She Junjie, Zhang Qican, Wang Yajun, Hu Hongying, You Meng, Shen Junfei
3D Sensing and Machine Vision Lab, College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.
State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral Medical Imaging, West China Hospital of Stomatology, Sichuan University, Chengdu 610065, China.
Photoacoustics. 2025 Aug 6;45:100757. doi: 10.1016/j.pacs.2025.100757. eCollection 2025 Oct.
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 little as 1.5 % of the full sampling rate, corresponding to more than a 66-fold increase in acquisition efficiency. In in-vivo experiments on mouse ear vasculature, IE-PAM outperforms both traditional and learning-based baselines in fine vascular fidelity, artifact suppression, and robustness across varying sampling rates. By minimizing information redundancy at the acquisition stage and enabling accurate reconstruction from minimal data, IE-PAM provides a foundation for efficient, fast and scalable photoacoustic imaging in both preclinical and research applications.
光声显微镜（PAM）提供高分辨率、非侵入性和无标记成像，使其在生物医学研究中具有极高价值。然而，数据采集速度慢和采样要求高仍然是限制其更广泛应用和可扩展性的关键挑战。我们提出了一种信息高效光声显微镜（IE-PAM），它将稀疏扫描编码与神经网络解码相结合，以从极其有限的测量中实现高质量重建。具体而言，IE-PAM采用由随机二进制掩码引导的稀疏扫描采集方案，并使用AFDU-Net重建高保真图像，AFDU-Net是一种在全采样真实数据上训练的定制神经解码器。我们的系统能够从低至全采样率1.5%的数据中忠实地恢复详细的解剖结构，这相当于采集效率提高了66倍以上。在小鼠耳部血管系统的体内实验中，IE-PAM在精细血管保真度、伪影抑制以及不同采样率下的稳健性方面均优于传统和基于学习的基线方法。通过在采集阶段最小化信息冗余并实现从最少数据进行准确重建，IE-PAM为临床前和研究应用中的高效、快速且可扩展的光声成像奠定了基础。
