WaveOrder用物理驱动的AI重建生物分子结构，支持多种显微成像模式。
WaveOrder: A differentiable wave-optical framework for scalable biological microscopy with diverse modalities
- 基于物理规律的可微分光学模型，统一建模多种显微镜成像方式。
- 无需先验知识自动调参，解决复杂退化图像的盲复原问题。
- 适用于从细胞器到斑马鱼的整体定量成像，适合高通量实验研究者。
共焦计算显微技术可通过缓解动态成像中的固有权衡，加速细胞动力学的成像与建模。现有计算显微框架或过于专一，或过于通用，限制了其在固定配置或领域专家之外的应用。我们提出WaveOrder，一种用于成像生物分子架构有序性的通用波光学框架。该框架从多通道采集中重建多样本属性，无论是否含荧光。它提供线性光学特性的统一表征，并建立涵盖宽场、共焦、光片及斜射无标记几何结构的可微分物理成像模型。WaveOrder利用物理信息机器学习实现模型参数自动调优，并求解盲变移不变恢复问题。此开源、基于PyTorch的框架支持从细胞器到成年斑马鱼的跨尺度定量成像，在高通量实验中显著提升细胞结构恢复效果。我们在多种成像应用中验证了WaveOrder，证明其能突破现有方法极限，恢复出更精细的生物分子结构。
原文摘要 · Abstract (English)
Correlative computational microscopy can accelerate imaging and modeling of cellular dynamics by relaxing trade-offs inherent to dynamic imaging. Existing computational microscopy frameworks are either specialized or overly generic, limiting use to fixed configurations or domain experts. We introduce WaveOrder, a generalist wave-optical framework for imaging the architectural order of biomolecules. WaveOrder reconstructs diverse specimen properties from multi-channel acquisitions, with or without fluorescence. It provides a unified representation of linear optical properties and differentiable physics-based image formation models spanning widefield, confocal, light-sheet, and oblique label-free geometries. WaveOrder uses physics-informed ML to auto-tune model parameters and solve blind shift-variant restoration problems. This open-source, PyTorch-based framework enables scalable quantitative imaging across scales from organelles to adult zebrafish, and improves restoration of cellular structures in high-throughput experiments. We validate WaveOrder on diverse imaging applications, demonstrating its ability to recover biomolecular structure beyond the limits of existing approaches.
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