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Main Authors: Chandler, Talon, Ivanov, Ivan E., Sturm, Gabriel, Xiao, Sheng, Zhao, Xiang, Hillsley, Alexander, Ryan, Allyson Quinn, Liu, Ziwen, Varra, Sricharan Reddy, Theodoro, Ilan, Hirata-Miyasaki, Eduardo, Sundarraman, Deepika, Verma, Amitabh, Sekhar, Madhurya, Liu, Chad, Pradeep, Soorya, Lee, See-Chi, Rhoads, Shannon N., Zanellati, Maria Clara, Cohen, Sarah, Arias, Carolina, Leonetti, Manuel D., Jacobo, Adrian, Balla, Keir, Royer, Loïc A., Mehta, Shalin B.
Format: Preprint
Published: 2024
Subjects:
Optics
Computer Vision and Pattern Recognition
Quantitative Methods
Online Access:https://arxiv.org/abs/2412.09775
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Table of Contents:
  • Correlative computational microscopy can accelerate imaging and modeling of cellular dynamics by relaxing trade-offs inherent to dynamic imaging. Existing computational microscopy frameworks are either specialized or overly generic, limiting use to fixed configurations or domain experts. We introduce WaveOrder, a generalist wave-optical framework for imaging the architectural order of biomolecules. WaveOrder reconstructs diverse specimen properties from multi-channel acquisitions, with or without fluorescence. It provides a unified representation of linear optical properties and differentiable physics-based image formation models spanning widefield, confocal, light-sheet, and oblique label-free geometries. WaveOrder uses physics-informed ML to auto-tune model parameters and solve blind shift-variant restoration problems. This open-source, PyTorch-based framework enables scalable quantitative imaging across scales from organelles to adult zebrafish, and improves restoration of cellular structures in high-throughput experiments. We validate WaveOrder on diverse imaging applications, demonstrating its ability to recover biomolecular structure beyond the limits of existing approaches.

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