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首页> 外文会议>Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XIII >Principal Component Analysis of Indocyanine Green Fluorescence Dynamics for Diagnosis of Vascular Diseases
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Principal Component Analysis of Indocyanine Green Fluorescence Dynamics for Diagnosis of Vascular Diseases

机译：吲哚菁绿荧光动力学的主成分分析在血管疾病诊断中的应用

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摘要

Indocyanine green (ICG), a near-infrared fluorophore, has been used in visualization of vascular structure and non-invasive diagnosis of vascular disease. Although many imaging techniques have been developed, there are still limitations in diagnosis of vascular diseases. We have recently developed a minimally invasive diagnostics system based on ICG fluorescence imaging for sensitive detection of vascular insufficiency. In this study, we used principal component analysis (PCA) to examine ICG spatiotemporal profile and to obtain pathophysiological information from ICG dynamics. Here we demonstrated that principal components of ICG dynamics in both feet showed significant differences between normal control and diabetic patients with vascular complications. We extracted the PCA time courses of the first three components and found distinct pattern in diabetic patient. We propose that PCA of ICG dynamics reveal better classification performance compared to fluorescence intensity analysis. We anticipate that specific feature of spatiotemporal ICG dynamics can be useful in diagnosis of various vascular diseases.
机译：吲哚菁绿（ICG）是一种近红外荧光团，已用于可视化血管结构和血管疾病的非侵入性诊断。尽管已经开发了许多成像技术，但是在诊断血管疾病方面仍然存在局限性。我们最近开发了一种基于ICG荧光成像的微创诊断系统，用于敏感检测血管功能不全。在这项研究中，我们使用主成分分析（PCA）来检查ICG时空分布并从ICG动力学中获取病理生理信息。在这里，我们证明了正常对照与患有血管并发症的糖尿病患者之间，双脚ICG动态的主要成分显示出显着差异。我们提取了前三个成分的PCA时程，并发现了糖尿病患者的独特模式。我们建议，与荧光强度分析相比，ICG动力学的PCA显示更好的分类性能。我们预期时空ICG动力学的特定特征可用于各种血管疾病的诊断。

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