Abstract #3616

Deep Network Interpolation for Accelerated Parallel MR Image Reconstruction

Chen Qin¹, Jo Schlemper^1,2, 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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