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.
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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 (March 6, 2013); doi:10.1117/12.2008140; http://dx.doi.org/10.1117/12.2008140