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Abstract #1006

Unrolled Physics-Based Deep Learning MRI Reconstruction with Dense Connections using Nesterov Acceleration  

Seyed Amir Hossein Hosseini1,2, Burhaneddin Yaman1,2, Steen Moeller2, Kamil Ugurbil2, Mingyi Hong1, and Mehmet Akcakaya1,2
1Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States, 2Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States

Numerous studies have recently employed deep learning (DL) for accelerated MRI reconstruction. Physics-based DL-MRI techniques unroll an iterative optimization procedure into a recurrent neural network, by alternating between linear data consistency and neural network-based regularization units. Data consistency unit typically implements a gradient step. We hypothesize that further gains can be achieved by allowing dense connections within unrolled network, facilitating information flow. Thus, we propose to unroll a Nesterov-accelerated gradient descent that considers the history of previous iterations. Results indicate that this method considerably improves reconstruction over unrolled gradient descent schemes without skip connections.

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