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 publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherSPIE
Volume9597
ISBN (Print)9781628417630, 9781628417630
DOIs
Publication statusPublished - 2015
EventWavelets and Sparsity XVI - San Diego, United States
Duration: Aug 10 2015Aug 12 2015

Other

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

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Keywords

  • 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

Cite this

Berman, B. P., Keerthivasan, M. B., Li, Z., Martin, D. R., Altbach, M. I., & Bilgin, A. (2015). Dictionary learning for compressive parameter mapping in magnetic resonance imaging. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 9597). [959707] SPIE. https://doi.org/10.1117/12.2187088