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Proceedings Paper

Hessian Schatten-norm regularization for CBCT image reconstruction using fast iterative shrinkage-thresholding algorithm
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Paper Abstract

Statistical iterative reconstruction in Cone-beam computed tomography (CBCT) uses prior knowledge to form different kinds of regularization terms. The total variation (TV) regularization has shown state-of-the-art performance in suppressing noises and preserving edges. However, it produces the well-known staircase effect. In this paper, a method that involves second-order differential operators was employed to avoid the staircase effect. The ability to avoid staircase effect lies in that higher-order derivatives can avoid over-sharpening the regions of smooth intensity transitions. Meanwhile, a fast iterative shrinkage-thresholding algorithm was used for the corresponding optimization problem. The proposed Hessian Schatten norm-based regularization keeps lots of favorable properties of TV, such as translation and scale invariant, with getting rid of the staircase effect that appears in TV-based reconstructions. The experiments demonstrated the outstanding ability of the proposed algorithm over TV method especially in suppressing the staircase effect.

Paper Details

Date Published: 18 March 2015
PDF: 9 pages
Proc. SPIE 9412, Medical Imaging 2015: Physics of Medical Imaging, 94123V (18 March 2015); doi: 10.1117/12.2082424
Show Author Affiliations
Xinxin Li, Huazhong Univ. of Science and Technology (China)
Jiang Wang, Univ. of Texas Southwestern Medical Ctr. (United States)
Shan Tan, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 9412:
Medical Imaging 2015: Physics of Medical Imaging
Christoph Hoeschen; Despina Kontos, Editor(s)

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