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Abstract #3616

Deep Network Interpolation for Accelerated Parallel MR Image Reconstruction

Chen Qin1, Jo Schlemper1,2, Kerstin Hammernik1, Jinming Duan3, Ronald M Summers4, and Daniel Rueckert1
1Imperial 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 present a deep network interpolation strategy for accelerated parallel MR image reconstruction. In particular, we examine the network interpolation in parameter space between a source model that is formulated in an unrolled scheme with L1 and SSIM losses and its counterpart that is trained with an adversarial loss. We show that by interpolating between the two different models of the same network structure, the new interpolated network can model a trade-off between perceptual quality and fidelity.

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