Abstract #3358

Clinical Utility of Deep Learning Motion Correction for Neuroimaging

Kamlesh Pawar¹, Jarrel Seah², Meng Law³, Tom Close⁴, Zhaolin Chen¹, and Gary Egan¹

¹Monash Biomedical Imaging, Monash University, Melbourne, Australia, ²Department of Neuroscience, Monash University, Melbourne, Australia, ³Radiology and Nuclear Medicine, Alfred Health, Melbourne, Australia, ⁴Monash Biomedical Imging, Monash University, Melbourne, Australia

The deep learning techniques have been shown to reduce the motion artifact in simulated motion scenarios and a few volunteer scans, the validation of it during routine clinical scans remains an unanswered question. In this study, we focus on evaluating the quality of images from the DL motion correction approach on a cohort of 27 actual patient motion cases that were obtained from the routine clinical scans. Two board-certified radiologists evaluated 9 anatomical regions in the 3D MPRAGE brain images and rated them on the 3-point scale.

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