Improving depth-resolution, in-plane contrast, and reducing nonuniformity artifacts for wide-angle digital breast tomosynthesis

Purpose: This work aims to develop an image reconstruction algorithm for wide-angle digital breast tomosynthesis (DBT) that has improved depth resolution and in-plane contrast while reducing non-uniformity artifacts. Approach: The image reconstruction algorithm is an extension of our prior work on sparsity-regularized iterative image reconstruction. The algorithm is performed in two stages as explained in a prior work. The first stage consists of a low-resolution reconstruction that exploits sparsity for quantitative accuracy. In this work, this first stage is augmented with a formulation that includes the estimation of a "background" image, which absorbs low-frequency artifacts that cause image non-uniformity. Results: The new algorithm is demonstrated on three patient cases, each with different features of clinical interest, and for which the data are acquired on a wide-angle DBT system. Conclusion: The results on the shown cases indicate that the algorithm design goals have been met, but additional empirical results and task-based assessment are needed to strengthen this conclusion.