Sparse scanning encoding and neural network decoding for compressed photoacoustic microscopy.

ABSTRACT: Photoacoustic microscopy (PAM) offers high-resolution, non-invasive, and label-free imaging, making it invaluable for biomedical research. However, slow data acquisition and high sampling requirements remain key challenges that limit its broader applicability and scalability. We propose an Information-Efficient Photoacoustic Microscopy (IE-PAM) that jointly integrates sparse scanning encoding with neural network decoding to achieve high-quality reconstruction from extremely limited measurements. Specifically, IE-PAM employs a sparse-scanning acquisition scheme guided by random binary masks and reconstructs high-fidelity images using AFDU-Net, a custom-designed neural decoder trained on fully sampled ground truth data. Our system can faithfully recover detailed anatomical structures from as

SUBMITTER: She J 

PROVIDER: S-EPMC12365337 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature