Wavelet improved GAN for MRI reconstruction
Yutong Chen, David Firmin, Guang Yang
Abstract
<strong>Background:</strong> Compressed sensing magnetic resonance imaging (CS-MRI) is an important technique of accel- erating the acquisition process of magnetic resonance (MR) images by undersampling. It has the potential of reducing MR scanning time and costs, thus minimising patient discomfort. <strong>Motivation:</strong> One of the successful CS-MRI techniques to recover the original image from undersampled images is generative adversarial network (GAN). However, GAN-based techniques suffer from three key limitations: training instability, slow convergence and input size constraints. <strong>Method and Result:</strong> In this study, we propose a novel GAN-based CS-MRI technique: WPD-DAGAN (Wavelet Packet Decomposition Improved de-aliaising GAN). We incorporate Wasserstein loss function and a novel structure based on wavelet packet decomposition (WPD) into the de-aliaising GAN (DAGAN) architecture, which is a well established GAN-based CS-MRI technique. We show that the proposed network architecture achieves a significant performance improvement over the state-of-the-art CS-MRI techniques.