Motivation and clinical relevance: Early detection is the key to surviving breast cancer. This project aims to complete the development of an experimental carbon nanotube-enabled x-ray imaging device for breast cancer screening. Best described as stationary digital breast tomosynthesis (sDBT), this unique approach to 3D breast imaging has been shown in pre-clinical testing to collect higher quality information than the commercially- available 3D mammography systems currently in use. As such, it has the potential to improve the early detection of cancer. However, as with all 3D imaging, the images presented to the reader are the product of extensive computer processing. For 3D breast imaging, the final and crucial step is the presentation of a synthetic mammogram. Purpose and hypotheses: The purpose of this project is to integrate synthetic mammography into the image processing capability of the sDBT system, thereby providing a complete clinical tool. We hypothesize that (1) the quality of the sDBT synthetic mammogram will be greater than the quality of synthetic mammograms from available 3D mammography systems and (2) readers will prefer the sDBT synthetic mammogram over standard mammograms when interpreting diagnostically-important image features. Methods: To test these hypotheses, the research will involve two specific aims. First, phantom-based experimentation will be used to develop image processing algorithms that optimize the quality of information generated by sDBT and displayed as a synthetic mammogram. Quantitative image quality metrics (detectability indices) will be used for optimization, with images from commercially-available 2D and 3D mammography devices providing references for comparison. Second, the clinical utility of the optimized synthetic mammogram will be tested in reader studies, when applied to a library of sDBT images that have been collected previously in human trials. These studies will quantify reader performance (diagnostic accuracy) and preference, when interpreting clinically-important image features, such as masses and microcalcifications, in a head-to-head comparison of sDBT synthetic mammograms to standard mammograms. Project value: Since trials assessing the value of 3D mammography should include a synthetic mammogram, this project will have a direct clinical impact. It will provide the foundation for continued human testing of this promising high-resolution imaging system, which has the potential to improve breast cancer detection. Training Plan: It is anticipated that this project will require two years, forming the core of the dissertation work to complete a PhD in Biomedical Engineering. It will be carried out in a basic research lab with scientists and computer programmers and will also involve working with patient data, under the supervision of physician-scientists and radiologists. Since this project combines basic experimentation with a direct clinical application, it should provide an ideal transition back to medical school as well as excellent training for this MD-PhD graduate student, who is preparing for a career in academic medicine with a research focus on advanced imaging technologies and 3D image processing.

Public Health Relevance

Early detection is the key to surviving breast cancer, and as such, research continues to improve screening mammography. The goal of this project is to complete the development of a promising new technology: stationary 3D mammography, by optimizing the computer processing steps that generate the images used by readers to identify concerning findings. More specifically, it will integrate synthetic mammography into a high-resolution, carbon nanotube-enabled, stationary digital breast tomosynthesis device, producing a powerful tool for breast cancer screening.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Individual Predoctoral NRSA for M.D./Ph.D. Fellowships (ADAMHA) (F30)
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Special Emphasis Panel (ZRG1)
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Damico, Mark W
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University of North Carolina Chapel Hill
Biomedical Engineering
Schools of Medicine
Chapel Hill
United States
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