Clinic at Levine Cancer Center,
UMass Memorial Health Care (UMMHC). Dr. Robert Quinlan, Medical
Director.
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Pathology Department and Cytology Lab, UMass Medical School (UMMS).
Dr. Ashraf Khan.
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UMMS Tomographic Breast Imaging Lab (TBIL) Team
Simulated mammogram
Power-law
Bakic breast
noise Clustered
phantom
Lumpy
Background
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