Enhanced clinical photoacoustic vascular imaging through a skin localization network and adaptive weighting
Photoacoustic tomography (PAT) is an emerging imaging modality with widespread applications in both preclinical and clinical studies. Despite its promising capabilities to provide high-resolution images, the visualization of vessels might be hampered by skin signals and attenuation in tissues. In th...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Photoacoustics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213597925000096 |
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| author | Chuqin Huang Emily Zheng Wenhan Zheng Huijuan Zhang Yanda Cheng Xiaoyu Zhang Varun Shijo Robert W. Bing Isabel Komornicki Linda M. Harris Ermelinda Bonaccio Kazuaki Takabe Emma Zhang Wenyao Xu Jun Xia |
| author_facet | Chuqin Huang Emily Zheng Wenhan Zheng Huijuan Zhang Yanda Cheng Xiaoyu Zhang Varun Shijo Robert W. Bing Isabel Komornicki Linda M. Harris Ermelinda Bonaccio Kazuaki Takabe Emma Zhang Wenyao Xu Jun Xia |
| author_sort | Chuqin Huang |
| collection | DOAJ |
| description | Photoacoustic tomography (PAT) is an emerging imaging modality with widespread applications in both preclinical and clinical studies. Despite its promising capabilities to provide high-resolution images, the visualization of vessels might be hampered by skin signals and attenuation in tissues. In this study, we have introduced a framework to retrieve deep vessels. It combines a deep learning network to segment skin layers and an adaptive weighting algorithm to compensate for attenuation. Evaluation of enhancement using vessel occupancy metrics and signal-to-noise ratio (SNR) demonstrates that the proposed method significantly recovers deep vessels across various body positions and skin tones. These findings indicate the method’s potential to enhance quantitative analysis in preclinical and clinical photoacoustic research. |
| format | Article |
| id | doaj-art-7fd4ddc8e73e4379b2857ea46555ac1f |
| institution | DOAJ |
| issn | 2213-5979 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Photoacoustics |
| spelling | doaj-art-7fd4ddc8e73e4379b2857ea46555ac1f2025-08-20T02:54:29ZengElsevierPhotoacoustics2213-59792025-04-014210069010.1016/j.pacs.2025.100690Enhanced clinical photoacoustic vascular imaging through a skin localization network and adaptive weightingChuqin Huang0Emily Zheng1Wenhan Zheng2Huijuan Zhang3Yanda Cheng4Xiaoyu Zhang5Varun Shijo6Robert W. Bing7Isabel Komornicki8Linda M. Harris9Ermelinda Bonaccio10Kazuaki Takabe11Emma Zhang12Wenyao Xu13Jun Xia14Department of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14228, United StatesDepartment of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14228, United StatesDepartment of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14228, United StatesDepartment of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14228, United StatesDepartment of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14228, United StatesDepartment of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14228, United StatesDepartment of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14228, United States; Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14228, United StatesDepartment of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14228, United StatesDepartment of Surgery, University at Buffalo, The State University of New York, Buffalo, NY 14228, United StatesDepartment of Surgery, University at Buffalo, The State University of New York, Buffalo, NY 14228, United StatesDepartment of Breast Imaging, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, United StatesDepartment of Surgery, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14263, United StatesDepartment of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14228, United StatesDepartment of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14228, United StatesDepartment of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14228, United States; Department of Computer Science and Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14228, United States; Corresponding author at: Department of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14228, United States.Photoacoustic tomography (PAT) is an emerging imaging modality with widespread applications in both preclinical and clinical studies. Despite its promising capabilities to provide high-resolution images, the visualization of vessels might be hampered by skin signals and attenuation in tissues. In this study, we have introduced a framework to retrieve deep vessels. It combines a deep learning network to segment skin layers and an adaptive weighting algorithm to compensate for attenuation. Evaluation of enhancement using vessel occupancy metrics and signal-to-noise ratio (SNR) demonstrates that the proposed method significantly recovers deep vessels across various body positions and skin tones. These findings indicate the method’s potential to enhance quantitative analysis in preclinical and clinical photoacoustic research.http://www.sciencedirect.com/science/article/pii/S2213597925000096PhotoacousticPhotoacoustic tomographyDeep learningImage enhancement |
| spellingShingle | Chuqin Huang Emily Zheng Wenhan Zheng Huijuan Zhang Yanda Cheng Xiaoyu Zhang Varun Shijo Robert W. Bing Isabel Komornicki Linda M. Harris Ermelinda Bonaccio Kazuaki Takabe Emma Zhang Wenyao Xu Jun Xia Enhanced clinical photoacoustic vascular imaging through a skin localization network and adaptive weighting Photoacoustics Photoacoustic Photoacoustic tomography Deep learning Image enhancement |
| title | Enhanced clinical photoacoustic vascular imaging through a skin localization network and adaptive weighting |
| title_full | Enhanced clinical photoacoustic vascular imaging through a skin localization network and adaptive weighting |
| title_fullStr | Enhanced clinical photoacoustic vascular imaging through a skin localization network and adaptive weighting |
| title_full_unstemmed | Enhanced clinical photoacoustic vascular imaging through a skin localization network and adaptive weighting |
| title_short | Enhanced clinical photoacoustic vascular imaging through a skin localization network and adaptive weighting |
| title_sort | enhanced clinical photoacoustic vascular imaging through a skin localization network and adaptive weighting |
| topic | Photoacoustic Photoacoustic tomography Deep learning Image enhancement |
| url | http://www.sciencedirect.com/science/article/pii/S2213597925000096 |
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