Automated quality control of brain MR images

Abstract

PURPOSE: To present a novel fully automated method for assessing the quality of magnetic resonance imaging (MRI) data acquired in a clinical trials environment. MATERIALS AND 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. CONCLUSION: Our automated objective QC procedure can replace many manual subjective procedures to provide increased data throughput while reducing reader variability.

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