ArticleBiomedical optics express2022
Depth-extended acoustic-resolution photoacoustic microscopy based on a two-stage deep learning network.
Article in Biomedical optics express, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Advances in photoacoustic imaging reconstruction and quantitative analysis for biomedical applications.Visual computing for industry, biomedicine, and art · 2026Review
- A comprehensive review of high-performance photoacoustic microscopy systems.Photoacoustics · 2025Review
- Heterogeneous tumor blood oxygenation dynamics during phototherapy deciphered with real-time label-free photoacoustic imaging.NPJ acoustics · 2025Article
- An ultrasensitive and broadband transparent ultrasound transducer for ultrasound and photoacoustic imaging in-vivo.Nature communications · 2024Article
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Authors and funding
6 authors.
Funding
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Abstract
Acoustic resolution photoacoustic microscopy (AR-PAM) is a major modality of photoacoustic imaging. It can non-invasively provide high-resolution morphological and functional information about biological tissues. However, the image quality of AR-PAM degrades rapidly when the targets move far away from the focus. Although some works have been conducted to extend the high-resolution imaging depth of AR-PAM, most of them have a small focal point requirement, which is generally not satisfied in a regular AR-PAM system. Therefore, we propose a two-stage deep learning (DL) reconstruction strategy for AR-PAM to recover high-resolution photoacoustic images at different out-of-focus depths adaptively. The residual U-Net with attention gate was developed to implement the image reconstruction. We carried out phantom and
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Registered trials
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