An efficient compressive sensing MR image reconstruction scheme

Jing Qin, Weihong Guo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

Compressive sensing (CS) has great potential to reduce imaging time. It samples very few linear projections, and exploits sparsity or compressibility to reconstruct images from the measurements. Medical and most natural images usually contain various fine features, details and textures. Widely used total variation (TV) and wavelet sparsity are not so effective in reconstructing these images. We propose to incorporate total generalized variation (TGV) and shearlet transform to efficiently produce high quality images from compressive sensing MRI data, i.e., incomplete spectral Fourier data. The proposed model is solved by using split Bregman and primal-dual methods. Numerous numerical results on various data corresponding to different sampling rates and noise levels show the advantage of our method in preserving various geometrical features, textures and spatially variant smoothness. The proposed method consistently outperforms related competitive methods and shows greater advantage as sampling rate goes lower.

Original languageEnglish
Title of host publicationISBI 2013 - 2013 IEEE 10th International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro
Pages306-309
Number of pages4
DOIs
StatePublished - 2013
Event2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013 - San Francisco, CA, United States
Duration: Apr 7 2013Apr 11 2013

Publication series

NameProceedings - International Symposium on Biomedical Imaging
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference2013 IEEE 10th International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2013
Country/TerritoryUnited States
CitySan Francisco, CA
Period4/7/134/11/13

Keywords

  • MRI
  • compressive sensing
  • primal dual
  • split Bregman
  • total generalized variation

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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