ArticleBiomedical optics express2020
Deep learning approach to improve tangential resolution in photoacoustic tomography.
Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Advances in photoacoustic imaging reconstruction and quantitative analysis for biomedical applications.Visual computing for industry, biomedicine, and art · 2026Review
- Sinogram to image: direct reconstruction of photoacoustic tomography image using a hybrid deep learning approach.Biomedical optics express · 2025Article
- Image restoration for ring-array photoacoustic tomography based on an attention mechanism driven conditional generative adversarial network.Photoacoustics · 2025Article
- PA-NeRF, a neural radiance field model for 3D photoacoustic tomography reconstruction from limited Bscan data.Biomedical optics express · 2024Article
- Deep learning on photoacoustic tomography to remove image distortion due to inaccurate measurement of the scanning radius.Biomedical optics express · 2023Article
- Review of imaging test phantoms.Journal of biomedical optics · 2023Review
- Co-optimization method to improve lateral resolution in photoacoustic computed tomography.Biomedical optics express · 2022Article
- Looking deep inside tissue with photoacoustic molecular probes: a review.Journal of biomedical optics · 2022Review
- High frame rate (∼3 Hz) circular photoacoustic tomography using single-element ultrasound transducer aided with deep learning.Journal of biomedical optics · 2022Article
- Photoacoustic imaging aided with deep learning: a review.Biomedical engineering letters · 2022Review
- Deep-E: A Fully-Dense Neural Network for Improving the Elevation Resolution in Linear-Array-Based Photoacoustic Tomography.IEEE transactions on medical imaging · 2022Article
- Translational Photoacoustic Imaging for Disease Diagnosis, Monitoring, and Surgical Guidance: introduction to the feature issue.Biomedical optics express · 2021Article
- Deep learning for biomedical photoacoustic imaging: A review.Photoacoustics · 2021Review
- Sounding out the hidden data: A concise review of deep learning in photoacoustic imaging.Experimental biology and medicine (Maywood, N.J.) · 2021Review
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In circular scan photoacoustic tomography (PAT), the axial resolution is spatially invariant and is limited by the bandwidth of the detector. However, the tangential resolution is spatially variant and is dependent on the aperture size of the detector. In particular, the tangential resolution improves with the decreasing aperture size. However, using a detector with a smaller aperture reduces the sensitivity of the transducer. Thus, large aperture size detectors are widely preferred in circular scan PAT imaging systems. Although several techniques have been proposed to improve the tangential resolution, they have inherent limitations such as high cost and the need for customized detectors. Herein, we propose a novel deep learning architecture to counter the spatially variant tangential resolution in circular scanning PAT imaging systems. We used a fully dense U-Net based convolutional neural network architecture along with 9 residual blocks to improve the tangential resolution of the PAT images. The network was trained on the simulated datasets and its performance was verified by experimental
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