Multi-contrast (MC) Magnetic Resonance Imaging (MRI) reconstruction aims to incorporate a reference image of auxiliary modality to guide the reconstruction process of the target modality. Known MC reconstruction methods perform well with a fully sampled reference image, but usually exhibit inferior performance, compared to single-contrast (SC) methods, when the reference image is missing or of low quality. To address this issue, we propose DuDoUniNeXt, a unified dual-domain MRI reconstruction network that can accommodate to scenarios involving absent, low-quality, and high-quality reference images. DuDoUniNeXt adopts a hybrid backbone that combines CNN and ViT, enabling specific adjustment of image domain and k-space reconstruction. Specifically, an adaptive coarse-to-fine feature fusion module (AdaC2F) is devised to dynamically process the information from reference images of varying qualities. Besides, a partially shared shallow feature extractor (PaSS) is proposed, which uses shared and distinct parameters to handle consistent and discrepancy information among contrasts. Experimental results demonstrate that the proposed model surpasses state-of-the-art SC and MC models significantly. Ablation studies show the effectiveness of the proposed hybrid backbone, AdaC2F, PaSS, and the dual-domain unified learning scheme.

翻译：多对比度磁共振成像（MRI）重建旨在融合辅助模态的参考图像，以引导目标模态的重建过程。现有MC重建方法在参考图像完全采样时表现良好，但若参考图像缺失或质量低下，其性能通常劣于单对比度（SC）方法。为解决此问题，我们提出DuDoUniNeXt——一种统一的双域MRI重建网络，可适应参考图像缺失、低质量及高质量等不同场景。DuDoUniNeXt采用CNN与ViT结合的混合骨干网络，实现对图像域与k空间重建的针对性调整。具体而言，我们设计了一种自适应粗到精特征融合模块（AdaC2F），可动态处理不同质量参考图像的信息；同时提出部分共享浅层特征提取器（PaSS），通过共享与独立的参数分别处理对比度间一致与差异信息。实验结果表明，所提模型显著优于当前最优的SC与MC模型。消融研究验证了混合骨干网络、AdaC2F、PaSS及双域统一学习方案的有效性。