Coordinate-based neural representations for computational adaptive optics in widefield microscopy

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 modalities in general.

Fast and accurate aberration estimation from 3D bead images using convolutional neural networks

Estimating optical aberrations from volumetric intensity images is a key step in sensorless adaptive optics for microscopy. Here we describe a method (PHASENET) for fast and accurate aberration measurement from experimentally acquired 3D bead images using convolutional neural networks. Importantly, we show that networks trained only on synthetically generated data can successfully predict aberrations from experimental images. We demonstrate our approach on two data sets acquired with different microscopy modalities and find that PHASENET yields results better than or comparable to classical methods while being orders of magnitude faster. We furthermore show that the number of focal planes required for satisfactory prediction is related to different symmetry groups of Zernike modes. PHASENET is freely available as open-source software in Python.