Indocyanine green (ICG) fluorescence imaging has been clinically used for noninvasive visualizations of vascular structures. We have previously developed a diagnostic system based on dynamic ICG fluorescence imaging for sensitive detection of vascular disorders. However, because high-dimensional raw data were used, the analysis of the ICG dynamics proved difficult. We used principal component analysis (PCA) in this study to extract important elements without significant loss of information. We examined ICG spatiotemporal profiles and identified critical features related to vascular disorders. PCA time courses of the first three components showed a distinct pattern in diabetic patients. Among the major components, the second principal component (PC2) represented arterial-like features. The explained variance of PC2 in diabetic patients was significantly lower than in normal controls. To visualize the spatial pattern of PCs, pixels were mapped with red, green, and blue channels. The PC2 score showed an inverse pattern between normal controls and diabetic patients. We propose that PC2 can be used as a representative bioimaging marker for the screening of vascular diseases. It may also be useful in simple extractions of arterial-like features.
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.
We have previously discovered that near-infrared optical imaging of indocyanine green (ICG) signal and analyzing its dynamics can be applied for measurement of blood perfusion rate and detection of Raynaud’s phenomenon (RP). Especially, RP is closely associated with abnormal vasomotor responses and can progress to tissue necrosis due to excessively sustained vasoconstriction. Therefore, early detecting of RP is one of important implication to prevent tissue damage from peripheral vascular disorders. In the present study, we propose new analysis and scoring method of symmetricity of T<sub>max</sub> value of left and right extremities. Moreover, this symmetricity analysis can give further information about microvascular insufficiency. For validation of the proposed method, we tested whether the segmental and paired analysis of T<sub>max</sub> value (time-to-peak) of ICG dynamics can be used for sensitive diagnosis of microvascular abnormalities which cannot be detected by conventional methods. From the near-infrared images of diabetes mellitus patients with vascular complications, the trend of asymmetry in T<sub>max</sub> value was observed. We assumed that decreasing local blood perfusion by autonomic nerve dysfunction causes the asymmetric T<sub>max</sub> value of right and left feet. These results collectively indicate that the proposed method can be used as a useful diagnostic tool for RP or other microvascular disorders.
Accurate and reliable diagnosis of functional insufficiency of peripheral vasculature is essential since Raynaud phenomenon (RP), most common form of peripheral vascular insufficiency, is commonly associated with systemic vascular disorders. We have previously demonstrated that dynamic imaging of near-infrared fluorophore indocyanine green (ICG) can be a noninvasive and sensitive tool to measure tissue perfusion. In the present study, we demonstrated that combined analysis of multiple parameters, especially onset time and modified Tmax which means the time from onset of ICG fluorescence to Tmax, can be used as a reliable diagnostic tool for RP. To validate the method, we performed the conventional thermographic analysis combined with cold challenge and rewarming along with ICG dynamic imaging and segmental analysis. A case-control analysis demonstrated that segmental pattern of ICG dynamics in both hands was significantly different between normal and RP case, suggesting the possibility of clinical application of this novel method for the convenient and reliable diagnosis of RP.