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Abstract #3642

Plug-and-Play Deep Learning Module for Faster Parallel MR Imaging

Kamlesh Pawar1, Gary Egan1, and Zhaolin Chen1
1Monash Biomedical Imaging, Monash University, Melbourne, Australia

Deep learning (DL) methods are superior to the conventional method of accelerated imaging such as parallel imaging and compressed sensing but the integration of DL methods into the MR scanners is still in its infancy. The integration of the DL methods into the MR scanner requires the design of new pulse sequences with a modified sampling patterns/trajectories and development of DL reconstruction framework within the MR scanner. In this work, we present an effective plug-and-play approach of integrating the DL reconstruction into the MR scanner that eliminates the need to modify the existing image acquisition and reconstruction pipeline.

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