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Related Experiment Video

Updated: May 8, 2026

Integrated Photoacoustic, Ultrasound, and Angiographic Tomography (PAUSAT) for NonInvasive Whole-Brain Imaging of Ischemic Stroke

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A generative adversarial network with multi-scale structural features for sparse-view photoacoustic tomography

Jialin Li^1,2, Xudong Luo³, Yiming Ma^1,2,3

¹Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.

|May 7, 2026

Summary

This study introduces a fast 3D photoacoustic tomography system using a generative adversarial network to reduce artifacts and improve image quality from sparse data. The method enhances biomedical imaging speed and detail recovery.

Keywords:

Generative adversarial network Photoacoustic tomography Sparse-view imaging

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Area of Science:

Biomedical Imaging
Medical Physics
Artificial Intelligence in Medicine

Background:

Photoacoustic tomography (PAT) offers high optical contrast and deep ultrasonic penetration for biomedical imaging.
High-resolution PAT requires extensive data acquisition, limiting imaging speed.
Sparse sampling accelerates PAT but introduces artifacts and reduces image quality.

Purpose of the Study:

To develop a high-speed, high-quality 3D photoacoustic tomography system using sparse-view sampling.
To improve artifact suppression and detail recovery in sparse-view PAT.
To enable practical clinical applications of photoacoustic tomography.

Main Methods:

Constructed a sparse-view photoacoustic tomography system.
Proposed a generative adversarial network (GAN) with multiscale structural features (MSF-GAN).
Incorporated pyramid squeeze attention and channel attention modules for enhanced feature extraction and structural consistency.
Utilized dual gradient regularized adversarial loss for improved stability.

Main Results:

The proposed system demonstrated superior artifact suppression and detail recovery compared to full-view reconstruction.
MSF-GAN achieved significant improvements in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) under sparse sampling (128 and 64 views).
Outperformed baseline models like diffusion models and MambaIR in image quality metrics.

Conclusions:

The developed MSF-GAN effectively leverages photoacoustic structural information for high-fidelity, high-contrast imaging.
The system achieves a desirable balance between imaging speed and reconstruction quality.
This work presents a promising approach for the clinical translation of photoacoustic tomography.