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Sparse-view X-ray CT reconstruction with Gamma regularization

Abstract : By providing fast scanning with low radiation doses, sparse-view (or sparse-projection) reconstruction has attracted much research attention in X-ray computerized tomography (CT) imaging. Recent contributions have demonstrated that the total variation (TV) constraint can lead to improved solution by regularizing the underdetermined ill-posed problem of sparse-view reconstruction. However, when the projection views are reduced below certain numbers, the performance of TV regularization tends to deteriorate with severe artifacts. In this paper, we explore the applicability of Gamma regularization for the sparse-view CT reconstruction. Experiments on simulated data and clinical data demonstrate that the Gamma regularization can lead to good performance in sparse-view reconstruction.
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-01484700
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Submitted on : Monday, March 27, 2017 - 5:00:45 PM
Last modification on : Thursday, January 9, 2020 - 4:04:02 PM
Long-term archiving on: : Wednesday, June 28, 2017 - 3:33:29 PM

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Junfeng Zhang, Yining Hu, Jian Yang, Yang Chen, Jean-Louis Coatrieux, et al.. Sparse-view X-ray CT reconstruction with Gamma regularization. Neurocomputing, Elsevier, 2017, 230, pp.251--269. ⟨10.1016/j.neucom.2016.12.019⟩. ⟨hal-01484700⟩

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