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T2 mapping from highly undersampled data by reconstruction of principal component coefficient maps using compressed sensing†

  1. Chuan Huang1,
  2. Christian G. Graff2,‡,
  3. Eric W. Clarkson3,4,
  4. Ali Bilgin5,6,
  5. Maria I. Altbach4,*

Article first published online: 16 AUG 2011

DOI: 10.1002/mrm.23128

Issue

Magnetic Resonance in Medicine

Magnetic Resonance in Medicine

Volume 67, Issue 5, pages 1355–1366, May 2012

Additional Information

How to Cite

Huang, C., Graff, C. G., Clarkson, E. W., Bilgin, A. and Altbach, M. I. (2012), T2 mapping from highly undersampled data by reconstruction of principal component coefficient maps using compressed sensing. Magn. Reson. Med., 67: 1355–1366. doi: 10.1002/mrm.23128

Author Information

  1. 1

    Department of Mathematics, University of Arizona, Tucson, Arizona, USA

  2. 2

    Program in Applied Mathematics, University of Arizona, Tucson, Arizona, USA

  3. 3

    The College of Optical Sciences, University of Arizona, Tucson, Arizona, USA

  4. 4

    Department of Radiology, University of Arizona, Tucson, Arizona, USA

  5. 5

    Department of Electrical and Computer Engineering, University of Arizona, Tucson, Arizona, USA

  6. 6

    Department of Biomedical Engineering, University of Arizona, Tucson, Arizona, USA

  1. ‡

    Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD

*Department of Radiology, University of Arizona, Tucson, AZ 85724

  1. †

    This article was originally presented in part at the 2010 ISMRM Meeting in Stockholm, Sweden.

Publication History

  1. Issue published online: 6 APR 2012
  2. Article first published online: 16 AUG 2011
  3. Manuscript Accepted: 8 JUL 2011
  4. Manuscript Revised: 14 JUN 2011
  5. Manuscript Received: 9 SEP 2010

Funded by

  • NIH. Grant Number: HL085385

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Keywords:

  • parameter mapping;
  • T2;
  • radial MRI;
  • FSE;
  • compressed sensing;
  • principal component analysis

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. Magn Reson Med, 2012. © 2011 Wiley Periodicals, Inc.

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