用概率传输方法提升三维荧光显微镜成像质量，同时给出每像素可信度。
VOLT: Volumetric Wide-Field Microscopy via 3D-Native Probabilistic Transport
- 基于随机插值的3D原生传输框架，直接在体素空间处理
- 重建质量显著提升，横向和纵向都更清晰
- 支持随机与确定性两种版本，可评估每个体素可信度
三维宽场荧光显微镜是体积成像的重要手段，但存在离焦模糊的固有缺陷。现有重建方法或难以处理高维体积数据，或无法提供重建结果的可信度评估。本文提出体积传输（VOLT），一种3D原生的概率化重建框架。VOLT结合基于传输的公式，通过随机插值将退化的测量映射到清晰体积，并采用3D原生各向异性网络分离横向与纵向处理。该设计直接在体素空间运行，无需分片近似，实现对大体积数据的高效扩展。我们在模拟宽场显微数据集上验证了VOLT，结果表明其在横向和纵向均显著提升重建质量，并提供体素级可信度估计。框架内同时构建了随机（SDE）与确定性（ODE）两种变体。
原文摘要 · Abstract (English)
Three-dimensional (3D) wide-field fluorescence microscopy is a widely used modality for volumetric imaging, but suffers from characteristic out-of-focus blur. Existing reconstruction methods either struggle to operate on high-dimensional volumes or fail to provide credibility characterization of the reconstruction. In this work, we introduce Volumetric Transport (VOLT), a 3D-native probabilistic framework for wide-field fluorescence microscopy reconstruction. VOLT combines a transport-based formulation that maps degraded measurements to clean volumes via stochastic interpolants with a 3D-native anisotropic network that separates lateral and axial processing. This design operates directly in voxel space and achieves improved scalability to large volumes without relying on slice-wise approximations. We develop both stochastic (SDE) and deterministic (ODE) variants within the same framework. We validate VOLT on simulated wide-field microscopy datasets. Our results show that VOLT significantly improves reconstruction quality in both lateral and axial directions while providing voxel-wise credibility estimates.
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