Sparse-view X-ray CT reconstruction with Gamma regularization

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• 作者：Junfeng Zhang^aAuthor Vitae ; Yining Hu^a ; ^bAuthor Vitae ; Jian Yang^cAuthor Vitae ; Yang Chen^a ; ^b ; ^{chenyang.list@seu.edu.cn}Author Vitae ; Jean-Louis Coatrieux^d ; ^e ; ^fAuthor Vitae ; Limin Luo^a ; ^bAuthor Vitae
• 关键词：Computer tomography ; Gamma regularization ; TV regularization ; l2-norm
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：22 March 2017
• 年：2017
• 卷：230
• 期：Complete
• 页码：251-269
• 全文大小：5063 K
• 卷排序：230

文摘

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.