ArticleScientific reports2024
Accelerating photoacoustic microscopy by reconstructing undersampled images using diffusion models.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Acquisition-inspired motion artifact modeling and degradation-consistent diffusion restoration in photoacoustic microscopy.Photoacoustics · 2026Article
- A comparative study of diffusion-based reconstruction frameworks for photoacoustic tomography.Scientific reports · 2026Article
- Identity-consistent and high-fidelity audio-driven portrait animation with enhanced latent diffusion.Scientific reports · 2026Article
- Improved Sensitivity in Large Field of View Multispectral Laser-Scanning Photoacoustic Microscopy for Measuring Oxygen Saturation In Vivo.Journal of biophotonics · 2026Article
- A comprehensive review of high-performance photoacoustic microscopy systems.Photoacoustics · 2025Review
- Photoacoustic-Integrated Multimodal Approach for Colorectal Cancer Diagnosis.ACS biomaterials science & engineering · 2025Review
- Lightweight sparse optoacoustic image reconstruction via an attention-driven multi-scale wavelet network.Photoacoustics · 2025Article
- Structure and oxygen saturation recovery of sparse photoacoustic microscopy images by deep learning.Photoacoustics · 2025Article
- Hybrid transformer-CNN network-driven optical-scanning undersampling for photoacoustic remote sensing microscopy.Photoacoustics · 2025Article
- Resolution Enhancement Strategies in Photoacoustic Microscopy: A Comprehensive Review.Micromachines · 2024Review
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Authors and funding
2 authors.
Funding
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Abstract
Photoacoustic Microscopy (PAM) integrates optical and acoustic imaging, offering enhanced penetration depth for detecting optical-absorbing components in tissues. Nonetheless, challenges arise in scanning large areas with high spatial resolution. With speed limitations imposed by laser pulse repetition rates, the potential role of computational methods is highlighted in accelerating PAM imaging. We propose a novel and highly adaptable algorithm named DiffPam that utilizes diffusion models to speed up the photoacoustic imaging process. We leveraged a diffusion model trained exclusively on natural images, comparing its performance with an in-domain trained U-Net model using a dataset focused on PAM images of mice brain microvasculature. Our findings indicate that DiffPam performs similarly to a dedicated U-Net model without needing a large dataset. We demonstrate that scanning can be accelerated fivefold with limited information loss. We achieved a
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