深度学习在光声成像中的应用现状

Application of Deep Learning in Photoacoustic Imaging

光声成像(Photoacoustic Imaging,PAI)是一种多物理场耦合的无创生物医学功能成像技术,它将纯光学成像的高对比度与超声成像的高空间分辨率相结合,可同时获得生物组织的结构和功能成分信息。近年来,随着深度学习算法在医学图像处理中的广泛应用,基于深度学习的光声成像算法也成为该领域的研究热点。对深度学习在PAI图像重建中的应用现状进行综述,归纳和总结现有的算法,分析目前存在的问题,并展望未来可能的发展趋势。

Photoacoustic imaging(PAI)is a multi-physics coupled non-invasive biomedical functional imaging technology.It combines the high contrast of pure optical imaging with the high spatial resolution of ultrasonic imaging,and can obtain the morpho-logy and functional components information of target tissues at the same time.In recent years,deep learning(DL)has been widely applied in medical image processing.The PAI imaging algorithms based on DL have attracted more and more attention of researchers.This paper reviewed the current application of DL in PAI image reconstruction,summarized the existing algorithms,analyzed their limits and forecasted the possible improvements in the future.