ReviewJournal of biomedical optics2021
Deep learning in photoacoustic imaging: a review.
Review in Journal of biomedical optics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 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.
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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.
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Who cites it
51 citing papers in PubMed.
- Acquisition-inspired motion artifact modeling and degradation-consistent diffusion restoration in photoacoustic microscopy.Photoacoustics · 2026Article
- Real-time deep learning-based vascular recognition in clinical breast photoacoustic imaging.Photoacoustics · 2026Article
- Review
- Deep learning-driven quantitative spectroscopic photoacoustic imaging for segmentation and oxygen saturation estimation.Ultrasonics · 2026Article
- PAT-Mamba: Adaptive all-in-one image restoration for photoacoustic tomography via state space model.Photoacoustics · 2026Article
- Photoacoustic Imaging for Women's Gynecological Health: Advances and Clinical Prospects.Bioengineering (Basel, Switzerland) · 2026Review
- Deep-learning-Assisted Photoacoustic and Ultrasound Evaluation for Pre-transplant Human Liver Graft Quality and Transplant Suitability.medRxiv : the preprint server for health sciences · 2026Article
- Efficient denoising in LED-based optoacoustic tomography with squeeze-and-excitation deep convolutional networks.Journal of biomedical optics · 2026Article
- Attention-driven complementary information fusion network for sparse photoacoustic image reconstruction.Photoacoustics · 2026Article
- Advances in photoacoustic imaging reconstruction and quantitative analysis for biomedical applications.Visual computing for industry, biomedicine, and art · 2026Review
- Multispectral Laser-Scanning Photoacoustic Microscopy With SRS-Generated Wavelengths for Skin Chromophore Characterization.Journal of biophotonics · 2026Article
- Potential of photoacoustic imaging for assessing tumor perfusion in intraocular tumors and high-resolution choroidal microvascular imaging: from technical advances to clinical translation.Frontiers in oncology · 2026Review
- Recent advances in photoacoustic-ultrasound dual-modality fusion imaging for functional visualization of the superficial tumor microenvironment.Oncology reviews · 2026Review
- Cross-regional real-time visualization of systemic physiology and dynamics with 3D panoramic photoacoustic computed tomography (3D-PanoPACT).Nature communications · 2025Article
- Photoacoustic imaging combined with robotics: a review of current works.Biomedical optics express · 2025Review
- Artifacts in photoacoustic imaging: Origins and mitigations.Photoacoustics · 2025Article
- Structure and oxygen saturation recovery of sparse photoacoustic microscopy images by deep learning.Photoacoustics · 2025Article
- Limited-view photoacoustic imaging reconstruction via high-quality self-supervised neural representation.Photoacoustics · 2025Article
- Enhancing signal-to-noise ratio in real-time LED-based photoacoustic imaging: A comparative study of CNN-based deep learning architectures.Photoacoustics · 2025Article
- Three-dimensional diffractive acoustic tomography.Nature communications · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
significancePhotoacoustic (PA) imaging can provide structural, functional, and molecular information for preclinical and clinical studies. For PA imaging (PAI), non-ideal signal detection deteriorates image quality, and quantitative PAI (QPAI) remains challenging due to the unknown light fluence spectra in deep tissue. In recent years, deep learning (DL) has shown outstanding performance when implemented in PAI, with applications in image reconstruction, quantification, and understanding.
aimWe provide (i) a comprehensive overview of the DL techniques that have been applied in PAI, (ii) references for designing DL models for various PAI tasks, and (iii) a summary of the future challenges and opportunities. APPROACH: Papers published before November 2020 in the area of applying DL in PAI were reviewed. We categorized them into three types: image understanding, reconstruction of the initial pressure distribution, and QPAI.
resultsWhen applied in PAI, DL can effectively process images, improve reconstruction quality, fuse information, and assist quantitative analysis.
conclusionDL has become a powerful tool in PAI. With the development of DL theory and technology, it will continue to boost the performance and facilitate the clinical translation of PAI.
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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.