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


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 publicationProceedings of SPIE - The International Society for Optical Engineering
ISBN (Print)9781628417630, 9781628417630
StatePublished - 2015
EventWavelets and Sparsity XVI - San Diego, United States
Duration: Aug 10 2015Aug 12 2015


OtherWavelets and Sparsity XVI
Country/TerritoryUnited States
CitySan Diego


  • Compressed sensing
  • MRI
  • Radial
  • Sparsity
  • T

ASJC Scopus subject areas

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


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