Magnetic Resonance Imaging (MRI) is an inherently multi-contrast modality, where cross-contrast priors can be exploited to improve image reconstruction from undersampled data. Recently, diffusion models have shown remarkable performance in MRI reconstruction. However, they still struggle to effectively utilize such priors, mainly because existing methods rely on feature-level fusion in image or latent spaces, which lacks explicit structural correspondence and thus leads to suboptimal performance. To address this issue, we propose $\mathbf{I}^2$SB-Inversion, a multi-contrast guided reconstruction framework based on the Schr\"odinger Bridge (SB). The proposed method performs pixel-wise translation between paired contrasts, providing explicit structural constraints between the guidance and target images. Furthermore, an Inversion strategy is introduced to correct inter-modality misalignment, which often occurs in guided reconstruction, thereby mitigating artifacts and improving reconstruction accuracy. Experiments on paired T1- and T2-weighted datasets demonstrate that $\mathbf{I}^2$SB-Inversion achieves a high acceleration factor of up to 14.4 and consistently outperforms existing methods in both quantitative and qualitative evaluations.

翻译：磁共振成像（MRI）本质上是一种多对比度成像模态，可利用跨对比度先验来改进欠采样数据的图像重建。近年来，扩散模型在MRI重建中展现出卓越性能。然而，现有方法仍难以有效利用此类先验，主要因其依赖于图像或潜在空间中的特征级融合，缺乏明确的结构对应关系，从而导致次优性能。为解决该问题，我们提出$\mathbf{I}^2$SB-Inversion——一种基于薛定谔桥的多对比度引导重建框架。该方法在配对对比度图像间执行像素级转换，为引导图像与目标图像提供明确的结构约束。此外，我们引入逆变换策略以校正模态间错位（这在引导重建中常出现），从而减少伪影并提升重建精度。在配对的T1加权与T2加权数据集上的实验表明，$\mathbf{I}^2$SB-Inversion可实现高达14.4的加速因子，并在定量与定性评估中均持续优于现有方法。