Low-dose CT reconstruction based on multiscale dictionary

Ti Bai; Xuanqin Mou; Qiong Xu; Yanbo Zhang

doi:10.1117/12.2008140

6 March 2013 Low-dose CT reconstruction based on multiscale dictionary

Ti Bai, Xuanqin Mou, Qiong Xu, Yanbo Zhang

Proceedings Volume 8668, Medical Imaging 2013: Physics of Medical Imaging; 86683L (2013) https://doi.org/10.1117/12.2008140
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Statistical CT reconstruction using penalized weighted least-squares(PWLS) criteria can improve image-quality in low-dose CT reconstruction. A suitable design of regularization term can benefit it very much. Recently, sparse representation based on dictionary learning has been treated as the regularization term and results in a high quality reconstruction. In this paper, we incorporated a multiscale dictionary into statistical CT reconstruction, which can keep more details compared with the reconstruction based on singlescale dictionary. Further more, we exploited reweigted l₁ norm minimization for sparse coding, which performs better than I norm minimization in locating the sparse solution of underdetermined linear systems of equations. To mitigate the time consuming process that computing the gradiant of regularization term, we adopted the so-called double surrogates method to accelerate ordered-subsets image reconstruction. Experiments showed that combining multiscale dictionary and reweighted l₁ norm minimization can result in a reconstruction superior to that bases on singlescale dictionary and l₁ norm minimization.

Citation Download Citation

Ti Bai, Xuanqin Mou, Qiong Xu, and Yanbo Zhang "Low-dose CT reconstruction based on multiscale dictionary", Proc. SPIE 8668, Medical Imaging 2013: Physics of Medical Imaging, 86683L (6 March 2013); https://doi.org/10.1117/12.2008140

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