Improving depth resolution in digital breast tomosynthesis by iterative image reconstruction

Abstract

Digital breast tomosynthesis (DBT) is currently enjoying tremendous growth in its application to screening for breast cancer. This is because it addresses a major weakness of mammographic projection imaging; namely, a cancer can be hidden by overlapping fibroglandular tissue structures or the same normal structures can mimic a malignant mass. DBT addresses these issues by acquiring few projections over a limited angle scanning arc that provides some depth resolution. As DBT is a relatively new device, there is potential to improve its performance significantly with improved image reconstruction algorithms. Previously, we reported a variation of adaptive steepest descent - projection onto convex sets (ASD-POCS) for DBT, which employed a finite differencing filter to enhance edges for improving visibility of tissue structures and to allow for volume-of-interest reconstruction. In the present work we present a singular value decomposition (SVD) analysis to demonstrate the gain in depth resolution for DBT afforded by use of the finite differencing filter.