Accelerated MRI Reconstruction with Dual-Domain Generative Adversarial Network

Fast reconstruction of under-sampled acquisitions has always been a central issue in MRI reconstruction. Recently years has seen multiple studies using deep learning as a de-aliasing framework to restore the aliased image. However, restoration of fine details is still problematic, especially when dealing with noisy image datasets. Sparked by the Fourier transform relationship, this work proposed and tested a new hypothesis: can regularization be directly added in the frequency domain to correct the high-frequency imperfection? To achieve this, discriminative networks are applied in both the image domain and the frequency domain (so-called dual-domain GAN). Evaluation on multiple datasets proved that the dual-domain GAN approach is an effective way to improve the quality of accelerated MR reconstruction.