标题： HiFi-Mamba：双流 W-拉普拉斯增强 Mamba 用于高保真 MRI 重建 显示英文标题

标题： HiFi-Mamba: Dual-Stream W-Laplacian Enhanced Mamba for High-Fidelity MRI Reconstruction

摘要： 从欠采样的k空间数据中重建高保真MRI图像仍然是MRI中的一个挑战性问题。 虽然用于视觉任务的Mamba变体以线性时间复杂度提供了有前景的长程建模能力，但它们直接应用于MRI重建会继承两个关键限制：(1) 对高频解剖细节不敏感；以及(2) 依赖于冗余的多方向扫描。 为了解决这些限制，我们引入了高保真Mamba（HiFi-Mamba），这是一种新颖的双流Mamba架构，由堆叠的W-Laplacian（WL）和HiFi-Mamba块组成。 具体而言，WL块执行保持保真度的频谱解耦，产生互补的低频和高频流。 这种分离使HiFi-Mamba块能够专注于低频结构，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集成高频特征，保留全面的频谱细节。 为了消除扫描冗余，HiFi-Mamba块采用了一种简化的单向遍历策略，在保持长程建模能力的同时提高了计算效率。 在标准MRI重建基准上的大量实验表明，HiFi-Mamba在重建精度方面始终优于最先进的基于CNN、基于Transformer和其他基于Mamba的模型，同时保持了紧凑高效的模型设计。 显示英文摘要

摘要： 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.