T 2 mapping from highly undersampled data by reconstruction of principal component coefficient maps using compressed sensing

Chuan Huang, Christian G. Graff, Eric W Clarkson, Ali Bilgin, Maria I Altbach

Research output: Contribution to journalArticle

85 Citations (Scopus)

Abstract

Recently, there has been an increased interest in quantitative MR parameters to improve diagnosis and treatment. Parameter mapping requires multiple images acquired with different timings usually resulting in long acquisition times. While acquisition time can be reduced by acquiring undersampled data, obtaining accurate estimates of parameters from undersampled data is a challenging problem, in particular for structures with high spatial frequency content. In this work, principal component analysis is combined with a model-based algorithm to reconstruct maps of selected principal component coefficients from highly undersampled radial MRI data. This novel approach linearizes the cost function of the optimization problem yielding a more accurate and reliable estimation of MR parameter maps. The proposed algorithm-reconstruction of principal component coefficient maps using compressed sensing-is demonstrated in phantoms and in vivo and compared with two other algorithms previously developed for undersampled data.

Original languageEnglish (US)
Pages (from-to)1355-1366
Number of pages12
JournalMagnetic Resonance in Medicine
Volume67
Issue number5
DOIs
StatePublished - May 2012

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Principal Component Analysis
Costs and Cost Analysis

Keywords

  • compressed sensing
  • FSE
  • parameter mapping
  • principal component analysis
  • T *radial MRI

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Medicine(all)

Cite this

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abstract = "Recently, there has been an increased interest in quantitative MR parameters to improve diagnosis and treatment. Parameter mapping requires multiple images acquired with different timings usually resulting in long acquisition times. While acquisition time can be reduced by acquiring undersampled data, obtaining accurate estimates of parameters from undersampled data is a challenging problem, in particular for structures with high spatial frequency content. In this work, principal component analysis is combined with a model-based algorithm to reconstruct maps of selected principal component coefficients from highly undersampled radial MRI data. This novel approach linearizes the cost function of the optimization problem yielding a more accurate and reliable estimation of MR parameter maps. The proposed algorithm-reconstruction of principal component coefficient maps using compressed sensing-is demonstrated in phantoms and in vivo and compared with two other algorithms previously developed for undersampled data.",
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AU - Graff, Christian G.

AU - Clarkson, Eric W

AU - Bilgin, Ali

AU - Altbach, Maria I

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