ArticleVisual computing for industry, biomedicine, and art2019
Deep-learning-based motion-correction algorithm in optical resolution photoacoustic microscopy.
Article in Visual computing for industry, biomedicine, and art, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 42 citations in OpenAlex.
- Acquisition-inspired motion artifact modeling and degradation-consistent diffusion restoration in photoacoustic microscopy.Photoacoustics · 2026Article
- Spectral-distortion-suppressed deep learning for fiber sensor photoacoustic microscopy.Photoacoustics · 2025Article
- TowardsFundamental research · 2024Review
- Unsupervised deep learning enables real-time image registration of fast-scanning optical-resolution photoacoustic microscopy.Photoacoustics · 2024Article
- Implicit neural representations in light microscopy.Biomedical optics express · 2024Article
- Niche preclinical and clinical applications of photoacoustic imaging with endogenous contrast.Photoacoustics · 2023Review
- Deep Learning-Powered Bessel-Beam Multiparametric Photoacoustic Microscopy.IEEE transactions on medical imaging · 2022Article
- Photoacoustic imaging for microcirculation.Microcirculation (New York, N.Y. : 1994) · 2022Review
- 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
- Suppression of motion artifacts in intravascular photoacoustic image sequences.Biomedical optics express · 2021Article
- Low-consumption photoacoustic method to measure liquid viscosity.Biomedical optics express · 2021Article
- Deep Learning in Biomedical Optics.Lasers in surgery and medicine · 2021Review
- Sounding out the hidden data: A concise review of deep learning in photoacoustic imaging.Experimental biology and medicine (Maywood, N.J.) · 2021Review
- Deep learning for biomedical photoacoustic imaging: A review.Photoacoustics · 2021Review
- [Progress of motion artifact correction in photoacoustic microscopy and photoacoustic tomography].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Article
- Deep learning in photoacoustic imaging: a review.Journal of biomedical optics · 2021Review
- Review of deep learning for photoacoustic imaging.Photoacoustics · 2021Review
- Reconstructing Undersampled Photoacoustic Microscopy Images Using Deep Learning.IEEE transactions on medical imaging · 2021Article
- Convolutional neural network for resolution enhancement and noise reduction in acoustic resolution photoacoustic microscopy.Biomedical optics express · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
In this study, we propose a deep-learning-based method to correct motion artifacts in optical resolution photoacoustic microscopy (OR-PAM). The method is a convolutional neural network that establishes an end-to-end map from input raw data with motion artifacts to output corrected images. First, we performed simulation studies to evaluate the feasibility and effectiveness of the proposed method. Second, we employed this method to process images of rat brain vessels with multiple motion artifacts to evaluate its performance for in vivo applications. The results demonstrate that this method works well for both large blood vessels and capillary networks. In comparison with traditional methods, the proposed method in this study can be easily modified to satisfy different scenarios of motion corrections in OR-PAM by revising the training sets.
Indexed as
Identifiers
What OpenQuestion holds
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.