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Abstract #3374

Combining MR-Physics and Machine Learning to Address Intractable Reconstruction Problems

Berkin Bilgic1, Stephen F Cauley1, Itthi Chatnuntawech2, Mary Kate Manhard1, Fuyixue Wang1, Melissa Haskell1, Congyu Liao1, Lawrence L Wald1, and Kawin Setsompop1

1Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 2National Nanotechnology Center, Pathum Thani, Thailand

We are combining Machine Learning (ML) with MR-physics based image reconstruction to tackle intractable problems. We address open problems that are either too stochastic to be modeled (e.g. shot-to-shot phase variations in multi-shot EPI due to physiological noise), or that admit a computationally prohibitive model (e.g. motion correction with simultaneous estimation of motion parameters and image content). Using ML to jumpstart physics-based non-convex reconstructions dramatically improve their efficiency and helps avoid local minima. In return, MR-physics reconstruction keeps ML in check, and avoids using it as a blackbox. Such synergistic combination also provides >2x reduction in RMSE over conventional reconstruction.

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