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

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Single-shot quantitative phase contrast imaging based on deep learning.

Yu-Chun Lin¹, Yuan Luo¹, Ying-Ju Chen¹

¹Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, No. 1, Sec. 1, Jen Ai Rd., Zhongzheng Dist., Taipei City 100, Taiwan.

Biomedical Optics Express

|July 27, 2023

Summary

This summary is machine-generated.

This study introduces a novel single-shot quantitative differential phase-contrast (DPC) imaging method using deep learning (DL) and color-encoded illumination. This technique enables real-time monitoring of living cells by generating phase images from single intensity measurements.

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Area of Science:

Biomedical Optics
Quantitative Phase Imaging
Deep Learning Applications

Background:

Quantitative differential phase-contrast (DPC) imaging is crucial for phase retrieval but requires multiple measurements, hindering real-time cell monitoring.
Existing methods face limitations in speed and complexity for live-cell imaging applications.

Purpose of the Study:

To develop a single-shot quantitative DPC imaging method.
To enable real-time monitoring of living cells using deep learning (DL) and color-encoded illumination.
To generate isotropic quantitative phase images from single intensity measurements.

Main Methods:

A deep learning model was trained to generate quantitative phase images from single-shot intensity measurements.
Color-encoded illumination with a radially asymmetric pattern was used as input.
A linear-gradient pupil with two-axis measurements was employed for ground-truth reconstruction.
The model was trained and validated on thirteen different cell lines.

Main Results:

DL-based phase images closely matched ground-truth images with a structural similarity index exceeding 0.98.
The phase difference between DL-based and ground-truth images was less than 13%.
The method demonstrated visual similarity between generated and true phase images.

Conclusions:

The study validates the feasibility of using deep learning for single-shot quantitative phase imaging.
This approach significantly advances real-time cell monitoring capabilities.
The developed method offers a faster and more efficient alternative to traditional DPC imaging.