DOI: 10.5281/zenodo.15420769
title: Graph-based modeling of optical system enables adaptive optics on dynamic samples with self-calibration

description: Fluorescence microscopy is fundamentally limited by aberrations that degrade resolution and image quality. While adaptive optics can compensate for these distortions, existing approaches face significant limitations: sensor-based methods require complex hardware that increases system complexity and cost, while computational methods rely on idealized models that lead to suboptimal correction when applied to real systems with inevitable imperfections. Moreover, existing computational methods assume static samples, limiting their applicability to live imaging where specimen motion and biological activity occur during acquisition. Here we introduce GRAPHYCS, a computational framework that bridges the gap between models and physical systems through differentiable graph-based modeling with automatic self-calibration and dynamic sample compatibility. In simulations, GRAPHYCS achieves a wavefront RMS error of 0.0252 μm compared to 0.2367 μm for the analytic phase-diversity wavefront sensing method under system non-idealities, and delivers superior aberration-corrected image quality (PSNR: 27.67 dB vs. 23.68 dB). In real-world microscopy experiments, GRAPHYCS demonstrates enhanced sample-induced aberration correction with PSNR of 21.35 dB compared to 19.68 dB for analytic phase-diversity, SSIM of 0.7713 vs. 0.5966, and PCC of 0.9493 vs. 0.8948, while effectively handling spatially varying aberrations across fields of view exceeding 1 mm². Furthermore, GRAPHYCS enables aberration correction in dynamic biological samples, successfully performing simultaneous wavefront sensing and neuronal activity detection in live larval zebrafish brain imaging where conventional phase-diversity methods fail. Overall, GRAPHYCS enables high-resolution imaging across extended regions and in dynamic biological samples without additional hardware complexity, providing a practical solution for aberration correction.

 

Datasets for paper titled " Graph-based modeling of optical system enables adaptive optics on dynamic samples with self-calibration"



Synthetic wide-field microscopy data

File: Figure2_Simulation.zip



Diversity_Images_Ideal.tif

Diversity_Images_NonIdeal.tif

GT_Object_Image.tif

appliedCoeff.txt: applied Zernike coefficients used to generate a set of diversity images






Experimental data: System and sample-induced aberrations (wide-field imaging)

File: Figure3_Experimental_widefield.zip



Diversity_Images_SystemAberration_Lymph.tif 

Diversity_Images_SampleAberration_Pancreas.tif  

appliedCoeff.txt: applied Zernike coefficients used to generate a set of diversity images






Experimental data: Spatially varying aberration over large field of view (wide-field imaging)

File: Figure4_SpatiallyVarying_widefield.zip



Diversity_Images_SampleAberration_Pancreas_LargeFoV.tif 

appliedCoeff.txt: applied Zernike coefficients used to generate a set of diversity images






Experimental data: Non-uniform illumination profile for wide-field imaging data

File: IlluminationProfile.zip



Illumination_Profile_centeralFoV.tif 

Illumination_Profile_LargeFoV.tif






Experimental data: Sample-induced aberration (light-sheet imaging)

File: Figure5_Experimental_lightsheet.zip



Diversity_Images_SampleAberration_Zebrafish.tif 

appliedCoeff.txt: applied Zernike coefficients used to generate a set of diversity images



Experimental data: Spatially varying aberration over large field of view (light-sheet imaging)

File: Figure5_SpatiallyVarying_lightsheet.zip



Diversity_Images_SampleAberration_Zebrafish_LargeFoV.tif 

appliedCoeff.txt: applied Zernike coefficients used to generate a set of diversity images