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1 March 2019 Prototyping optimization problems for digital breast tomosynthesis image reconstruction with a primal-dual algorithm
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Abstract
Digital Breast Tomosynthesis (DBT) is an emerging semi-tomographic modality that is gaining widespread popularity for mammographic screening. As the modality has only recently, in the last 10 years, been employed in the clinic, there is much variation amongst the vendors in DBT scan configuration and, accordingly, image reconstruction algorithm. In recent research there has been interest in developing iterative image reconstruction (IIR) based on gradient-sparsity regularization and for inclusion of physical modeling of detector response and noise properties. Due to the various motivations in designing IIR algorithms, there can be a great variety of optimization problems of interest. In this work, we employ a general optimization problem form where the objective function is a convex data discrepancy term and all other aspects of the imaging model are formulated as convex constraints. This general form of optimization can be efficiently solved using the primal-dual algorithm developed by Chambolle and Pock. We use the general optimization formulation together with this solver to prototype alternate imaging models for DBT; a least-squares data discrepancy with a modified total variation (TV) constraint is shown to be of particular interest in preliminary results.
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Emil Y. Sidky, Ingrid S. Reiser, Sean D. Rose, and Xiaochuan Pan "Prototyping optimization problems for digital breast tomosynthesis image reconstruction with a primal-dual algorithm", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094852 (1 March 2019); https://doi.org/10.1117/12.2513337
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