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Abstract #4691

Motion Aware Deep Learning Accelerated MRI Reconstruction

Kamlesh Pawar1,2, Gary F Egan1,2,3, and Zhaolin Chen1,4
1Monash Biomedical Imaging, Monash University, Melbourne, Australia, 2School of Psychological Sciences, Monash University, Melbourne, Australia, 3ARC Centre of Excellence for Integrative Brain Function, Monash University, Melbourne, Australia, 4Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, Australia


Deep learning (DL) models for accelerated image reconstruction involves retrospective undersampling of the fully sampled k-space data for training and validation. This strategy is not a true reflection of real-world data and in many instances, the input k-space data is corrupted with artifacts and errors, such as motion artifacts. In this work, we propose to improve existing methods of DL training and validation by incorporating a motion layer during the training process. The incorporation of a motion layer makes the DL model aware of the underlying motion and results in improved image reconstruction in the presence of motion.

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