UNSUPERVISED LEARNING FOR COMPRESSED SENSING MRI USING CYCLEGAN

Abstract

Recently, deep learning based approaches for accelerated MRI have been extensively studied due to its high performance and reduced run time complexity. The existing deep learning methods for accelerated MRI are mostly supervised methods, where matched subsampled k-space data and fully sampled k-space data are necessary. However, it is hard to acquire fully sampled k-space data because of long scan time of MRI. Therefore, unsupervised method without matched label data has become a very important research topic. In this paper, we propose an unsupervised method using a novel cycle-consistent generative adversarial network (cycleGAN) with a single deep generator. We show that the proposed cycleGAN architecture can be derived from a dual formulation of the optimal transport with the penalized least squares cost. The results of experiments show that our method can remove aliasing patterns in downsampled MR images without the matched reference data.