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Three-dimensional Optical-resolution Photoacoustic Microscopy

Three-dimensional Optical-resolution Photoacoustic Microscopy

Published on: May 3, 2011

Self-supervised light fluence correction network for photoacoustic tomography based on diffusion equation.

Zhaoyong Liang^1,2,3, Zongxin Mo^1,2,3, Shuangyang Zhang^1,2,3

¹School of Biomedical Engineering, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, China.

|February 3, 2025

Summary

This study introduces SQPA-Net, a self-supervised deep learning method for quantitative photoacoustic tomography (QPAT) that corrects light fluence without ground truth data. The novel approach improves accuracy and significantly reduces processing time for biological tissue imaging.

Keywords:

Absorption coefficient estimation Light fluence correction Quantitative photoacoustic tomography Self-supervised learning

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Area of Science:

Biomedical Imaging
Optical Imaging
Photoacoustics

Background:

Quantitative photoacoustic tomography (QPAT) is crucial for visualizing tissue properties.
Deep learning (DL) methods show potential for QPAT but require extensive ground truth data for training.
Estimating light fluence distribution is essential for accurate QPAT.

Purpose of the Study:

To develop a self-supervised deep learning method for light fluence correction in QPAT.
To enable accurate QPAT without requiring ground truth data.
To improve the efficiency and accuracy of QPAT imaging.

Main Methods:

Proposed a self-supervised deep learning network (SQPA-Net) for QPAT.
Integrated light fluence estimation based on the diffusion equation into the network's loss function.
Trained the network using only original photoacoustic (PA) images.

Main Results:

SQPA-Net effectively corrects light fluence distribution in QPAT.
The method demonstrated superior performance compared to traditional iterative and current DL-based approaches.
Significant reduction in image processing time was achieved.

Conclusions:

SQPA-Net offers an effective and efficient solution for light fluence correction in QPAT.
The self-supervised approach overcomes the limitation of requiring ground truth data.
This method advances the application of DL in quantitative photoacoustic imaging.