Wavelet improved GAN for MRI reconstruction

Yutong Chen; David Firmin; Guang Yang

doi:10.1117/12.2581004

15 February 2021 Wavelet improved GAN for MRI reconstruction

Yutong Chen, David Firmin, Guang Yang

Author Affiliations +

Proceedings Volume 11595, Medical Imaging 2021: Physics of Medical Imaging; 1159513 (2021) https://doi.org/10.1117/12.2581004
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Background: 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. Motivation: 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. Method and Result: 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.

Conference Presentation

Citation Download Citation

Yutong Chen, David Firmin, and Guang Yang "Wavelet improved GAN for MRI reconstruction", Proc. SPIE 11595, Medical Imaging 2021: Physics of Medical Imaging, 1159513 (15 February 2021); https://doi.org/10.1117/12.2581004

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