学术文献库

Digital Breast Tomosynthesis is an X-ray imaging technique that allows a volumetric reconstruction of the breast, from a small number of low-dose two-dimensional projections. Although it is already used in clinical setting, enhancing the quality of the recovered images is still a subject of research. Aim of this paper is to propose, in a general optimization framework, very accurate iterative algorithms for Digital Breast Tomosynthesis image reconstruction, characterized by a convergent behaviour. They are able to detect the cancer object of interest, i.e. masses and microcalcifications, in the early iterations and to enhance the image quality in a prolonged execution. The suggested model-based implementations are specifically aligned to Digital Breast Tomosynthesis clinical requirements and take advantage of a Total Variation regularizer. We also tune a fully-automatic strategy to set a proper regularization parameter. We assess our proposals on real data, acquired from a breast accreditation phantom and a clinical case. The results confirm the effectiveness of the presented solutions in reconstructing breast volumes with particular focus on the masses and microcalcifications.