Wang Ruofan, Zhu Jing, Xia Jun, Yao Junjie, Shi Junhui, Li Chiye
Research Center for Humanoid Sensing, Zhejiang Lab, Hangzhou, 311100, China.
Department of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14260, USA.
Biomed Opt Express. 2023 Mar 30;14(4):1777-1799. doi: 10.1364/BOE.483081. eCollection 2023 Apr 1.
Photoacoustic imaging combines high optical absorption contrast and deep acoustic penetration, and can reveal structural, molecular, and functional information about biological tissue non-invasively. Due to practical restrictions, photoacoustic imaging systems often face various challenges, such as complex system configuration, long imaging time, and/or less-than-ideal image quality, which collectively hinder their clinical application. Machine learning has been applied to improve photoacoustic imaging and mitigate the otherwise strict requirements in system setup and data acquisition. In contrast to the previous reviews of learned methods in photoacoustic computed tomography (PACT), this review focuses on the application of machine learning approaches to address the limited spatial sampling problems in photoacoustic imaging, specifically the limited view and undersampling issues. We summarize the relevant PACT works based on their training data, workflow, and model architecture. Notably, we also introduce the recent limited sampling works on the other major implementation of photoacoustic imaging, i.e., photoacoustic microscopy (PAM). With machine learning-based processing, photoacoustic imaging can achieve improved image quality with modest spatial sampling, presenting great potential for low-cost and user-friendly clinical applications.
光声成像结合了高光学吸收对比度和深声学穿透性，能够无创地揭示生物组织的结构、分子和功能信息。由于实际限制，光声成像系统常常面临各种挑战，例如复杂的系统配置、较长的成像时间和/或不理想的图像质量，这些因素共同阻碍了它们的临床应用。机器学习已被用于改善光声成像，并减轻系统设置和数据采集方面原本严格的要求。与之前对光声计算机断层扫描（PACT）中学习方法的综述不同，本综述重点关注机器学习方法在解决光声成像中有限空间采样问题的应用，特别是有限视角和欠采样问题。我们根据相关PACT工作的训练数据、工作流程和模型架构对其进行总结。值得注意的是，我们还介绍了光声成像另一种主要实现方式，即光声显微镜（PAM）最近的有限采样工作。通过基于机器学习的处理，光声成像能够在适度的空间采样下实现更好的图像质量，为低成本和用户友好型临床应用展现出巨大潜力。
