Investigation of reconstruction quality in digital breast tomosynthesis (DBT) based on compressed-sensing algorithm and synthesized 2D breast image

Digital breast tomosynthesis (DBT) is most commonly used in three-dimensional (3D) mammography because it provides a 3D view, so suspected tumors and massed in the breast can be detected with a higher degree of accuracy. Conventional DBT reconstruction methods are based on the filtered-backprojection (FBP) with an additional deblurring filter. However, this approach usually requires dense projection data with low noise levels for acceptable reconstruction quality. In this work, instead, we investigated a state-of-the-art image reconstruction based on the compressed-sensing (CS) theory for potential application to accurate, low-dose DBT. We implemented a CS-based algorithm as well as a FBP-based algorithm for DBT reconstruction and performed a systematic experiment to verify the usefulness of the algorithm by comparing its reconstruction quality to the FBP-based one. We successfully obtained DBT images of substantially high accuracy by using the CS-based algorithm and synthesized a 2D breast image from the CS-reconstructed DBT images, which showed heightened details retained from DBT images, indicating superior performance compared to traditional 2D breast image alone.