Optimization-based reconstruction for correcting non-linear partial volume artifacts in CT

Xin Liu; Buxin Chen; Zheng Zhang; Dan Xia; Emil Y. Sidky; Xiaochuan Pan

doi:10.1117/12.2512917

1 March 2019 Optimization-based reconstruction for correcting non-linear partial volume artifacts in CT

Xin Liu, Buxin Chen, Zheng Zhang, Dan Xia, Emil Y. Sidky, Xiaochuan Pan

Proceedings Volume 10948, Medical Imaging 2019: Physics of Medical Imaging; 109482Q (2019) https://doi.org/10.1117/12.2512917
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

In this work, we investigate the non-linear partial volume (NLPV) effect caused by sub-detector sampling in CT. A non-linear log-sum of exponential data model is employed to describe the NLPV effect. Leveraging our previous work on multispectral CT reconstruction dealing with a similar non-linear data model, we propose an optimization-based reconstruction method for correcting the NLPV artifacts by numerically inverting the non-linear model through solving a non-convex optimization program. A non-convex Chambolle-Pock (ncCP) algorithm is developed and tailored to the non-linear data model. Simulation studies are carried out with both discrete and continuous FORBILD head phantom with one high-contrast ear section on the right side, based on a circular 2D fan-beam geometry. The results suggest that, under the data condition in this work, the proposed method can effectively reduce or eliminate the NLPV artifacts caused by the sub-detector ray integration.

Citation Download Citation

Xin Liu, Buxin Chen, Zheng Zhang, Dan Xia, Emil Y. Sidky, and Xiaochuan Pan "Optimization-based reconstruction for correcting non-linear partial volume artifacts in CT", Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109482Q (1 March 2019); https://doi.org/10.1117/12.2512917

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