Dictionary learning for compressive parameter mapping in magnetic resonance imaging

Benjamin P. Berman, Mahesh B. Keerthivasan, Zhitao Li, Diego R. Martin, Maria I. Altbach, Ali Bilgin

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

Abstract

Parameter mapping is a valuable quantitative tool for soft tissue contrast. Accelerated data acquisition is critical for clinical utility, which has lead to various novel reconstruction techniques. In this work, a model-based compressed sensing method is extended to include a sparse regularization that is learned from the principal component coefficient. The principal components for a range of T2 decay curves are computed, and the coefficients of the principal components are reconstructed. These coefficient maps share coherent spatial structures, suggesting a patch{based dictionary is a well suited sparse transformation. This transformation is learned from the coefficients themselves. The proposed reconstruction is suited for non-Cartesian, multi-channel data. The dictionary constraint leads to parameter maps with less noise and less aliasing for high amounts of acceleration.

Original languageEnglish (US)
Title of host publicationWavelets and Sparsity XVI
EditorsVivek K. Goyal, Dimitri Van De Ville, Dimitri Van De Ville, Manos Papadakis, Dimitri Van De Ville, Manos Papadakis, Vivek K. Goyal, Dimitri Van De Ville
PublisherSPIE
ISBN (Electronic)9781628417630, 9781628417630
DOIs
StatePublished - 2015
EventWavelets and Sparsity XVI - San Diego, United States
Duration: Aug 10 2015Aug 12 2015

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume9597
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceWavelets and Sparsity XVI
CountryUnited States
CitySan Diego
Period8/10/158/12/15

Keywords

  • Compressed sensing
  • MRI
  • Radial
  • Sparsity
  • T

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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