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Abstract #3598

Attention Based Scale Recurrent Network for Under-Sampled MRI Reconstruction

Gabriel della Maggiora1,2,3, Alberto Di Biase1,2,3, Carlos Castillo-Passi2,3,4, and Pablo Irarrazaval1,2,3,4
1Electrical Engineering Department, Pontificia Universidad Católica de Chile, Santiago, Chile, 2Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile, 3Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile, 4Institute for Biological and Medical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile

We propose an Attention Based Scale Recurrent Network for reconstructing under-sampled MRI data. This network is a variation of the recently proposed Scale Recurrent Network for blind deblurring1. We treat the reconstruction problem as a deblurring problem. Thus the under-sampling pattern does not need to be known. We trained and tested our network with the NYU knee dataset available for the fastMRI challenge. The proposed model shows promising results for single-coil reconstruction outperforming both baselines.

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