High resolution photoacoustic vascular image reconstruction through the fast residual dense generative adversarial network.

Yameng Zhang, Hua Tian, Min Wan, Shihao Tang, Ziyun Ding, Wei Huang, Yamin Yang, Weitao Li

Abstract read

In one paragraph

Article in Photoacoustics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

In Vivo Monitoring of Pressure Ulcer Model Development and Recoverability Under Multimodal Parameters Within a Unified Field of View.Journal of biophotonics · 2026
Article

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yameng ZhangSchool of Computer Engineering, Nanjing Institute of Technology, Nanjing, Jiangsu 211167, China.

Hua TianSchool of Computer Engineering, Nanjing Institute of Technology, Nanjing, Jiangsu 211167, China.

Min WanDepartment of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, China.

Shihao TangDepartment of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, China.

Ziyun DingSchool of Engineering, University of Birmingham, Birmingham B15 2TT, UK.

Wei HuangSchool of Computer Engineering, Nanjing Institute of Technology, Nanjing, Jiangsu 211167, China.

Yamin YangDepartment of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, China.

Weitao LiDepartment of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Photoacoustic imaging is a powerful technique that provides high-resolution, deep tissue imaging. However, the time-intensive nature of photoacoustic microscopy (PAM) poses a significant challenge, especially when high-resolution images are required for real-time applications. In this study, we proposed an optimized Fast Residual Dense Generative Adversarial Network (FRDGAN) for high-quality PAM reconstruction. Through dataset validation on mouse ear vasculature, FRDGAN demonstrated superior performance in image quality, background noise suppression, and computational efficiency across multiple down-sampling scales (×4, ×8) compared to classical methods. Furthermore, in the in vivo experiments of mouse cerebral vasculature, FRDGAN achieves the improvement of 2.24 dB and 0.0255 in peak signal-to-noise ratio and structural similarity metrics in contrast to SRGAN, respectively. Our FRDGAN method provides a promising solution for fast, high-quality PAM microvascular imaging in biomedical research.

Indexed as

Generative adversarial networkPhotoacoustic microscopyResidual dense moduleSuper-resolution imagingVascular imaging

Identifiers

PMID40241881

DOI10.1016/j.pacs.2025.100720

PMCPMC12000740

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.