Accelerating CS-MRI Reconstruction With Fine-Tuning Wasserstein Generative Adversarial Network

Abstract

Compressed sensing magnetic resonance imaging (CS-MRI) is a time-efficient method to acquire MR images by taking advantage of the highly under-sampled k-space data to accelerate the time consuming acquisition process. In this paper, we proposed a de-aliasing fine-tuning Wasserstein generative adversarial network (DA-FWGAN) for imaging reconstruction of highly under-sampled k-space data in CS-MRI. In the architecture, we used the fine-tuning method for accurate training of the neural network parameters and the Wasserstein distance as the discrepancy measure between the real and reconstructed images. Furthermore, for better preservation of the fine structures in the reconstructed images, we incorporated perceptual loss, image and frequency loss into the loss function for training the network. With experimental results from 3 different sampling schemes and 3 levels of sampling rates, we compared the reconstruction performance of the DA-FWGAN method with other state-of-the-art deep learning methods for CS-MRI reconstruction, including ADMM-Net, Pixel-GAN, and DAGAN. The proposed DA-FWGAN method outperforms all other methods and can provide superior reconstruction with improved peak signal-to-noise ratio (PSNR) and structural similarity index measure.