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Proceedings Paper

TV-Stokes strategy for sparse-view CT image reconstruction
Author(s): Yan Liu; Lin Chen; Hao Zhang; Ke Wang; Jianhua Ma; Zhengrong Liang
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Paper Abstract

This paper introduces a new strategy to reconstruct computed tomography (CT) images from sparse-view projection data based on total variation stokes (TVS) strategy. Previous works have shown that CT images can be reconstructed from sparse-view data by solving a constrained TV problem. Considering the incompressible property of the voxels along the tangent direction of isophote lines, a tangent vector is consolidated in this newly-proposed algorithm for normal vector estimation. Then, a minimization problem based on this estimated normal vector is addressed and resolved in computation. The to-be-estimated image is obtained by executing this two-step framework iteratively with projection data fidelity constraints. By introducing this normal vector estimation, the edge information of the image is well preserved and the artifacts are efficiently inhibited. In addition, the new proposed algorithm can mitigate the staircase effects which are usually observed from the results of the conventional constrained TV method. In this study, the TVS method was evaluated by patients’ brain raw data which was acquired from Siemens SOMATOM Sensation 16-slice CT scanner. The results suggest that the proposed TVS strategy can accurately reconstruct the brain images and produce comparable results relative to the TV-projection onto convex sets (TV-POCS) method and its general case: adaptiveweighted TV-POCS (AwTV-POCS) method from 232,116 projection views. In addition, an improvement was observed when using only 77 views for TVS method compared to the AwTV/TV-POCS methods. In the quantitative evaluation, the TVS method showed adequate noise-resolution property and highest universal quality index value.

Paper Details

Date Published: 6 March 2013
PDF: 9 pages
Proc. SPIE 8668, Medical Imaging 2013: Physics of Medical Imaging, 86683D (6 March 2013); doi: 10.1117/12.2006957
Show Author Affiliations
Yan Liu, Stony Brook Univ., SUNY (United States)
Lin Chen, Stony Brook Univ., SUNY (United States)
Hao Zhang, Stony Brook Univ., SUNY (United States)
Ke Wang, Stony Brook Univ., SUNY (United States)
Jianhua Ma, Southern Medical Univ. (China)
Zhengrong Liang, Stony Brook Univ., SUNY (United States)


Published in SPIE Proceedings Vol. 8668:
Medical Imaging 2013: Physics of Medical Imaging
Robert M. Nishikawa; Bruce R. Whiting; Christoph Hoeschen, Editor(s)

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