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• W3131781207 abstract "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 the clinical setting, enhancing the quality of the recovered images is still a subject of research. The aim of this paper was to propose and compare, in a general optimization framework, three slightly different models and corresponding accurate iterative algorithms for Digital Breast Tomosynthesis image reconstruction, characterized by a convergent behavior. 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 framework in reconstructing breast volumes, with particular focus on the masses and microcalcifications, in few iterations and in enhancing the image quality in a prolonged execution." @default.
• W3131781207 created "2021-03-01" @default.
• W3131781207 creator A5008703659 @default.
• W3131781207 creator A5053041046 @default.
• W3131781207 date "2021-02-13" @default.
• W3131781207 modified "2023-10-12" @default.
• W3131781207 title "A Model-Based Optimization Framework for Iterative Digital Breast Tomosynthesis Image Reconstruction" @default.
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• W3131781207 doi "https://doi.org/10.3390/jimaging7020036" @default.
• W3131781207 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8321284" @default.
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• W3131781207 hasPublicationYear "2021" @default.
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