Automated quality control protocol for MR spectra of brain tumors

Abstract

eTUMOUR (http://www.etumour.net/) is acquiring a large database of brain tumor (1)H 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.