Low-dose CT reconstruction based on multiscale dictionary

Ti Bai, Xuanqin Mou, Qiong Xu, Yanbo Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

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 1 norm minimization for sparse coding, which performs better than 1 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 1 norm minimization can result in a reconstruction superior to that bases on singlescale dictionary and 1 norm minimization.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2013
Subtitle of host publicationPhysics of Medical Imaging
DOIs
StatePublished - 2013
Externally publishedYes
EventMedical Imaging 2013: Physics of Medical Imaging - Lake Buena Vista, FL, United States
Duration: Feb 11 2013Feb 14 2013

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume8668
ISSN (Print)1605-7422

Other

OtherMedical Imaging 2013: Physics of Medical Imaging
Country/TerritoryUnited States
CityLake Buena Vista, FL
Period2/11/132/14/13

Keywords

  • Double surrogates
  • Multiscale dictionary
  • Reweighted 1 norm minimization
  • Singlescale dictionary
  • Sparsity

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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