ArticlePhotoacoustics2022
High-fidelity deconvolution for acoustic-resolution photoacoustic microscopy enabled by convolutional neural networks.
Article in Photoacoustics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 15 citations in OpenAlex.
- Artificial Intelligence for Discovery in Life Sciences.Bioconjugate chemistry · 2026Review
- A comprehensive review of high-performance photoacoustic microscopy systems.Photoacoustics · 2025Review
- Sound Out the Deep Clarity: Super-Resolution Photoacoustic Imaging at Depths.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2024Review
- Virtual-point-based deconvolution for optical-resolution photoacoustic microscopy.Journal of biophotonics · 2024Article
- Photoacoustic imaging plus X: a review.Journal of biomedical optics · 2024Review
- Adaptive enhancement of acoustic resolution photoacoustic microscopy imagingPhotoacoustics · 2023Article
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acoustic-resolution photoacoustic microscopy (AR-PAM) image resolution is determined by the point spread function (PSF) of the imaging system. Previous algorithms, including Richardson-Lucy (R-L) deconvolution and model-based (MB) deconvolution, improve spatial resolution by taking advantage of the PSF as prior knowledge. However, these methods encounter the problems of inaccurate deconvolution, meaning the deconvolved feature size and the original one are not consistent (e.g., the former can be smaller than the latter). We present a novel deep convolution neural network (CNN)-based algorithm featuring high-fidelity recovery of multiscale feature size to improve lateral resolution of AR-PAM. The CNN is trained with simulated image pairs of line patterns, which is to mimic blood vessels. To investigate the suitable CNN model structure and elaborate on the effectiveness of CNN methods compared with non-learning methods, we select five different CNN models, while R-L and directional MB methods are also applied for comparison. Besides simulated data, experimental data including tungsten wires, leaf veins, and
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