ArticleScientific reports2023
Enhanced resolution and sensitivity acoustic-resolution photoacoustic microscopy with semi/unsupervised GANs.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 16 citations in OpenAlex.
- Advances in photoacoustic imaging reconstruction and quantitative analysis for biomedical applications.Visual computing for industry, biomedicine, and art · 2026Review
- Spectral-distortion-suppressed deep learning for fiber sensor photoacoustic microscopy.Photoacoustics · 2025Article
- GPU-accelerated volumetric-mosaic optical-resolution photoacoustic microscopy and quantifying tumor vasculature growth.Photoacoustics · 2025Article
- A comprehensive review of high-performance photoacoustic microscopy systems.Photoacoustics · 2025Review
- Synthetic polarization-sensitive optical coherence tomography using contrastive unpaired translation.Scientific reports · 2024Article
- Resolution Enhancement Strategies in Photoacoustic Microscopy: A Comprehensive Review.Micromachines · 2024Review
- Correction of high-rate motion for photoacoustic microscopy by orthogonal cross-correlation.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Acoustic-resolution photoacoustic microscopy (AR-PAM) enables visualization of biological tissues at depths of several millimeters with superior optical absorption contrast. However, the lateral resolution and sensitivity of AR-PAM are generally lower than those of optical-resolution PAM (OR-PAM) owing to the intrinsic physical acoustic focusing mechanism. Here, we demonstrate a computational strategy with two generative adversarial networks (GANs) to perform semi/unsupervised reconstruction with high resolution and sensitivity in AR-PAM by maintaining its imaging capability at enhanced depths. The b-scan PAM images were prepared as paired (for semi-supervised conditional GAN) and unpaired (for unsupervised CycleGAN) groups for label-free reconstructed AR-PAM b-scan image generation and training. The semi/unsupervised GANs successfully improved resolution and sensitivity in a phantom and in vivo mouse ear test with ground truth. We also confirmed that GANs could enhance resolution and sensitivity of deep tissues without the ground truth.
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Registered trials
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