Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology, Fos, China.
Foshan University, School of Physics and Optoelectronic Engineering, Foshan, China.
J Biomed Opt. 2022 Feb;27(2). doi: 10.1117/1.JBO.27.2.026002.
Full-field optical angiography is critical for vascular disease research and clinical diagnosis. Existing methods struggle to improve the temporal and spatial resolutions simultaneously.
Spatiotemporal absorption fluctuation imaging (ST-AFI) is proposed to achieve dynamic blood flow imaging with high spatial and temporal resolutions.
ST-AFI is a dynamic optical angiography based on a low-coherence imaging system and U-Net. The system was used to acquire a series of dynamic red blood cell (RBC) signals and static background tissue signals, and U-Net is used to predict optical absorption properties and spatiotemporal fluctuation information. U-Net was generally used in two-dimensional blood flow segmentation as an image processing algorithm for biomedical imaging. In the proposed approach, the network simultaneously analyzes the spatial absorption coefficient differences and the temporal dynamic absorption fluctuation.
The spatial resolution of ST-AFI is up to 4.33 μm, and the temporal resolution is up to 0.032 s. In vivo experiments on 2.5-day-old chicken embryos were conducted. The results demonstrate that intermittent RBCs flow in capillaries can be resolved, and the blood vessels without blood flow can be suppressed.
Using ST-AFI to achieve convolutional neural network (CNN)-based dynamic angiography is a novel approach that may be useful for several clinical applications. Owing to their strong feature extraction ability, CNNs exhibit the potential to be expanded to other blood flow imaging methods for the prediction of the spatiotemporal optical properties with improved temporal and spatial resolutions.
全场光学血管造影术对于血管疾病研究和临床诊断至关重要。现有的方法在提高时间和空间分辨率方面都存在困难。
提出时空吸收波动成像(ST-AFI)以实现具有高时空分辨率的动态血流成像。
ST-AFI 是一种基于低相干成像系统和 U-Net 的动态光学血管造影术。该系统用于获取一系列动态红细胞(RBC)信号和静态背景组织信号,U-Net 用于预测光吸收特性和时空波动信息。U-Net 通常用于二维血流分割,作为生物医学成像的图像处理算法。在提出的方法中,网络同时分析空间吸收系数差异和时间动态吸收波动。
ST-AFI 的空间分辨率高达 4.33 μm,时间分辨率高达 0.032 s。对 2.5 天大的鸡胚进行了体内实验。结果表明,可以分辨毛细血管中间歇性 RBC 流动,并且可以抑制无血流的血管。
使用 ST-AFI 实现基于卷积神经网络(CNN)的动态血管造影术是一种新方法,可能对几种临床应用有用。由于其强大的特征提取能力,CNN 有可能扩展到其他血流成像方法,以提高时间和空间分辨率来预测时空光学特性。