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Abstract #0987

Σ-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction

Kerstin Hammernik1, Jo Schlemper1,2, Chen Qin1, Jinming Duan3, Gavin Seegoolam1, Cheng Ouyang1, Ronald M Summers4, and Daniel Rueckert1
1Department of Computing, Imperial College London, London, United Kingdom, 2Hyperfine Research Inc., Guilford, CT, United States, 3School of Computer Science, University of Birmingham, Birmingham, United Kingdom, 4NIH Clinical Center, Bethesda, MD, United States

We propose an ensembled Ʃ-net for fast parallel MR image reconstruction, including parallel coil networks, which perform implicit coil weighting, and sensitivity networks, involving explicit sensitivity maps. The networks in Ʃ-net are trained with various ways of data consistency, i.e., gradient descent, proximal mapping, and variable splitting, and with a semi-supervised finetuning scheme to adapt to the k-space data at test time. We achieved robust and high SSIM scores by ensembling all models to a Ʃ-net. At the date of submission, Ʃ-net is the leading entry of the public fastMRI multicoil leaderboard.

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