A motion compensating prior for dynamic MRI reconstruction using combination of compressed sensing and parallel imaging

Çaǧdaş Bilen, Ivan Selesnick, Yao Wang, Ricardo Otazo, Daniel K. Sodickson

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

Abstract

Many areas in signal processing have benefited from the emergence of compressed sensing and sparse reconstruction methods, one of which is magnetic resonance imaging (MRI). Recent studies showed that MRI acquisition can be highly accelerated with the joint use of compressed sensing and parallel imaging methods. It is also suggested that dynamic MRI can be further improved by making use of temporal correlations. Although methods using motion compensation has been proposed to exploit temporal dependence, most of these require reference frames and/or a sub-portion of k-space to be fully sampled. In this paper we propose a new approach to exploit the motion information during compressed sensing reconstruction without any requirement for reference frames, modeled motion or a specific sampling pattern on the k-space measurements.

Original languageEnglish (US)
Title of host publication2011 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2011
DOIs
StatePublished - 2011
Event2011 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2011 - Brooklyn, NY, United States
Duration: Dec 10 2011Dec 10 2011

Publication series

Name2011 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2011

Other

Other2011 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2011
CountryUnited States
CityBrooklyn, NY
Period12/10/1112/10/11

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Medicine(all)

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