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 fullyautomatic 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.

Loli Piccolomini E., Morotti E. (2021). A model-based optimization framework for iterative digital breast tomosynthesis image reconstruction. JOURNAL OF IMAGING, 7(2), 1-20 [10.3390/jimaging7020036].

A model-based optimization framework for iterative digital breast tomosynthesis image reconstruction

Loli Piccolomini E.;Morotti E.
2021

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 fullyautomatic 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.
2021
Loli Piccolomini E., Morotti E. (2021). A model-based optimization framework for iterative digital breast tomosynthesis image reconstruction. JOURNAL OF IMAGING, 7(2), 1-20 [10.3390/jimaging7020036].
Loli Piccolomini E.; Morotti E.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/846487
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