Graph-based modeling of optical system enables adaptive optics with self-calibration over large field of view

Fluorescence microscopy of biological structures is fundamentally limited by aberrations that degrade resolution and image quality. While adaptive optics techniques can compensate for these distortions, existing approaches either require complex hardware or rely on idealized optical models, leading to suboptimal correction in real-world systems. Here we introduce GRAPHYCS, a computational adaptive optics framework that bridges the gap between computational models and physical systems through differentiable graph-based modeling. GRAPHYCS uniquely integrates automatic self-calibration to account for system non-idealities while simultaneously estimating aberrations and object structure via backpropagation. Through simulations and experiments, we demonstrate that GRAPHYCS achieves improvements of 94.5% in wavefront estimation accuracy (wavefront RMS error) and 69.7% in image quality over existing methods, and also effectively handles spatially varying aberrations across fields of view exceeding 1 mm². This capability is critical for imaging heterogeneous biological specimens with non-uniform refractive index distributions. GRAPHYCS enables high-resolution imaging across extended sample regions without additional hardware complexity, providing a practical solution for wide-area aberration correction in fluorescence microscopy. Datasets for paper titled "Graph-based modeling of optical system enables adaptive optics with self-calibration over large field of view" 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 File: Figure3_Experimental.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 File: Figure4_SpatiallyVarying.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 File: IlluminationProfile.zip Illumination_Profile.tif