Photoacoustic imaging (PAI) represents an innovative biomedical imaging modality that harnesses the advantages of optical resolution and acoustic penetration depth while ensuring enhanced safety. Despite its promising potential across a diverse array of preclinical and clinical applications, the clinical implementation of PAI faces significant challenges, including the trade-off between penetration depth and spatial resolution, as well as the demand for faster imaging speeds. This paper explores the fundamental principles underlying PAI, with a particular emphasis on three primary implementations: photoacoustic computed tomography (PACT), photoacoustic microscopy (PAM), and photoacoustic endoscopy (PAE). We undertake a critical assessment of their respective strengths and practical limitations. Furthermore, recent developments in utilizing conventional or deep learning (DL) methodologies for image reconstruction and artefact mitigation across PACT, PAM, and PAE are outlined, demonstrating considerable potential to enhance image quality and accelerate imaging processes. Furthermore, this paper examines the recent developments in quantitative analysis within PAI, including the quantification of haemoglobin concentration, oxygen saturation, and other physiological parameters within tissues. Finally, our discussion encompasses current trends and future directions in PAI research while emphasizing the transformative impact of deep learning on advancing PAI.

翻译：光声成像（PAI）是一种创新的生物医学成像技术，它结合了光学分辨率与声学穿透深度的优势，同时确保了更高的安全性。尽管光声成像在广泛的临床前和临床应用中展现出巨大潜力，但其临床实施仍面临重大挑战，包括穿透深度与空间分辨率之间的权衡，以及对更快成像速度的需求。本文探讨了光声成像的基本原理，并重点介绍了三种主要实现方式：光声计算机断层扫描（PACT）、光声显微镜（PAM）和光声内窥镜（PAE）。我们对它们各自的优势与实际局限性进行了批判性评估。此外，本文概述了近年来利用传统方法或深度学习（DL）方法在PACT、PAM和PAE中进行图像重建和伪影抑制的最新进展，这些方法在提升图像质量和加速成像过程方面显示出巨大潜力。同时，本文还探讨了光声成像定量分析的最新发展，包括组织中血红蛋白浓度、血氧饱和度及其他生理参数的量化。最后，我们的讨论涵盖了光声成像研究的当前趋势与未来方向，并强调了深度学习在推动光声成像发展方面的变革性影响。