ArticlePhotoacoustics2021
Compensating for visibility artefacts in photoacoustic imaging with a deep learning approach providing prediction uncertainties.
Article in Photoacoustics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Sinogram to image: direct reconstruction of photoacoustic tomography image using a hybrid deep learning approach.Biomedical optics express · 2025Article
- Joint segmentation and image reconstruction with error prediction in photoacoustic imaging using deep learning.Photoacoustics · 2024Article
- Unsupervised denoising of photoacoustic images based on the Noise2Noise network.Biomedical optics express · 2024Article
- U-Net enhanced real-time LED-based photoacoustic imaging.Journal of biophotonics · 2024Article
- Photoacoustic imaging on its way toward clinical utility: a tutorial review focusing on practical application in medicine.Journal of biomedical optics · 2023Review
- Niche preclinical and clinical applications of photoacoustic imaging with endogenous contrast.Photoacoustics · 2023Review
- Performance evaluation of mesoscopic photoacoustic imaging.Photoacoustics · 2023Article
- Evaluation of Cholesterol Thickness of Blood Vessels Using Photoacoustic Technology.BioMed research international · 2023Article
- Investigating the feasibility of a hand-held photoacoustic imaging probe for margin assessment during breast conserving surgery.Photoacoustics · 2022Article
- High frame rate (∼3 Hz) circular photoacoustic tomography using single-element ultrasound transducer aided with deep learning.Journal of biomedical optics · 2022Article
- Deep-Learning-Based Algorithm for the Removal of Electromagnetic Interference Noise in Photoacoustic Endoscopic Image Processing.Sensors (Basel, Switzerland) · 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
- Sound out the impaired perfusion: Photoacoustic imaging in preclinical ischemic stroke.Frontiers in neuroscience · 2022Review
- 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
- A Survey on Distributed Fibre Optic Sensor Data Modelling Techniques and Machine Learning Algorithms for Multiphase Fluid Flow Estimation.Sensors (Basel, Switzerland) · 2021Review
- Deep learning in photoacoustic imaging: a review.Journal of biomedical optics · 2021Review
- Review of deep learning for photoacoustic imaging.Photoacoustics · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Conventional photoacoustic imaging may suffer from the limited view and bandwidth of ultrasound transducers. A deep learning approach is proposed to handle these problems and is demonstrated both in simulations and in experiments on a multi-scale model of leaf skeleton. We employed an experimental approach to build the training and the test sets using photographs of the samples as ground truth images. Reconstructions produced by the neural network show a greatly improved image quality as compared to conventional approaches. In addition, this work aimed at quantifying the reliability of the neural network predictions. To achieve this, the dropout Monte-Carlo procedure is applied to estimate a pixel-wise degree of confidence on each predicted picture. Last, we address the possibility to use transfer learning with simulated data in order to drastically limit the size of the experimental dataset.
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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.