  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.
