AI-Powered Artefact Removal Can Identify Poor-Quality MRI Images with Near-Human Accuracy in Milliseconds

At UNC’s Biomedical Research Imaging Center (BRIC), a team is exploring the use of deep learning to identify poor-quality images with near-human accuracy in milliseconds. Their investigative work is aimed at increasing timely decision-making in MRI re-scan. RAC is an increasingly investigated technique in MRI for the correction of motion-induced artefacts. Their study in applied imaging evidences superior motion correction via artificial intelligence (AI) techniques for RAC. Their investigation demonstrates further study of reliable AI techniques for RAC is warranted to benefit image correction and reconstruction in future MRI studies.

“AI-powered RAC can salvage innumerable images with motion artefacts to significantly boost the quantity of usable images and reduce financial losses for imaging studies,” said Pew-Thian Yap, PhD, Image Analysis Core Director at BRIC, who is leading the team.

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UNC School of Medicine