Image reconstruction for sparse-view CT and interior CT—introduction to compressed sensing and differentiated backprojection

Quantitative Imaging in Medicine and Surgery

Essam A. Rashed²  Hiroyuki Kudo¹  Taizo Suzuki¹ 

[1] Division of Information Engineering, Faculty of Engineering, Information and Systems, University of Tsukuba, Tennoudai 1-1-1, Tsukuba 305-8573, Japan;;Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia 41522, Egypt

关键词: Computed tomography (CT);    image reconstruction;    image processing;    compressed sensing (CS);    differentiated backprojection (DBP);   

DOI  :  10.3978/j.issn.2223-4292.2013.06.01

学科分类：外科医学

来源: AME Publications

【 摘 要 】

New designs of future computed tomography (CT) scanners called sparse-view CT and interior CT have been considered in the CT community. Since these CTs measure only incomplete projection data, a key to put these CT scanners to practical use is a development of advanced image reconstruction methods. After 2000, there was a large progress in this research area briefly summarized as follows. In the sparse-view CT, various image reconstruction methods using the compressed sensing (CS) framework have been developed towards reconstructing clinically feasible images from a reduced number of projection data. In the interior CT, several novel theoretical results on solution uniqueness and solution stability have been obtained thanks to the discovery of a new class of reconstruction methods called differentiated backprojection (DBP). In this paper, we mainly review this progress including mathematical principles of the CS image reconstruction and the DBP image reconstruction for readers unfamiliar with this area. We also show some experimental results from our past research to demonstrate that this progress is not only theoretically elegant but also works in practical imaging situations.

【 授权许可】

Unknown