  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.
