Abstract #4654

Multi-scale Unrolled Deep Learning Network for Accelerated MRI

Ukash Nakarmi^1,2, Joseph Yitan Cheng^1,2, Edgar Anselmo Rios Piedra^1,2, Morteza Mardani^1,2, John M Pauly², Leslie Ying^3,4, and Shreyas Vasanawala¹

¹Department of Radiology, Stanford University, Stanford, CA, United States, ²Department of Electrical Engineering, Stanford University, Stanford, CA, United States, ³Department of Electrical Engineering, University at Buffalo, Buffalo, NY, United States, ⁴Department of Biomedical Engineering, University at Buffalo, Buffalo, NY, United States

Model prior based reconstruction and data-centric prior reconstruction are two strong paradigms in image reconstruction inverse problems. In this abstract, we propose a framework that integrates the model prior and data-centric multi-scale deep learning priors for reconstructing magnetic resonance images (MRI) from undersampled k-space data. The proposed framework brings together the best of both paradigms and has proven superior to conventional accelerated MRI reconstruction techniques.

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