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Abstract #3452

Learning Deep Linear Convolutional Transforms For Accelerated MRI

Hongyi Gu1,2, Burhaneddin Yaman1,2, Steen Moeller2, Il Yong Chun3, and Mehmet Akçakaya1,2
1Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, United States, 2Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, United States, 3Electrical and Computer Engineering, University of Hawai’i at Mānoa, Honolulu, HI, United States

Synopsis

Research shows that deep learning (DL) based MRI reconstruction outperform conventional methods, such as parallel imaging and compressed sensing (CS). Unlike CS with pre-determined linear representations for regularization, DL uses nonlinear representations learned from a large database. Transform learning (TL) is another line of work bridging the gap between these two approaches. In this work, we combine ideas from CS, TL and DL to learn deep linear convolutional transforms, which has comparable performance to DL and supports uniform under-sampling unlike CS, while enabling sparse convex optimization at inference time.

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