Can I read Coordinate-based Neural Representations for Computational Adaptive Optics in Widefield Microscopy on EtoBox?

Coordinate-based Neural Representations for Computational Adaptive Optics in Widefield Microscopy by Kang, Iksung; Zhang, Qinrong; Yu, Stella X.; Ji, Na is a scholarly article available to read on EtoBox.

What is Coordinate-based Neural Representations for Computational Adaptive Optics in Widefield Microscopy about?

Widefield microscopy is widely used for non-invasive imaging of biological structures at subcellular resolution. When applied to complex specimen, its image quality is degraded by sample-induced optical aberration. Adaptive optics can correct wavefront distortion and restore diffraction-limited resolution but require wavefront sensing and corrective devices, increasing system complexity and cost. Here, we describe a self-supervised machine learning algorithm, CoCoA, that performs joint wavefront estimation and three-dimensional structural information extraction from a single input 3D image stack without the need for external training dataset. We implemented CoCoA for widefield imaging of mouse brain tissues and validated its performance with direct-wavefront-sensing-based adaptive optics. Importantly, we systematically explored and quantitatively characterized the limiting factors of CoCoA's performance. Using CoCoA, we demonstrated the first in vivo widefield mouse brain imaging using machine-learning-based adaptive optics. Incorporating coordinate-based neural representations and a forward physics model, the self-supervised scheme of CoCoA should be applicable to microscopy modal

Author

Kang, Iksung; Zhang, Qinrong; Yu, Stella X.; Ji, Na

Published

2023

Language

EN