Volume Electron Microscopy (VEM) is crucial for 3D tissue imaging but often produces anisotropic data with poor axial resolution, hindering visualization and downstream analysis. Existing methods for isotropic reconstruction often suffer from neglecting abundant axial information and employing simple downsampling to simulate anisotropic data. To address these limitations, we propose VEMamba, an efficient framework for isotropic reconstruction. The core of VEMamba is a novel 3D Dependency Reordering paradigm, implemented via two key components: an Axial-Lateral Chunking Selective Scan Module (ALCSSM), which intelligently re-maps complex 3D spatial dependencies (both axial and lateral) into optimized 1D sequences for efficient Mamba-based modeling, explicitly enforcing axial-lateral consistency; and a Dynamic Weights Aggregation Module (DWAM) to adaptively aggregate these reordered sequence outputs for enhanced representational power. Furthermore, we introduce a realistic degradation simulation and then leverage Momentum Contrast (MoCo) to integrate this degradation-aware knowledge into the network for superior reconstruction. Extensive experiments on both simulated and real-world anisotropic VEM datasets demonstrate that VEMamba achieves highly competitive performance across various metrics while maintaining a lower computational footprint. The source code is available on GitHub: https://github.com/I2-Multimedia-Lab/VEMamba

翻译：体积电子显微镜（VEM）对于三维组织成像至关重要，但其生成的数据常呈现各向异性，轴向分辨率较低，这阻碍了可视化与下游分析。现有的各向同性重建方法往往忽略丰富的轴向信息，并采用简单的下采样来模拟各向异性数据。为应对这些局限，我们提出了VEMamba，一种高效的各向同性重建框架。VEMamba的核心是一种新颖的三维依赖关系重排序范式，通过两个关键组件实现：轴向-侧向分块选择性扫描模块（ALCSSM），该模块智能地将复杂的三维空间依赖关系（包括轴向和侧向）重新映射为优化的一维序列，以支持基于Mamba的高效建模，并显式地强制轴向-侧向一致性；以及动态权重聚合模块（DWAM），用于自适应地聚合这些重排序后的序列输出，以增强表征能力。此外，我们引入了一种真实的退化模拟，并利用动量对比（MoCo）将这种退化感知知识整合到网络中，以实现更优的重建。在模拟和真实世界各向异性VEM数据集上的大量实验表明，VEMamba在各种指标上均取得了极具竞争力的性能，同时保持了较低的计算开销。源代码已在GitHub上发布：https://github.com/I2-Multimedia-Lab/VEMamba