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Article

A Model-Based Optimization Framework for Iterative Digital Breast Tomosynthesis Image Reconstruction

Department of Computer Science and Engineering, University of Bologna, Via Mura Anteo Zamboni 7, 40126 Bologna, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Tanja Tarvainen
J. Imaging 2021, 7(2), 36; https://doi.org/10.3390/jimaging7020036
Received: 2 January 2021 / Revised: 4 February 2021 / Accepted: 8 February 2021 / Published: 13 February 2021
(This article belongs to the Special Issue Inverse Problems and Imaging)
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. View Full-Text
Keywords: Digital Breast Tomosynthesis; few-views tomography; model-based method; Iterative Reconstruction algorithm; Total Variation regularization Digital Breast Tomosynthesis; few-views tomography; model-based method; Iterative Reconstruction algorithm; Total Variation regularization
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MDPI and ACS Style

Loli Piccolomini, E.; Morotti, E. A Model-Based Optimization Framework for Iterative Digital Breast Tomosynthesis Image Reconstruction. J. Imaging 2021, 7, 36. https://doi.org/10.3390/jimaging7020036

AMA Style

Loli Piccolomini E, Morotti E. A Model-Based Optimization Framework for Iterative Digital Breast Tomosynthesis Image Reconstruction. Journal of Imaging. 2021; 7(2):36. https://doi.org/10.3390/jimaging7020036

Chicago/Turabian Style

Loli Piccolomini, Elena, and Elena Morotti. 2021. "A Model-Based Optimization Framework for Iterative Digital Breast Tomosynthesis Image Reconstruction" Journal of Imaging 7, no. 2: 36. https://doi.org/10.3390/jimaging7020036

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