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Updated: Apr 18, 2026

Three-dimensional Optical-resolution Photoacoustic Microscopy

Three-dimensional Optical-resolution Photoacoustic Microscopy

Published on: May 3, 2011

Deep learning-driven quantitative spectroscopic photoacoustic imaging for segmentation and oxygen saturation

Ruibo Shang¹, Sidhartha Jandhyala², Yujia Wu²

¹Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA; uWAMIT Center, Department of Bioengineering, University of Washington, Seattle, WA 98195, USA.

|April 16, 2026

Summary

A new deep neural network, Hybrid-Net, accurately estimates blood oxygenation saturation (sO2) in vivo using spectroscopic photoacoustic imaging. This method bypasses the need for optical fluence estimation, improving accuracy in heterogeneous tissues.

Keywords:

Blood oxygenation Deep learning Segmentation Spectroscopic quantitative photoacoustic imaging

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Area of Science:

Biomedical Imaging
Optical Physics
Machine Learning

Background:

Spectroscopic photoacoustic (sPA) imaging offers noninvasive in vivo estimation of blood oxygenation saturation (sO2).
Accurate sO2 quantification relies on precise optical fluence estimation, which is challenging in heterogeneous tissues due to varying light absorption and scattering.
Robust modeling of light transport is crucial for reliable sPA imaging results.

Purpose of the Study:

To develop a deep neural network (Hybrid-Net) for simultaneous sO2 estimation and vessel segmentation in sPA imaging.
To improve the accuracy of sO2 measurements by minimizing errors within segmented blood vessels.
To enable accurate blood oxygenation estimation without requiring explicit optical fluence calculations.

Main Methods:

Developed Hybrid-Net, a deep neural network for sPA imaging.
Trained Hybrid-Net on simulated 3D Monte Carlo data of light transport in breast tissue at 700 nm and 850 nm.
Retrained and validated Hybrid-Net on experimental sPA data from tissue-mimicking phantoms with embedded blood pools.

Main Results:

Hybrid-Net achieved high segmentation accuracy (≥0.978 in simulations, 0.999 in experiments) across various noise levels.
Demonstrated low sO2 mean squared error (≤0.048 in simulations, 0.002 in experiments).
Successfully estimated sO2 in blood vessels without needing optical fluence estimates.

Conclusions:

Hybrid-Net provides accurate blood oxygenation saturation estimation in sPA imaging.
The developed method effectively segments blood vessels and quantifies sO2 within them.
This approach has the potential to significantly advance in vivo sO2 monitoring.