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. 2016 Jan;43(1):23.
doi: 10.1118/1.4937597.

Population of 224 realistic human subject-based computational breast phantoms

David W Erickson  1 , Jered R Wells  2 , Gregory M Sturgeon  3 , Ehsan Samei  4 , James T Dobbins 3rd  5 , W Paul Segars  6 , Joseph Y Lo  7
Affiliations

Population of 224 realistic human subject-based computational breast phantoms

David W Erickson et al. Med Phys. 2016 Jan.

Abstract

Purpose: To create a database of highly realistic and anatomically variable 3D virtual breast phantoms based on dedicated breast computed tomography (bCT) data.

Methods: A tissue classification and segmentation algorithm was used to create realistic and detailed 3D computational breast phantoms based on 230 + dedicated bCT datasets from normal human subjects. The breast volume was identified using a coarse three-class fuzzy C-means segmentation algorithm which accounted for and removed motion blur at the breast periphery. Noise in the bCT data was reduced through application of a postreconstruction 3D bilateral filter. A 3D adipose nonuniformity (bias field) correction was then applied followed by glandular segmentation using a 3D bias-corrected fuzzy C-means algorithm. Multiple tissue classes were defined including skin, adipose, and several fractional glandular densities. Following segmentation, a skin mask was produced which preserved the interdigitated skin, adipose, and glandular boundaries of the skin interior. Finally, surface modeling was used to produce digital phantoms with methods complementary to the XCAT suite of digital human phantoms.

Results: After rejecting some datasets due to artifacts, 224 virtual breast phantoms were created which emulate the complex breast parenchyma of actual human subjects. The volume breast density (with skin) ranged from 5.5% to 66.3% with a mean value of 25.3% ± 13.2%. Breast volumes ranged from 25.0 to 2099.6 ml with a mean value of 716.3 ± 386.5 ml. Three breast phantoms were selected for imaging with digital compression (using finite element modeling) and simple ray-tracing, and the results show promise in their potential to produce realistic simulated mammograms.

Conclusions: This work provides a new population of 224 breast phantoms based on in vivo bCT data for imaging research. Compared to previous studies based on only a few prototype cases, this dataset provides a rich source of new cases spanning a wide range of breast types, volumes, densities, and parenchymal patterns.

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Figures

FIG. 1.
FIG. 1.
Breast volume masking. (a) Original bCT coronal slice showing severe motion artifact. (b) BCFCM3D segmentation of (a) depicting three segmentation classes: background (black), breast, and background/breast average (gray). (c) Masked version of (a) with intermediary (blurred) boundary removed.
FIG. 2.
FIG. 2.
Normalized voxel intensity distribution and class assignments from sample bCT dataset. (a) Histogram of voxel intensities clearly shows adipose, glandular, and skin peaks. (b) Box-whisker plot of BCFCM3D segmentation of data into six classes ranging from adipose (0% glandular density—class 1) to glandular (100% glandular density—class 5). Note that skin (class 6) tends to be more dense than glandular tissue making its equivalent glandular density >100%.
FIG. 3.
FIG. 3.
Visual depicition of bCT segmentation processing. (a) Original coronal slice from breast volume. (b) Breast volume mask. (c) Image (a) after masking and denoising with bilateral filter. (d) Image (c) after uniformity correction. (e) BCFCM3D segmentation of (d). (f) Skin mask.
FIG. 4.
FIG. 4.
(Top) Denoised and nonuniformity-corrected coronal bCT images. (Middle) Phantoms produced with the methods of Li et al. with five integer density classes. (Bottom) Corresponding phantoms from the current work with six segmentation classes shown as integer grayscale levels.
FIG. 5.
FIG. 5.
Volume breast density distributions of the virtual breast phantom library.
FIG. 6.
FIG. 6.
V BDNSk versus breast volume for 224 bCT subjects.
FIG. 7.
FIG. 7.
Simulated projection images (with breast compression and ray-tracing). From left to right, VBDSk are 11.8%, 24.7%, and 34.6%, respectively.
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