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Updated: May 21, 2025

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

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Published on: April 28, 2022

Near-zero photon bioimaging by fusing deep learning and ultralow-light microscopy.

Lucas Sheneman¹, Sulaimon Balogun², Jill L Johnson³

¹Institute for Interdisciplinary Data Sciences, University of Idaho, Moscow, ID 83844-3051.

Proceedings of the National Academy of Sciences of the United States of America

|May 19, 2025

Summary

This summary is machine-generated.

Researchers developed near-zero photon bioimaging, a method using AI and specialized microscopy to reconstruct images from extremely low light. This enhances optical microscopy reliability and imaging speed, overcoming photon sparsity limitations.

Keywords:

imaging microscopy single-photon

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Area of Science:

Biotechnology
Optical Microscopy
Artificial Intelligence (AI)

Background:

Reducing specimen irradiance in optical microscopy is crucial for reliability and reproducibility.
Low irradiance increases photon sparsity (Poisson noise), limiting image quality.
Current methods to overcome sparsity require long acquisition times, reducing imaging rates.

Purpose of the Study:

To introduce a novel bioimaging method operating at significantly reduced irradiance and high speeds.
To overcome the limitations of photon sparsity in biological imaging.
To enable high-fidelity imaging with minimal light exposure.

Main Methods:

Developed a near-zero photon bioimaging technique combining a specialized epifluorescence microscope with ultralow background capabilities.
Utilized artificial intelligence (AI) algorithms to reconstruct biological images from extremely low photon counts (as low as 0.01 photons per pixel).
Operated the system at kilohertz (kHz) rates, achieving 10,000-fold lower irradiance than standard microscopy.

Main Results:

Demonstrated high-fidelity reconstruction of multicellular and subcellular structures from near-zero photons per pixel.
Achieved kHz imaging rates with significantly reduced specimen irradiance.
Showcased the method's ability to capture features represented by minimal photon counts.

Conclusions:

Near-zero photon bioimaging offers a paradigm shift for enhancing optical microscopy reliability and speed.
The AI-driven approach effectively reconstructs images from sparse photon data.
This technology has potential applications beyond microscopy, including remote sensing and biomedical imaging.