Breast tumor detection by mechanical contrast via decompression in tomosynthesis
Lead Presenter: Fernando Quivera
Additional Presenters: Dana Brooks (NU) Daniel Kopans (MGH) Waleed Meleis (NU) Richard Moore (MGH) Sinan Muftu (NU) Kristen Tgavalekos (NU) Kai-tak Wan (NU)
Faculty Advisor/Principal Investigator: Waleed Meleis
Method of Presentation: Oral Presentation
Breast cancer is the most common non-preventable cancer among women, with over 200,000 new cases in the United States each year. Conventional two-dimensional mammographic screening is estimated to detect 70-80% of breast cancers, but many cancers are still not detected early enough to permit a cure, and more than 40,000 women die from the disease each year. While conventional 2D mammography is currently the most effective screening tool for the early detection of breast cancer, efforts are needed to expand the benefits afforded by mammography. One such new technology is digital breast tomosynthesis (DBT), which provides the first three-dimensional, high resolution x-ray images of the breast. DBT holds the promise of making it easier for a radiologist to detect tumors by resolving superimposed structures along the x-ray axis. However, an attractive, untapped source of contrast in an imaging setting is indicated by the fact that breast cancers are often palpable to the patient or her physician while still not visible with mammography or DBT. This supports investigating combining mechanical imaging with mammography. We are using digital breast tomosynthesis to acquire 3D images of the breast at different levels of mechanical compression. We build a detailed finite-element model of the subject’s breast at one compression and use that model to simulate the movement of breast structures as they deform under matching changes in compression level. Discrepancies between finite-element predictions and acquired images are analyzed to locate regions with anomalous mechanical behavior corresponding to lesions and tumors.