GDoT: A gated dual domain transformer for enhanced MRI off-resonance correction

Abstract

Deep learning-based MRI reconstruction methods have gained significant attention recently due to the need for accelerated MRI scans. However, existing deep learning-based methods for off-resonance correction rely on simple CNNs, resulting in suboptimal solutions. In this paper, we propose a gated dual domain transformer with gated spatial projection and gated frequency projection to effectively handle complex-valued MRI, as the first attempt to utilize transformer-based model for off-resonance correction. Additionally, we introduce a selective perceptual loss with a novel test-time translation-merger to reconstruct perceptually high-quality images without checkerboard artifacts. Experiments on both simulated and real off-resonance MRI datasets demonstrate the effectiveness of our approach. Furthermore, we also present ablation studies to determine the optimal design choices.