Magnetic resonance imaging (MRI) plays a vital role in clinical diagnostics, yet it remains hindered by long acquisition times and motion artifacts. Multi-contrast MRI reconstruction has emerged as a promising direction by leveraging complementary information from fully-sampled reference scans. However, existing approaches suffer from three major limitations: (1) superficial reference fusion strategies, such as simple concatenation, (2) insufficient utilization of the complementary information provided by the reference contrast, and (3) fixed under-sampling patterns. We propose an efficient and interpretable frequency error-guided reconstruction framework to tackle these issues. We first employ a conditional diffusion model to learn a Frequency Error Prior (FEP), which is then incorporated into a unified framework for jointly optimizing both the under-sampling pattern and the reconstruction network. The proposed reconstruction model employs a model-driven deep unfolding framework that jointly exploits frequency- and image-domain information. In addition, a spatial alignment module and a reference feature decomposition strategy are incorporated to improve reconstruction quality and bridge model-based optimization with data-driven learning for improved physical interpretability. Comprehensive validation across multiple imaging modalities, acceleration rates (4-30x), and sampling schemes demonstrates consistent superiority over state-of-the-art methods in both quantitative metrics and visual quality. All codes are available at https://github.com/fangxinming/JUF-MRI.

翻译：磁共振成像在临床诊断中发挥着至关重要的作用，但其仍受限于较长的采集时间和运动伪影。多对比度磁共振成像重建通过利用全采样参考扫描的互补信息，已成为一个前景广阔的研究方向。然而，现有方法存在三个主要局限：(1) 参考图像融合策略较为浅层，如简单拼接；(2) 对参考对比度提供的互补信息利用不足；(3) 采用固定的欠采样模式。为解决这些问题，我们提出了一种高效且可解释的频率误差引导重建框架。我们首先采用条件扩散模型学习频率误差先验，随后将其整合到一个统一框架中，用于联合优化欠采样模式与重建网络。所提出的重建模型采用模型驱动的深度展开框架，联合利用频率域与图像域信息。此外，框架中引入了空间对齐模块和参考特征分解策略，以提升重建质量，并桥接基于模型的优化与数据驱动的学习，从而增强物理可解释性。在多种成像模态、加速倍数（4-30倍）及采样方案下的综合验证表明，本方法在定量指标与视觉质量上均持续优于现有最先进方法。所有代码均公开于 https://github.com/fangxinming/JUF-MRI。