Fourier-Based 3D Multistage Transformer for Aberration Correction in Multicellular Specimens

TL;DR

AOViFT is a machine learning framework that corrects optical distortions in 3D tissue imaging, enabling high-resolution microscopy without complex hardware.

Contribution

AOViFT introduces a 3D multistage Vision Transformer operating in the Fourier domain for efficient aberration correction in multicellular specimens.

Findings

AOViFT achieves diffraction-limited performance with reduced computational cost and memory usage.

AOViFT successfully corrects spatially varying aberrations in live zebrafish embryos using deformable mirrors or post-acquisition deconvolution.

The framework eliminates the need for guide stars and wavefront sensing hardware, simplifying high-resolution volumetric microscopy.

Abstract

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.