A noise-robust accelerated MRI reconstruction using CycleGAN

We propose a novel loss function that increases noise-robustness in accelerated parallel MR image reconstruction. The loss function is based on the variance of the background area in the noisy undersampled image and that of the difference image between noisy undersampled image and the synthesized undersampled image. The proposed loss function provides stronger regularization and robustness when applied along with other constraints. We show that the application of the proposed loss function boosts performance of the network, yielding improved quality of reconstructed image.