Multi-contrast MRI acceleration has become prevalent in MR imaging, enabling the reconstruction of high-quality MR images from under-sampled k-space data of the target modality, using guidance from a fully-sampled auxiliary modality. The main crux lies in efficiently and comprehensively integrating complementary information from the auxiliary modality. Existing methods either suffer from quadratic computational complexity or fail to capture long-range correlated features comprehensively. In this work, we propose MMR-Mamba, a novel framework that achieves comprehensive integration of multi-contrast features through Mamba and spatial-frequency information fusion. Firstly, we design the \textit{Target modality-guided Cross Mamba} (TCM) module in the spatial domain, which maximally restores the target modality information by selectively absorbing useful information from the auxiliary modality. Secondly, leveraging global properties of the Fourier domain, we introduce the \textit{Selective Frequency Fusion} (SFF) module to efficiently integrate global information in the frequency domain and recover high-frequency signals for the reconstruction of structure details. Additionally, we present the \textit{Adaptive Spatial-Frequency Fusion} (ASFF) module, which enhances fused features by supplementing less informative features from one domain with corresponding features from the other domain. These innovative strategies ensure efficient feature fusion across spatial and frequency domains, avoiding the introduction of redundant information and facilitating the reconstruction of high-quality target images. Extensive experiments on the BraTS and fastMRI knee datasets demonstrate the superiority of the proposed MMR-Mamba over state-of-the-art MRI reconstruction methods.

翻译：多对比度磁共振成像加速技术已在磁共振成像中广泛应用，其通过利用全采样的辅助模态数据作为引导，从欠采样的目标模态k空间数据中重建出高质量的磁共振图像。该方法的核心在于如何高效且全面地整合来自辅助模态的互补信息。现有方法要么受限于二次计算复杂度，要么未能全面捕获长程相关特征。在本工作中，我们提出MMR-Mamba，一种新颖的框架，通过Mamba与空频信息融合实现多对比度特征的全面整合。首先，我们在空间域设计了\textit{目标模态引导的交叉Mamba}模块，该模块通过选择性地从辅助模态吸收有用信息，最大限度地恢复目标模态信息。其次，利用傅里叶域的全局特性，我们引入了\textit{选择性频率融合}模块，以高效整合频域全局信息并恢复高频信号，从而重建结构细节。此外，我们提出了\textit{自适应空频融合}模块，该模块通过用一个域中信息量较少的特征补充另一个域中的对应特征，从而增强融合特征。这些创新策略确保了空间域与频率域之间的高效特征融合，避免了冗余信息的引入，并促进了高质量目标图像的重建。在BraTS和fastMRI膝关节数据集上进行的大量实验表明，所提出的MMR-Mamba优于当前最先进的磁共振成像重建方法。