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K-T ISD: Compressed sensing with iterative support detection for dynamic MRI

  • Dong Liang
  • , Edward V.R. Dibella
  • , Rong Rong Chen
  • , Leslie Ying
  • University of Wisconsin-Milwaukee
  • University of Utah

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

4 Scopus citations

Abstract

In this paper, we propose a new k-t Iterative Support Detection (k-t ISD) method to improve the CS reconstruction for dynamic cardiac MRI by incorporating additional information on the support of the dynamic image in x-f space. The proposed method uses an iterative procedure for alternating image reconstruction and support detection in x-f space. Experimental results demonstrate that the proposed k-t ISD method improves the reconstruction quality of dynamic cardiac MRI over the basic CS method in which support information is not exploited.

Original languageEnglish
Title of host publication2011 8th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI'11
Pages1264-1267
Number of pages4
DOIs
StatePublished - 2011
Event2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11 - Chicago, IL, United States
Duration: Mar 30 2011Apr 2 2011

Publication series

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

Conference

Conference2011 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI'11
Country/TerritoryUnited States
CityChicago, IL
Period03/30/1104/2/11

Keywords

  • Compressed sensing
  • dynamic MRI
  • k-t Iterative Support Detection (k-t ISD)
  • partially known support
  • truncated minimization

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