论文标题
加速平行MR图像重建的深网插值
Deep Network Interpolation for Accelerated Parallel MR Image Reconstruction
论文作者
论文摘要
我们提出了一个深层网络插值策略，用于加速平行的MR图像重建。特别是，我们检查了参数空间中的网络插值之间的源模型之间的网络插值，该源模型以L1和SSIM损失的展开方案配制，并通过对抗性损失进行了训练。我们表明，通过在同一网络结构的两个不同模型之间进行插值，新的插值网络可以在感知质量和忠诚度之间建模折衷。
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.
