用傅里叶域Transformer实现多细胞样本的光学像差校正，无需昂贵硬件。
Fourier-Based 3D Multistage Transformer for Aberration Correction in Multicellular Specimens
- 基于3D多阶段视觉Transformer，在傅里叶域直接感知像差。
- 在斑点标记样本上实现衍射极限成像，计算成本大幅降低。
- 适用于活体斑马鱼胚胎，支持硬件或后处理校正，简化实验流程。
高分辨率组织成像常受样本引起的光学像差影响，导致分辨率和对比度下降。虽然波前传感器自适应光学（AO）可测量这些像差，但此类硬件方案通常复杂、昂贵且在大视场内逐点扫描时速度慢。本文提出AOViFT（自适应光学视觉傅里叶变换器）——一种基于机器学习的像差感知框架，其核心为3D多阶段视觉Transformer，操作于傅里叶域嵌入。相比传统架构或实空间网络，AOViFT在斑点标记样本上显著降低了计算成本、训练时间和内存占用，同时实现衍射极限性能恢复。我们在基因编辑活体斑马鱼胚胎上验证了该方法，证明其可利用可变形镜或后处理去卷积有效校正空间变化像差。通过省去导星与波前传感硬件，简化实验流程，显著降低了多样生物样本中高分辨率体积显微技术的技术门槛。
原文摘要 · Abstract (English)
High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. While wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement, and slow when serially mapping spatially varying aberrations across large fields of view. Here, we introduce AOViFT (Adaptive Optical Vision Fourier Transformer) -- a machine learning-based aberration sensing framework built around a 3D multistage Vision Transformer that operates on Fourier domain embeddings. AOViFT infers aberrations and restores diffraction-limited performance in puncta-labeled specimens with substantially reduced computational cost, training time, and memory footprint compared to conventional architectures or real-space networks. We validated AOViFT on live gene-edited zebrafish embryos, demonstrating its ability to correct spatially varying aberrations using either a deformable mirror or post-acquisition deconvolution. By eliminating the need for the guide star and wavefront sensing hardware and simplifying the experimental workflow, AOViFT lowers technical barriers for high-resolution volumetric microscopy across diverse biological samples.
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