Hybrid transformer-CNN network-driven optical-scanning undersampling for photoacoustic remote sensing microscopy.

ABSTRACT: Imaging speed is critical for photoacoustic microscopy as it affects the capability to capture dynamic biological processes and support real-time clinical applications. Conventional approaches for increasing imaging speed typically involve high-repetition-rate lasers, which pose a risk of thermal damage to samples. Here, we propose a deep-learning-driven optical-scanning undersampling method for photoacoustic remote sensing (PARS) microscopy, accelerating imaging acquisition while maintaining a constant laser repetition rate and reducing laser dosage. We develop a hybrid Transformer-Convolutional Neural Network, HTC-GAN, to address the challenges of both nonuniform sampling and motion misalignment inherent in optical-scanning undersampling. A mouse ear vasculature image dataset is created

SUBMITTER: Pi Y 

PROVIDER: S-EPMC11889609 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature