ArticleBiomedical optics express2019
PA-Fuse: deep supervised approach for the fusion of photoacoustic images with distinct reconstruction characteristics.
Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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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
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Who cites it
12 citing papers in PubMed, 27 citations in OpenAlex.
- Multiscale local sparsity and prior learning algorithm for Cherenkov-excited luminescence scanned tomography reconstruction.Applied optics · 2025Article
- Article
- Noise insensitive volumetric fusion method for enhanced photoacoustic microscopy.Journal of biomedical optics · 2023Article
- Photoacoustic imaging with limited sampling: a review of machine learning approaches.Biomedical optics express · 2023Review
- Photoacoustic imaging aided with deep learning: a review.Biomedical engineering letters · 2022Review
- Advanced Ultrasound and Photoacoustic Imaging in Cardiology.Sensors (Basel, Switzerland) · 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
- Review of deep learning for photoacoustic imaging.Photoacoustics · 2021Review
- Dimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data.Biomedical optics express · 2021Article
- Deep learning approach to improve tangential resolution in photoacoustic tomography.Biomedical optics express · 2020Article
- Deep-Learning Image Reconstruction for Real-Time Photoacoustic System.IEEE transactions on medical imaging · 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
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The methods available for solving the inverse problem of photoacoustic tomography promote only one feature-either being smooth or sharp-in the resultant image. The fusion of photoacoustic images reconstructed from distinct methods improves the individually reconstructed images, with the guided filter based approach being state-of-the-art, which requires that implicit regularization parameters are chosen. In this work, a deep fusion method based on convolutional neural networks has been proposed as an alternative to the guided filter based approach. It has the combined benefit of using less data for training without the need for the careful choice of any parameters and is a fully data-driven approach. The proposed deep fusion approach outperformed the contemporary fusion method, which was proved using experimental, numerical phantoms and
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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.