Automated quality control protocol for MR spectra of brain tumors

Alan J. Wright, Carles Arús, Jannie P. Wijnen, Angel Moreno-Torres, John R. Griffiths, Bernardo Celda, Franklyn A. Howe

Research output: Contribution to journalArticleResearchpeer-review

29 Citations (Scopus)


eTUMOUR ( is acquiring a large database of brain tumor 1H MR spectra to develop automated pattern recognition methods and decision support system (DSS) for tumor diagnosis. Development of accurate pattern-recognition algorithms requires spectra undistorted by artifacts, low signal-to-noise, or broad lines. eTUMOUR currently uses panels of expert spectroscopists to subjectively grade spectra as being acceptable or unacceptable. Automated quality control (QC) would be more satisfactory for several reasons: 1) to provide a reproducible objective classification of spectrum quality; 2) for use within the future DSS to prevent misdiagnosis due to poor spectrum quality; 3) to rapidly process the very large datasets of 1H spectra being accrued. An automated QC method using independent component analysis for feature extraction with a least-squares support vector machine classifier is presented. Separate training (n = 144) and test sets (n = 98) of single-voxel spectra from brain tumors and other lesions were acquired at multiple clinical centers with short and long echo times. Pairs of expert spectroscopists classified the test set an average of 85% the same. The automated QC classification agreed with an expert for 87% of test spectra, on average, suggesting the method classifies spectrum quality as accurately as expert spectroscopists. © 2008 Wiley-Liss, Inc.
Original languageEnglish
Pages (from-to)1274-1281
JournalMagnetic Resonance in Medicine
Publication statusPublished - 1 Jun 2008


  • Brain tumors
  • MRS
  • Quality control


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