Automated detection of motion artifacts in brain MR images using deep
  learning and explainable artificial intelligence
Marina Manso Jimeno Keerthi Sravan Ravi Maggie Fung John Thomas Vaughan, Jr. Sairam Geethanath
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% and 80%
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.