ArticleIEEE transactions on medical imaging2020
Deep-Learning Image Reconstruction for Real-Time Photoacoustic System.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
51 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Image Quality Improvement Techniques and Assessment Adequacy in Clinical Optoacoustic Imaging: A Systematic Review.Biosensors · 2022Pooled it
- Dermoscopic Image Classification of Pigmented Nevus under Deep Learning and the Correlation with Pathological Features.Computational and mathematical methods in medicine · 2022Trial
- Real-time deep learning-based vascular recognition in clinical breast photoacoustic imaging.Photoacoustics · 2026Article
- A Model-Based Joint Feature Fusion framework for limited-view photoacoustic tomography reconstruction.Photoacoustics · 2026Article
- 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
- Dual SwinUNet architecture for enhanced photoacoustic imaging: a contrastive learning approach in image and sinogram domains.Journal of biomedical optics · 2026Article
- Progressive dual-branch adversarial diffusion for sparse-view photoacoustic tomography reconstruction.Photoacoustics · 2026Article
- Complementary fluorescence and photoacoustic imaging of tozuleristide in muscle tissue in vitro- toward optically-guided solid tumor surgery: feasibility studies.Scientific reports · 2026Article
- Photoacoustic Imaging for Women's Gynecological Health: Advances and Clinical Prospects.Bioengineering (Basel, Switzerland) · 2026Review
- Circular-scanning variance delay and sum algorithm for photoacoustic imaging.Biomedical optics express · 2026Article
- Artifacts in photoacoustic imaging: Origins and mitigations.Photoacoustics · 2025Article
- Generative priors-constraint accelerated iterative reconstruction for extremely sparse photoacoustic tomography boosted by mean-reverting diffusion model: Towards 8 projections.Photoacoustics · 2025Article
- Full-view volumetric photoacoustic imaging using a hemispheric transducer array combined with an acoustic reflector.Biomedical optics express · 2024Article
- Joint segmentation and image reconstruction with error prediction in photoacoustic imaging using deep learning.Photoacoustics · 2024Article
- Unsupervised disentanglement strategy for mitigating artifact in photoacoustic tomography under extremely sparse view.Photoacoustics · 2024Article
- Compensating unknown speed of sound in learned fast 3D limited-view photoacoustic tomography.Photoacoustics · 2024Article
- Dual-modal Photoacoustic and Ultrasound Imaging: from preclinical to clinical applications.Frontiers in photonics · 2024Article
- Review of Deep Learning Approaches for Interleaved Photoacoustic and Ultrasound (PAUS) Imaging.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2023Article
- Sparse-view reconstruction for photoacoustic tomography combining diffusion model with model-based iteration.Photoacoustics · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Recent advances in photoacoustic (PA) imaging have enabled detailed images of microvascular structure and quantitative measurement of blood oxygenation or perfusion. Standard reconstruction methods for PA imaging are based on solving an inverse problem using appropriate signal and system models. For handheld scanners, however, the ill-posed conditions of limited detection view and bandwidth yield low image contrast and severe structure loss in most instances. In this paper, we propose a practical reconstruction method based on a deep convolutional neural network (CNN) to overcome those problems. It is designed for real-time clinical applications and trained by large-scale synthetic data mimicking typical microvessel networks. Experimental results using synthetic and real datasets confirm that the deep-learning approach provides superior reconstructions compared to conventional methods.
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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.