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

Xinxin Li; Jiang Wang; Shan Tan

doi:10.1117/12.2082424

18 March 2015 Hessian Schatten-norm regularization for CBCT image reconstruction using fast iterative shrinkage-thresholding algorithm

Xinxin Li, Jiang Wang, Shan Tan

Author Affiliations +

Proceedings Volume 9412, Medical Imaging 2015: Physics of Medical Imaging; 94123V (2015) https://doi.org/10.1117/12.2082424
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

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.

Citation Download Citation

Xinxin Li, Jiang Wang, and Shan Tan "Hessian Schatten-norm regularization for CBCT image reconstruction using fast iterative shrinkage-thresholding algorithm", Proc. SPIE 9412, Medical Imaging 2015: Physics of Medical Imaging, 94123V (18 March 2015); https://doi.org/10.1117/12.2082424

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