HiFi-MambaV2：分层共享路由MoE用于高保真MRI重建
HiFi-MambaV2: Hierarchical Shared-Routed MoE for High-Fidelity MRI Reconstruction
AI总结：
HiFi-MambaV2通过分层共享路由MoE和频率一致拉普拉斯金字塔，实现高保真MRI重建，优于多种基线模型。
摘要：
从欠采样k空间数据重建高保真MRI图像需要在恢复高频细节的同时保持解剖一致性。我们提出了HiFi-MambaV2，一种分层共享路由的混合专家（MoE）Mamba架构，将频率分解与内容自适应计算相结合。该模型包含两个核心组件：（i）可分离的频率一致拉普拉斯金字塔（SF-Lap），能够提供抗混叠、稳定的低频和高频流；（ii）分层共享路由MoE，通过每像素top-1稀疏调度到共享专家和局部路由器，实现有效的专业化并保持稳定的跨深度行为。一个轻量级的全局上下文路径被融合到一个展开的数据一致性正则化主干中，以增强长距离推理并保持解剖一致性。在fastMRI、CC359、ACDC、M4Raw和Prostate158数据集上评估，HiFi-MambaV2在PSNR、SSIM和NMSE指标上在单通道和多通道设置以及多个加速因子下均优于CNN、Transformer和先前Mamba基线模型，持续在高频细节和整体结构保真度上取得显著提升。这些结果表明，HiFi-MambaV2能够实现可靠且稳健的MRI重建。
英文摘要：
Reconstructing high-fidelity MR images from undersampled k-space data requires recovering high-frequency details while maintaining anatomical coherence. We present HiFi-MambaV2, a hierarchical shared-routed Mixture-of-Experts (MoE) Mamba architecture that couples frequency decomposition with content-adaptive computation. The model comprises two core components: (i) a separable frequency-consistent Laplacian pyramid (SF-Lap) that delivers alias-resistant, stable low- and high-frequency streams; and (ii) a hierarchical shared-routed MoE that performs per-pixel top-1 sparse dispatch to shared experts and local routers, enabling effective specialization with stable cross-depth behavior. A lightweight global context path is fused into an unrolled, data-consistency-regularized backbone to reinforce long-range reasoning and preserve anatomical coherence. Evaluated on fastMRI, CC359, ACDC, M4Raw, and Prostate158, HiFi-MambaV2 consistently outperforms CNN-, Transformer-, and prior Mamba-based baselines in PSNR, SSIM, and NMSE across single- and multi-coil settings and multiple acceleration factors, consistently surpassing consistent improvements in high-frequency detail and overall structural fidelity. These results demonstrate that HiFi-MambaV2 enables reliable and robust MRI reconstruction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
