Accelerating cardiovascular imaging by exploiting regional low-rank structure via group sparsity

Anthony G. Christodoulou, S. Derin Babacan, Zhi Pei Liang

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

Abstract

Sparse sampling of (k, t)-space has proved useful for cardiac MRI. This paper builds on previous work on using partial separability (PS) and spatial-spectral sparsity for high-quality image reconstruction from highly undersampled (k, t)-space data. This new method uses a more flexible control over the PS-induced low-rank constraint via group-sparse regularization. A novel algorithm is also described to solve the corresponding (1,2)-norm regularized inverse problem. Reconstruction results from simulated cardiovascular imaging data are presented to demonstrate the performance of the proposed method.

Original languageEnglish (US)
Title of host publication2012 9th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2012 - Proceedings
Pages330-333
Number of pages4
DOIs
StatePublished - 2012
Event2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012 - Barcelona, Spain
Duration: May 2 2012May 5 2012

Publication series

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

Other

Other2012 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2012
CountrySpain
CityBarcelona
Period5/2/125/5/12

Keywords

  • Cardiovascular MRI
  • Group sparsity
  • Inverse problems
  • Low-rank modeling
  • Partial separability

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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