系统和样本无关的各向同性三维显微成像：基于弱物理信息、抗域偏移的轴向去模糊方法
System- and Sample-agnostic Isotropic 3D Microscopy by Weakly Physics-informed, Domain-shift-resistant Axial Deblurring
AI总结：
本文提出SSAI-3D框架，通过生成PSF灵活、抗噪声的训练数据并稀疏微调预训练盲去模糊网络，实现系统和样本无关的稳健各向同性三维显微成像。
摘要：
三维（3D）亚细胞成像对生物医学研究至关重要，但光学显微镜的衍射极限损害了轴向分辨率，阻碍了准确的三维结构分析。这一挑战在厚且异质的组织的无标记成像中尤为突出，因为关于数据分布的假设（例如稀疏性、标记特异性分布以及横向-轴向相似性）和系统先验（例如独立同分布（i.i.d.）噪声和线性平移不变（LSI）点扩散函数（PSFs））往往不成立。在此，我们提出SSAI-3D，一个弱物理信息引导、抗域偏移的框架，用于实现稳健的各向同性三维成像。SSAI-3D通过生成一个PSF灵活、抗噪声、样本信息驱动的训练数据集，并对一个大型预训练盲去模糊网络进行稀疏微调，从而实现稳健的轴向去模糊。SSAI-3D被应用于活体类器官、新鲜切除的人子宫内膜组织和小鼠触须垫的无标记非线性成像，并在公开可用的具有真实值配对实验数据集中进一步验证，这些数据集来自不同显微系统、具有未知模糊和噪声的三维异质生物组织。
英文摘要：
Three-dimensional (3D) subcellular imaging is essential for biomedical research, but the diffraction limit of optical microscopy compromises axial resolution, hindering accurate 3D structural analysis. This challenge is particularly pronounced in label-free imaging of thick, heterogeneous tissues, where assumptions about data distribution (e.g. sparsity, label-specific distribution, and lateral-axial similarity) and system priors (e.g. independent and identically distributed (i.i.d.) noise and linear shift-invariant (LSI) point-spread functions (PSFs)) are often invalid. Here, we introduce SSAI-3D, a weakly physics-informed, domain-shift-resistant framework for robust isotropic 3D imaging. SSAI-3D enables robust axial deblurring by generating a PSF-flexible, noise-resilient, sample-informed training dataset and sparsely fine-tuning a large pre-trained blind deblurring network. SSAI-3D was applied to label-free nonlinear imaging of living organoids, freshly excised human endometrium tissue, and mouse whisker pads, and further validated in publicly available ground-truth-paired experimental datasets of 3D heterogeneous biological tissues with unknown blurring and noise across different microscopy systems.
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