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Automated quality control of brain MR images
Author(s) -
Gedamu Elias L.,
Collins D.L.,
Arnold Douglas L.
Publication year - 2008
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.21434
Subject(s) - ghosting , computer science , context (archaeology) , image quality , artificial intelligence , computer vision , image (mathematics) , paleontology , biology
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. J. Magn. Reson. Imaging 2008;28:308–319. © 2008 Wiley‐Liss, Inc.