Image Registration using Averaging VoxelMorph with CNN Edge Detector

Motivation: Image registration followed by averaging is a common technique to improve the quality of free-breathing single-shot cardiac images. However, registering images becomes challenging when the SNR is low. Goal(s): Improve image registration for free-breathing cardiac MRI. Approach: We train a network, called AvgMorph, to register all source images to one target image. In addition, we use the output of a sophisticated deep learning-based edge detector to compute loss. Results: We validate AvgMorph using a realistic MRXCAT digital phantom for late gadolinium enhancement. AvgMorph outperforms existing methods in terms of NMSE, SSIM, and perceptual quality metrics. Impact: Pairwise registration of free-breathing images is suboptimal. We propose a network to register all source images to a single target image and utilize a loss function computed to edge maps rather than the images themselves.