Automated quality control of brain MR images.
OBJECTIVE
To present a novel fully automated method for assessing the quality of magnetic resonance imaging (MRI) data acquired in a clinical trials environment.


METHODS
This work was performed in the context of clinical trials for multiple sclerosis. Quality control (QC) procedures included were: (i) patient brain identity verification, (ii) alphanumeric parameter matching, (iii) signal-to-noise ratio estimation, (iv) gadolinium-enhancement verification, and (v) detection of ghosting due to head motion. Each QC procedure produces a quantitative measurement which is compared against an acceptance threshold that was determined based on receiver operating characteristic analysis of traditional manual and visual QC performed by trained experts.


RESULTS
The automated QC results have high sensitivity and specificity when compared with the visual QC.


CONCLUSIONS
Our automated objective QC procedure can replace many manual subjective procedures to provide increased data throughput while reducing reader variability.
Gedamu
Elias L
EL
Montreal Neurological Institute, McGill University, 3801 University Street, Montreal, Quebec, Canada. elias.gedamu@mcgill.ca
Collins
D L
DL
Arnold
Douglas L
DL
D001931:Brain Mapping / Q000379:methods*; D002986:Clinical Trials as Topic; D003287:Contrast Media; D005682:Gadolinium; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000592:standards*; D008279:Magnetic Resonance Imaging / Q000592:standards*; D011786:Quality Control
D003287:Contrast Media; D005682:Gadolinium