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Sparsesense: Application of compressed sensing in parallel MRI

  • University of Wisconsin-Milwaukee

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

68 Scopus citations

Abstract

Compressed sensing (CS) has recently drawn great attentions in the MRI research community. The most desirable property of CS in MRI application is that it allows sampling of k-space well below Nyquist sampling rate, while still being able to reconstruct the image if certain conditions are satisfied. Recent work has successfully applied CS to reduce scanning time in conventional Fourier imaging. In this paper, the application of CS to parallel imaging, a fast imaging technique, is investigated to achieve an even higher imaging speed. The sampling scheme for incoherence is discussed and reconstruction method using Begman iteration is proposed. Our experiments show that the combined method, named SparseSENSE, can achieve a reduction factor higher than the number of channels.

Original languageEnglish
Title of host publication5th Int. Conference on Information Technology and Applications in Biomedicine, ITAB 2008 in conjunction with 2nd Int. Symposium and Summer School on Biomedical and Health Engineering, IS3BHE 2008
Pages127-130
Number of pages4
DOIs
StatePublished - 2008
Event5th International Conference on Information Technology and Applications in Biomedicine, ITAB 2008 in conjunction with 2nd International Symposium and Summer School on Biomedical and Health Engineering, IS3BHE 2008 - Shenzhen, China
Duration: May 30 2008May 31 2008

Publication series

Name5th Int. Conference on Information Technology and Applications in Biomedicine, ITAB 2008 in conjunction with 2nd Int. Symposium and Summer School on Biomedical and Health Engineering, IS3BHE 2008

Conference

Conference5th International Conference on Information Technology and Applications in Biomedicine, ITAB 2008 in conjunction with 2nd International Symposium and Summer School on Biomedical and Health Engineering, IS3BHE 2008
Country/TerritoryChina
CityShenzhen
Period05/30/0805/31/08

Keywords

  • Bregman iteration
  • Compressed sensing
  • SENSE

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