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Optimization methods for very accurate Digital Breast Tomosynthesis image reconstruction

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Document pages: 17 pages

Abstract: Digital Breast Tomosynthesis is an X-ray imaging technique that allows avolumetric reconstruction of the breast, from a small number of low-dosetwo-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, veryaccurate iterative algorithms for Digital Breast Tomosynthesis imagereconstruction, characterized by a convergent behaviour. They are able todetect the cancer object of interest, i.e. masses and microcalcifications, inthe early iterations and to enhance the image quality in a prolonged execution.The suggested model-based implementations are specifically aligned to DigitalBreast Tomosynthesis clinical requirements and take advantage of a TotalVariation regularizer. We also tune a fully-automatic strategy to set a properregularization parameter. We assess our proposals on real data, acquired from abreast accreditation phantom and a clinical case. The results confirm theeffectiveness of the presented solutions in reconstructing breast volumes withparticular focus on the masses and microcalcifications.

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