Reconstructing high-fidelity MR images from undersampled k-space data remains a challenging problem in MRI. While Mamba variants for vision tasks offer promising long-range modeling capabilities with linear-time complexity, their direct application to MRI reconstruction inherits two key limitations: (1) insensitivity to high-frequency anatomical details; and (2) reliance on redundant multi-directional scanning. To address these limitations, we introduce High-Fidelity Mamba (HiFi-Mamba), a novel dual-stream Mamba-based architecture comprising stacked W-Laplacian (WL) and HiFi-Mamba blocks. Specifically, the WL block performs fidelity-preserving spectral decoupling, producing complementary low- and high-frequency streams. This separation enables the HiFi-Mamba block to focus on low-frequency structures, enhancing global feature modeling. Concurrently, the HiFi-Mamba block selectively integrates high-frequency features through adaptive state-space modulation, preserving comprehensive spectral details. To eliminate the scanning redundancy, the HiFi-Mamba block adopts a streamlined unidirectional traversal strategy that preserves long-range modeling capability with improved computational efficiency. Extensive experiments on standard MRI reconstruction benchmarks demonstrate that HiFi-Mamba consistently outperforms state-of-the-art CNN-based, Transformer-based, and other Mamba-based models in reconstruction accuracy while maintaining a compact and efficient model design.

翻译：从欠采样的k空间数据重建高保真磁共振图像仍然是磁共振成像领域的一个挑战性问题。尽管用于视觉任务的Mamba变体提供了具有线性时间复杂度的、有前景的长程建模能力，但其直接应用于MRI重建存在两个关键局限性：(1) 对高频解剖细节不敏感；(2) 依赖冗余的多方向扫描。为解决这些局限性，我们提出了高保真Mamba（HiFi-Mamba），这是一种新颖的基于双流Mamba的架构，包含堆叠的W-拉普拉斯（WL）块和HiFi-Mamba块。具体而言，WL块执行保真度谱解耦，产生互补的低频和高频流。这种分离使得HiFi-Mamba块能够专注于低频结构，从而增强全局特征建模。同时，HiFi-Mamba块通过自适应状态空间调制选择性地整合高频特征，保留全面的频谱细节。为消除扫描冗余，HiFi-Mamba块采用了一种简化的单向遍历策略，该策略在保持长程建模能力的同时提高了计算效率。在标准MRI重建基准上进行的大量实验表明，HiFi-Mamba在重建精度上始终优于最先进的基于CNN、基于Transformer以及其他基于Mamba的模型，同时保持了紧凑高效的模型设计。