ArticleBiomedical optics express2021
Denoising of pre-beamformed photoacoustic data using generative adversarial networks.
Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed.
- Unveiling precision: a data-driven approach to enhance photoacoustic imaging with sparse data.Biomedical optics express · 2024Article
- Adaptive enhancement of acoustic resolution photoacoustic microscopy imagingPhotoacoustics · 2023Article
- Deep learning methods hold promise for light fluence compensation in three-dimensional optoacoustic imaging.Journal of biomedical optics · 2022Article
- Deep learning-based classification of the anterior chamber angle in glaucoma gonioscopy.Biomedical optics express · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We have trained generative adversarial networks (GANs) to mimic both the effect of temporal averaging and of singular value decomposition (SVD) denoising. This effectively removes noise and acquisition artifacts and improves signal-to-noise ratio (SNR) in both the radio-frequency (RF) data and in the corresponding photoacoustic reconstructions. The method allows a single frame acquisition instead of averaging multiple frames, reducing scan time and total laser dose significantly. We have tested this method on experimental data, and quantified the improvement over using either SVD denoising or frame averaging individually for both the RF data and the reconstructed images. We achieve a mean squared error (MSE) of 0.05%, structural similarity index measure (SSIM) of 0.78, and a feature similarity index measure (FSIM) of 0.85 compared to our ground-truth RF results. In the subsequent reconstructions using the denoised data we achieve a MSE of 0.05%, SSIM of 0.80, and a FSIM of 0.80 compared to our ground-truth reconstructions.
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Registered trials
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