Automated detection of motion artifacts in brain MR images using deep learning and explainable artificial intelligence
<|reference_start|>Automated detection of motion artifacts in brain MR images using deep learning and explainable artificial intelligence: 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.<|reference_end|>
@article{jimeno2024automated,
    title={Automated detection of motion artifacts in brain MR images using deep
  learning and explainable artificial intelligence},
    author={Marina Manso Jimeno, Keerthi Sravan Ravi, Maggie Fung, John Thomas
  Vaughan, Jr., Sairam Geethanath},
    journal={arXiv preprint arXiv:2402.08749},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.08749},
    primaryClass={cs.CV cs.LG}
}