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Abstract #1859

Accelerated Magnetic Resonance Imaging with Flow-Based Priors

Frederik Fraaz1 and Reinhard Heckel1,2
1Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany, 2Department of Electrical and Computer Engineering, Rice University, Houston, TX, United States


Convolutional neural networks trained end-to-end achieve state-of-the-art image quality for accelerated MRI. But end-to-end networks are trained for a specific undersampling operator. A more flexible approach that can work with any undersampling operator at inference is to train a generative image prior and impose it during reconstruction. In this work, we train a flow-based generator on image patches and then impose it as a prior in the reconstruction. We find that this method achieves slightly better reconstruction quality than state-of-the-art un-trained methods and slightly worse quality than neural networks trained end-to-end on the 4x accelerated multi-coil fastMRI dataset.

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