ArticleIEEE transactions on ultrasonics, ferroelectrics, and frequency control2020
A Deep Learning Approach to Photoacoustic Wavefront Localization in Deep-Tissue Medium.
Article in IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Deep Learning to Localize Photoacoustic Sources in Three Dimensions: Theory and Implementation.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2025Article
- Deep learningJournal of biomedical optics · 2024Article
- A Convolutional Neural Network for Beamforming and Image Reconstruction in Passive Cavitation Imaging.Sensors (Basel, Switzerland) · 2023Article
- Photoacoustic imaging for microcirculation.Microcirculation (New York, N.Y. : 1994) · 2022Review
- Modeling combined ultrasound and photoacoustic imaging: Simulations aiding device development and artificial intelligence.Photoacoustics · 2021Article
- Improving Minimum Variance Beamforming with Sub-Aperture Processing for Photoacoustic Imaging.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2021Article
- A High Sensitivity Transparent Ultrasound Transducer based on PMN-PT for Ultrasound and Photoacoustic Imaging.IEEE sensors letters · 2021Article
- Comparing Deep Learning Frameworks for Photoacoustic Tomography Image Reconstruction.Photoacoustics · 2021Article
- Photoacoustic imaging for monitoring of stroke diseases: A review.Photoacoustics · 2021Review
- Investigation of photoacoustic tomography reconstruction with a limited view from linear array.Journal of biomedical optics · 2021Article
- Simultaneous Denoising and Localization Network for Photoacoustic Target Localization.IEEE transactions on medical imaging · 2021Article
- Deep Learning in Biomedical Optics.Lasers in surgery and medicine · 2021Review
- Subaperture Processing-Based Adaptive Beamforming for Photoacoustic Imaging.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2021Article
- Deep learning for biomedical photoacoustic imaging: A review.Photoacoustics · 2021Review
- Deep learning in photoacoustic imaging: a review.Journal of biomedical optics · 2021Review
- Photoacoustic-guided surgery from head to toe [Invited].Biomedical optics express · 2021Review
- Enhancement of in vivo cardiac photoacoustic signal specificity using spatiotemporal singular value decomposition.Journal of biomedical optics · 2021Article
- Spatiotemporal Coherence Weighting for In Vivo Cardiac Photoacoustic Image Beamformation.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2021Article
- Reconstructing Undersampled Photoacoustic Microscopy Images Using Deep Learning.IEEE transactions on medical imaging · 2021Article
- Photoacoustic Imaging of Human Vasculature Using LED versus Laser Illumination: A Comparison Study on Tissue Phantoms and In Vivo Humans.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
7 authors.
Funding
Abstract
Optical photons undergo strong scattering when propagating beyond 1-mm deep inside biological tissue. Finding the origin of these diffused optical wavefronts is a challenging task. Breaking through the optical diffusion limit, photoacoustic (PA) imaging (PAI) provides high-resolution and label-free images of human vasculature with high contrast due to the optical absorption of hemoglobin. In real-time PAI, an ultrasound transducer array detects PA signals, and B-mode images are formed by delay-and-sum or frequency-domain beamforming. Fundamentally, the strength of a PA signal is proportional to the local optical fluence, which decreases with the increasing depth due to depth-dependent optical attenuation. This limits the visibility of deep-tissue vasculature or other light-absorbing PA targets. To address this practical challenge, an encoder-decoder convolutional neural network architecture was constructed with custom modules and trained to identify the origin of the PA wavefronts inside an optically scattering deep-tissue medium. A comprehensive ablation study provides strong evidence that each module improves the localization accuracy. The network was trained on model-based simulated PA signals produced by 16 240 blood-vessel targets subjected to both optical scattering and Gaussian noise. Test results on 4600 simulated and five experimental PA signals collected under various scattering conditions show that the network can localize the targets with a mean error less than 30 microns (standard deviation 20.9 microns) for targets below 40-mm imaging depth and 1.06 mm (standard deviation 2.68 mm) for targets at a depth between 40 and 60 mm. The proposed work has broad applications such as diffused optical wavefront shaping, circulating melanoma cell detection, and real-time vascular surgeries (e.g., deep-vein thrombosis).
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