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A constrained least-squares approach to the automated quantitation of in vivo 1H magnetic resonance spectroscopy data

  1. Martin Wilson1,2,*,
  2. Greg Reynolds3,
  3. Risto A. Kauppinen4,
  4. Theodoros N. Arvanitis2,5,
  5. Andrew C. Peet1,2

Article first published online: 27 SEP 2010

DOI: 10.1002/mrm.22579

Issue

Magnetic Resonance in Medicine

Magnetic Resonance in Medicine

Volume 65, Issue 1, pages 1–12, January 2011

Additional Information

How to Cite

Wilson, M., Reynolds, G., Kauppinen, R. A., Arvanitis, T. N. and Peet, A. C. (2011), A constrained least-squares approach to the automated quantitation of in vivo 1H magnetic resonance spectroscopy data. Magn. Reson. Med., 65: 1–12. doi: 10.1002/mrm.22579

Author Information

  1. 1

    Cancer Sciences, University of Birmingham, Birmingham, United Kingdom

  2. 2

    Academic Department of Paediatrics and Child Health, Birmingham Children's Hospital NHS Foundation Trust, Birmingham, United Kingdom

  3. 3

    Pattern Analytics Ltd., Birmingham, United Kingdom

  4. 4

    Department of Radiology, Dartmouth College, Hanover, New Hampshire, USA

  5. 5

    School of Electronic, Electrical and Computer Engineering, University of Birmingham, Birmingham, United Kingdom

*Academic Department of Paediatrics and Child Health, Birmingham Children's Hospital, Steelhouse Lane, Birmingham, West Midlands B4 6NH, United Kingdom

Publication History

  1. Issue published online: 17 DEC 2010
  2. Article first published online: 27 SEP 2010
  3. Manuscript Accepted: 29 JUN 2010
  4. Manuscript Revised: 23 JUN 2010
  5. Manuscript Received: 25 FEB 2010

Funded by

  • Medical Research Council
  • EUFP6
  • Cancer Research (United Kingdom)
  • EPSRC
  • Department of Health

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Keywords:

  • magnetic resonance spectroscopy;
  • quantitation;
  • brain;
  • tumor

Abstract

Totally Automatic Robust Quantitation in NMR (TARQUIN), a new method for the fully automatic analysis of short echo time in vivo 1H Magnetic resonance spectroscopy is presented. Analysis is performed in the time domain using non-negative least squares, and a new method for applying soft constraints to signal amplitudes is used to improve fitting stability. Initial point truncation and Hankel singular value decomposition water removal are used to reduce baseline interference. Three methods were used to test performance. First, metabolite concentrations from six healthy volunteers at 3 T were compared with LCModel™. Second, a Monte-Carlo simulation was performed and results were compared with LCModel™ to test the accuracy of the new method. Finally, the new algorithm was applied to 1956 spectra, acquired clinically at 1.5 T, to test robustness to noisy, abnormal, artifactual, and poorly shimmed spectra. Discrepancies of less than approximately 20% were found between the main metabolite concentrations determined by TARQUIN and LCModel™ from healthy volunteer data. The Monte-Carlo simulation revealed that errors in metabolite concentration estimates were comparable with LCModel™. TARQUIN analyses were also found to be robust to clinical data of variable quality. In conclusion, TARQUIN has been shown to be an accurate and robust algorithm for the analysis of magnetic resonance spectroscopy data making it suitable for use in a clinical setting. Magn Reson Med, 2010. © 2010 Wiley-Liss, Inc.

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