High resolution photoacoustic vascular image reconstruction through the fast residual dense generative adversarial network
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 prop...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | Photoacoustics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213597925000436 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850260377851396096 |
|---|---|
| author | Yameng Zhang Hua Tian Min Wan Shihao Tang Ziyun Ding Wei Huang Yamin Yang Weitao Li |
| author_facet | Yameng Zhang Hua Tian Min Wan Shihao Tang Ziyun Ding Wei Huang Yamin Yang Weitao Li |
| author_sort | Yameng Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cdf5360fdbc643e6aa563ef9530c590b |
| institution | OA Journals |
| issn | 2213-5979 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Photoacoustics |
| spelling | doaj-art-cdf5360fdbc643e6aa563ef9530c590b2025-08-20T01:55:38ZengElsevierPhotoacoustics2213-59792025-06-014310072010.1016/j.pacs.2025.100720High resolution photoacoustic vascular image reconstruction through the fast residual dense generative adversarial networkYameng Zhang0Hua Tian1Min Wan2Shihao Tang3Ziyun Ding4Wei Huang5Yamin Yang6Weitao Li7School of Computer Engineering, Nanjing Institute of Technology, Nanjing, Jiangsu 211167, China; Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, ChinaSchool of Computer Engineering, Nanjing Institute of Technology, Nanjing, Jiangsu 211167, ChinaDepartment of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, ChinaDepartment of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, ChinaSchool of Engineering, University of Birmingham, Birmingham B15 2TT, UKSchool of Computer Engineering, Nanjing Institute of Technology, Nanjing, Jiangsu 211167, ChinaDepartment of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, ChinaDepartment of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 211106, China; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2213597925000436Photoacoustic microscopyGenerative adversarial networkVascular imagingSuper-resolution imagingResidual dense module |
| spellingShingle | Yameng Zhang Hua Tian Min Wan Shihao Tang Ziyun Ding Wei Huang Yamin Yang Weitao Li High resolution photoacoustic vascular image reconstruction through the fast residual dense generative adversarial network Photoacoustics Photoacoustic microscopy Generative adversarial network Vascular imaging Super-resolution imaging Residual dense module |
| title | High resolution photoacoustic vascular image reconstruction through the fast residual dense generative adversarial network |
| title_full | High resolution photoacoustic vascular image reconstruction through the fast residual dense generative adversarial network |
| title_fullStr | High resolution photoacoustic vascular image reconstruction through the fast residual dense generative adversarial network |
| title_full_unstemmed | High resolution photoacoustic vascular image reconstruction through the fast residual dense generative adversarial network |
| title_short | High resolution photoacoustic vascular image reconstruction through the fast residual dense generative adversarial network |
| title_sort | high resolution photoacoustic vascular image reconstruction through the fast residual dense generative adversarial network |
| topic | Photoacoustic microscopy Generative adversarial network Vascular imaging Super-resolution imaging Residual dense module |
| url | http://www.sciencedirect.com/science/article/pii/S2213597925000436 |
| work_keys_str_mv | AT yamengzhang highresolutionphotoacousticvascularimagereconstructionthroughthefastresidualdensegenerativeadversarialnetwork AT huatian highresolutionphotoacousticvascularimagereconstructionthroughthefastresidualdensegenerativeadversarialnetwork AT minwan highresolutionphotoacousticvascularimagereconstructionthroughthefastresidualdensegenerativeadversarialnetwork AT shihaotang highresolutionphotoacousticvascularimagereconstructionthroughthefastresidualdensegenerativeadversarialnetwork AT ziyunding highresolutionphotoacousticvascularimagereconstructionthroughthefastresidualdensegenerativeadversarialnetwork AT weihuang highresolutionphotoacousticvascularimagereconstructionthroughthefastresidualdensegenerativeadversarialnetwork AT yaminyang highresolutionphotoacousticvascularimagereconstructionthroughthefastresidualdensegenerativeadversarialnetwork AT weitaoli highresolutionphotoacousticvascularimagereconstructionthroughthefastresidualdensegenerativeadversarialnetwork |