Automated detection of motion artifacts in brain MR images using deep learning and explainable artificial intelligence
CoRR(2024)
摘要
Quality assessment, including inspecting the images for artifacts, is a
critical step during MRI data acquisition to ensure data quality and downstream
analysis or interpretation success. This study demonstrates a deep learning
model to detect rigid motion in T1-weighted brain images. We leveraged a 2D CNN
for three-class classification and tested it on publicly available
retrospective and prospective datasets. Grad-CAM heatmaps enabled the
identification of failure modes and provided an interpretation of the model's
results. The model achieved average precision and recall metrics of 85
on six motion-simulated retrospective datasets. Additionally, the model's
classifications on the prospective dataset showed a strong inverse correlation
(-0.84) compared to average edge strength, an image quality metric indicative
of motion. This model is part of the ArtifactID tool, aimed at inline automatic
detection of Gibbs ringing, wrap-around, and motion artifacts. This tool
automates part of the time-consuming QA process and augments expertise on-site,
particularly relevant in low-resource settings where local MR knowledge is
scarce.
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