Yin Hao, Wu Yanxiong, Guo Peiqing, Luo Jiaxiong, Lin Jianan, Zhou Bin, Ye Qianyao, Lin Lintong, Li Hongbo, Zou Donglan, Li Xiaosong, Wei Bin, Yang Zhiming
School of Physics and Optoelectronic Engineering, Foshan University, Foshan, 528000, China.
Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology, School of Physics and Optoelectronic Engineering, Guangdong-HongKong-Macao, Foshan University, Foshan, 528225, China.
J Imaging Inform Med. 2025 Jan 6. doi: 10.1007/s10278-024-01379-1.
Nailfold microcirculation examination is crucial for the early differential diagnosis of diseases and indicating their severity. In particular, panoramic nailfold flow velocity measurements can provide direct quantitative indicators for the study of vascular diseases and technical support to assess vascular health. Previously, nailfold imaging equipment was limited by a small field of view. Therefore, research on nailfold flow velocity measurement primarily focused on improving the accuracy of single-vessel flow velocity results, while there were few studies on nailfold panoramic flow velocity. Furthermore, with improvements in the imaging field of view and the increasing clinical demand for speed in obtaining nailfold parameter results, doctors do not have time to crop videos to obtain flow velocity results. Therefore, research on nailfold panoramic flow velocity measurement is crucial. This study presents a panoramic nailfold flow velocity measurement method based on enhanced plasma gap information. In contrast to previous methods, the use of a deep learning model to decompose the panoramic flow velocity measurement task into several vessel flow velocity measurement tasks is proposed herein. For improved accuracy, a plasma gap information enhancement method is proposed, using the frame difference to enhance the position movement information of plasma gaps in videos. The t-test results show that the Pearson correlation coefficient between the results of the proposed method and those manually calculated by experts is 0.992 (t = - 0.0889, p = 0.929; > 0.05), with an average error of 2.137%. Therefore, there is no significant difference between the results obtained by the proposed method proposed and the manually calculated results. The feasibility experiment demonstrates that the proposed method can concurrently obtain the flow rate results of 13 nailfold blood vessels. Finally, the proposed method provides an efficient solution for panoramic flow velocity measurement of large-field nailfold multi-vessel videos.
甲襞微循环检查对于疾病的早期鉴别诊断及其严重程度的评估至关重要。特别是,甲襞全视野血流速度测量可为血管疾病的研究提供直接的定量指标,并为评估血管健康提供技术支持。以前,甲襞成像设备受限于小视野。因此,甲襞血流速度测量的研究主要集中在提高单血管血流速度结果的准确性上,而关于甲襞全视野血流速度的研究很少。此外,随着成像视野的改善以及临床对快速获取甲襞参数结果的需求增加,医生没有时间裁剪视频来获取血流速度结果。因此,甲襞全视野血流速度测量的研究至关重要。本研究提出了一种基于增强血浆间隙信息的甲襞全视野血流速度测量方法。与以前的方法相比,本文提出使用深度学习模型将甲襞全视野血流速度测量任务分解为多个血管血流速度测量任务。为了提高准确性,提出了一种血浆间隙信息增强方法,利用帧差来增强视频中血浆间隙的位置移动信息。t检验结果表明,该方法的结果与专家手动计算的结果之间的Pearson相关系数为0.992(t = -0.0889,p = 0.929;>0.05),平均误差为2.137%。因此,该方法所得结果与手动计算结果之间无显著差异。可行性实验表明,该方法可以同时获得13条甲襞血管的流速结果。最后,该方法为大视野甲襞多血管视频的全视野血流速度测量提供了一种有效的解决方案。