Principal component analysis of indocyanine green fluorescence dynamics for diagnosis of vascular diseases

Abstract

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 vascula 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.