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Proceedings Article

Low-dose CT reconstruction based on multiscale dictionary

[+] Author Affiliations
Ti Bai, Xuanqin Mou, Qiong Xu, Yanbo Zhang

Xi'an Jiaotong Univ. (China)

Proc. SPIE 8668, Medical Imaging 2013: Physics of Medical Imaging, 86683L (March 6, 2013); doi:10.1117/12.2008140
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From Conference Volume 8668

  • Medical Imaging 2013: Physics of Medical Imaging
  • Robert M. Nishikawa; Bruce R. Whiting
  • Lake Buena Vista (Orlando Area), Florida, USA | February 09, 2013

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. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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 (March 6, 2013); doi:10.1117/12.2008140; http://dx.doi.org/10.1117/12.2008140


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