扩散光学层析成像(DOT)是一种利用近红外光来探测生物组织光学结构的低成本、无辐射损伤、成像深度深的在体光学功能性成像技术。由于生物组织体自身需满足强散射、低吸收以及成像空间分辨率高等需求,因此DOT重建的逆问题具有严重的病态特性。传统的逆问题解决办法主要是基于代数迭代的重建方法,随着人工智能的发展及大数据时代的到来,深度学习研究掀起了一个新高潮,基于深度学习网络模型的逆问题解决方法逐步被用于DOT重建过程中。通过梳理传统的DOT重建算法,重点综述了最新深度学习用于DOT重建的研究进展,旨在为本领域相关研究团队提供参考。

Diffuse optical tomography (DOT) is a low-cost, non-radiative damage, deep detection in vivo optical functional imaging technology that uses near-infrared light to detect biological tissue optical structures. Due to the strong scattering, low absorption characteristics, and high spatial resolution of the biological tissue itself, the inverse problem of DOT reconstruction has serious ill-conditioned characteristics. The traditional inverse problem solution is mainly based on the algebraic iterative reconstruction method. With the development of artificial intelligence and the arrival of the era of big data, deep learning research has set off to reach another new climax. The inverse problem-solving method based on a deep learning network model is gradually used in the DOT reconstruction process. On the basis of combing the traditional DOT reconstruction algorithm, this manuscript focuses on the research progress of the latest deep learning for DOT reconstruction and provides reference for relevant research teams in this field.