Compressed Sensing MRI Reconstruction using Generative Adversarial Networks with Cyclic Loss

Abstract

Compressed Sensing MRI (CS-MRI) has provided theoretical foundations upon which the time-consuming MRI acquisition process can be accelerated. However, it primarily relies on iterative numerical solvers which still hinders their adaptation in time-critical applications. In addition, recent advances in deep neural networks have shown their potential in computer vision and image processing, but their adaptation to MRI reconstruction is still at an early stage. Therefore, we propose a novel compressed sensing MRI reconstruction algorithm based on a deep generative adversarial neural network with cyclic data consistency constraint. The proposed method is fast and outperforms the state-of-the-art CS-MRI methods by a large margin in running times and image quality, which is demonstrated via evaluation using several open-source MRI databases.