Exploiting local low-rank structure in higher-dimensional MRI applications

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

17 Scopus citations

Abstract

In many clinical MRI applications, not one but a series of images is acquired. Techniques that promote intra-and inter-image sparsity have recently emerged as powerful strategies for accelerating MRI applications; however, sparsity alone cannot always describe the complex relationships that exist between images in these series. In this paper, we will discuss the modeling of higher-dimensional MRI signals as matrices and tensors, and why promoting these signals to be low-rank (and, specifically, locally low-rank) can effectively identify and exploit these complex relationships. Example applications including training-free dynamic and calibrationless parallel MRI will be demonstrated.

Original languageEnglish (US)
Title of host publicationWavelets and Sparsity XV
DOIs
StatePublished - Dec 9 2013
EventWavelets and Sparsity XV - San Diego, CA, United States
Duration: Aug 26 2013Aug 29 2013

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8858
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

OtherWavelets and Sparsity XV
CountryUnited States
CitySan Diego, CA
Period8/26/138/29/13

Keywords

  • Image Reconstruction
  • Low-Rank
  • MRI
  • Sparsity

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Trzasko, J. D. (2013). Exploiting local low-rank structure in higher-dimensional MRI applications. In Wavelets and Sparsity XV [885821] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 8858). https://doi.org/10.1117/12.2027059