ArticleBiomedical optics express2020
Convolutional neural network for resolution enhancement and noise reduction in acoustic resolution photoacoustic microscopy.
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 32 papers.
What it found
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Who cites it
32 citing papers in PubMed, 63 citations in OpenAlex.
- Improved quantification of photoacoustic biomarkers through residual-based noise masking.Photoacoustics · 2026Article
- Physics-consistent denoising of full-field photoacoustic displacement maps using an adaptive residual attention network.Photoacoustics · 2026Article
- Photoacoustic microscopy reveals deep angiogenic responses in 3D bioprinted tumor-vessel models.Microsystems & nanoengineering · 2026Article
- Reduction of photobleaching effects in photoacoustic imaging using noise agnostic, platform-flexible deep-learning methods.Journal of biomedical optics · 2025Article
- High resolution photoacoustic vascular image reconstruction through the fast residual dense generative adversarial network.Photoacoustics · 2025Article
- UPAMNet: A unified network with deep knowledge priors for photoacoustic microscopy.Photoacoustics · 2024Article
- Accelerating photoacoustic microscopy by reconstructing undersampled images using diffusion models.Scientific reports · 2024Article
- 4D spectral-spatial computational photoacoustic dermoscopy.Photoacoustics · 2023Article
- Enhanced resolution and sensitivity acoustic-resolution photoacoustic microscopy with semi/unsupervised GANs.Scientific reports · 2023Article
- Applications of Deep Learning Algorithms to Ultrasound Imaging Analysis in Preclinical Studies on In Vivo Animals.Life (Basel, Switzerland) · 2023Review
- Restoring the imaging quality of circular transducer array-based PACT using synthetic aperture focusing technique integrated with 2nd-derivative-based back projection scheme.Photoacoustics · 2023Article
- Niche preclinical and clinical applications of photoacoustic imaging with endogenous contrast.Photoacoustics · 2023Review
- Adaptive enhancement of acoustic resolution photoacoustic microscopy imagingPhotoacoustics · 2023Article
- Deep Learning-Powered Bessel-Beam Multiparametric Photoacoustic Microscopy.IEEE transactions on medical imaging · 2022Article
- Deep learning methods hold promise for light fluence compensation in three-dimensional optoacoustic imaging.Journal of biomedical optics · 2022Article
- Depth-extended acoustic-resolution photoacoustic microscopy based on a two-stage deep learning network.Biomedical optics express · 2022Article
- High-fidelity deconvolution for acoustic-resolution photoacoustic microscopy enabled by convolutional neural networks.Photoacoustics · 2022Article
- High frame rate (∼3 Hz) circular photoacoustic tomography using single-element ultrasound transducer aided with deep learning.Journal of biomedical optics · 2022Article
- Visualizing tumor angiogenesis and boundary with polygon-scanning multiscale photoacoustic microscopy.Photoacoustics · 2022Article
- Achieving depth-independent lateral resolution in AR-PAM using the synthetic-aperture focusing technique.Photoacoustics · 2022Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In acoustic resolution photoacoustic microscopy (AR-PAM), a high numerical aperture focused ultrasound transducer (UST) is used for deep tissue high resolution photoacoustic imaging. There is a significant degradation of lateral resolution in the out-of-focus region. Improvement in out-of-focus resolution without degrading the image quality remains a challenge. In this work, we propose a deep learning-based method to improve the resolution of AR-PAM images, especially at the out of focus plane. A modified fully dense U-Net based architecture was trained on simulated AR-PAM images. Applying the trained model on experimental images showed that the variation in resolution is ∼10% across the entire imaging depth (∼4 mm) in the deep learning-based method, compared to ∼180% variation in the original PAM images. Performance of the trained network on
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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.