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.
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