  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.
