ArticlePhotoacoustics2025
Enhanced clinical photoacoustic vascular imaging through a skin localization network and adaptive weighting.
Article in Photoacoustics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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.
Who cites it
6 citing papers in PubMed.
- Nanomaterials for In Vivo Photoacoustic Sensing: Emerging Strategies and Future Outlook.ACS sensors · 2026Review
- FM-Adapt: Foundation model adaptation with photoacoustic-supervised learning for interventional ultrasound.Photoacoustics · 2026Article
- Photoacoustic Imaging for Women's Gynecological Health: Advances and Clinical Prospects.Bioengineering (Basel, Switzerland) · 2026Review
- Radiomics-driven perfusion prediction in clinical photoacoustic foot imaging.Photoacoustics · 2025Article
- OneTouch Automated Photoacoustic and Ultrasound Imaging of Breast in Standing Pose.IEEE transactions on medical imaging · 2025Article
- High-resolution three-dimensional quantitative photoacoustic and ultrasound imaging of subcutaneous microvasculature in extremities.Biomedical optics express · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
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.
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Registered trials
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.