Methodology for generating a 3D computerized breast phantom from empirical data

Christina M. Li, W. Paul Segars, Georgia D. Tourassi, John M. Boone, James T. Dobbins

Research output: Contribution to journalArticlepeer-review

101 Scopus citations

Abstract

The initial process for creating a flexible three-dimensional computer-generated breast phantom based on empirical data is described. Dedicated breast computed-tomography data were processed to suppress noise and scatter artifacts in the reconstructed image set. An automated algorithm was developed to classify the breast into its primary components. A preliminary phantom defined using subdivision surfaces was generated from the segmented data. To demonstrate potential applications of the phantom, simulated mammographic image data were acquired of the phantom using a simplistic compression model and an analytic projection algorithm directly on the surface model. The simulated image was generated using a model for a polyenergetic cone-beam projection of the compressed phantom. The methods used to create the breast phantom generate resulting images that have a high level of tissue structure detail available and appear similar to actual mammograms. Fractal dimension measurements of simulated images of the phantom are comparatively similar to measurements from images of real human subjects. A realistic and geometrically defined breast phantom that can accurately simulate imaging data may have many applications in breast imaging research.

Original languageEnglish
Pages (from-to)3122-3131
Number of pages10
JournalMedical Physics
Volume36
Issue number7
DOIs
StatePublished - 2009
Externally publishedYes

Funding

Grant support for this project was provided by the Department of Defense Breast Cancer Research Program (Grant No. W81XWH-06-1-073), National Institutes of Health (NIH) (Grant No. R01EB001838), NIH/NCI (Grant No. R01CA112437), and NIH/NCI (Grant No. R01CA94236). The authors would like to acknowledge the help and support provided by Dr. Jessie Q. Xia for the use of the denoising algorithm, Dr. Joseph Y. Lo and Seimens Healthcare for providing the sample mammogram of the real human subject, and Dr. Jay A. Baker for reviewing simulated images of the phantom and providing valuable feedback.

FundersFunder number
National Institutes of HealthR01EB001838
National Cancer InstituteR01CA94236, R01CA112437
National Institute of Biomedical Imaging and BioengineeringR01EB002138
California Breast Cancer Research ProgramW81XWH-06-1-073

    Keywords

    • Breast imaging
    • Mammography
    • Modeling
    • Phantom
    • Segmentation

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