Keywords: Data Processing, Artifacts
Motivation: The implementation of QC in T1w MRI scans remains unstandardized and still involves human specialists, without consensus on a systematic approach to identify the presence of artifacts
Goal(s): Our goal was to compare the relative performance of three algorithms with visual QC, and compare their capabilities in detecting simulated blurring, ghosting, and noise artifacts on a new unseen dataset.
Approach: Synthetic artifacts were introduced into MRI scans that passed visual quality control, and thresholds were determined for CAT12 and LONIQC, and a classifier for MRIQC was trained.
Results: MRIQC outperformed CAT12 and LONIQC in detecting both real artifacts as well as simulated artifacts.
Impact: Substantial differences in the performance of different automatic quality control algorithms were shown when compared to visual QC and on simulated data. This suggests that better evaluation of the relation between artifact type, input features and classification methods is needed.
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