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.

通过 MMR-Mamba 框架中 Mamba 和空间频率信息融合，从辅助模态获取的信息，实现了多对比度 MRI 加速成像和高质量重建目标图像，通过创新的策略有效融合了空间和频率领域的特征，避免冗余信息的引入。