Progressive dual-domain-transfer cycleGAN for unsupervised MRI reconstruction

Abstract

Supervised MRI reconstruction methods perform well when provided with matched undersampled and fully sampled data pairs. However, acquiring paired data can be expensive and impractical in several clinical settings, such as cine MRI. As a result, recent studies have focused on reducing the reliance on paired data. Yet, most unsupervised approaches have attempted to directly convert the undersampled data domain into the fully sampled data domain, potentially leading to subpar reconstruction performance due to significant domain discrepancies. In this study, we propose a progressive dual-domain-transfer cycleGAN (PDD-GAN) to effectively address this issue. Our proposed method develops a dual-domain framework in an unsupervised manner, enabling the learning of representations from both image and frequency domains. Simultaneously, we break down the direct domain transfer problem into a multi-stage issue and solve it progressively, correcting reconstruction errors and preserving anatomical information at each transfer step. We conduct comprehensive experiments demonstrating that our approach outperforms several state-of-the-art supervised and unsupervised models on two public datasets. The code is publicly available at https://github.com/Coolwen1997/PDD-GAN.