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.
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.
The purpose of this study is to examine the peripheral tissue perfusion rates by time-series analysis of
distribution and elimination kinetics of a clinically proven NIR fluorescence probe, indocyanine green (ICG).
We developed a new method, dynamic ICG perfusion imaging technique to evaluate peripheral tissue perfusion
that employs planar imaging with a CCD digital imaging system and time-series analysis of the spatiotemporal dynamics (150s) of intravenously injected ICG by using nonlinear regression and differential evolution methods.
Six parameters (α, β, s, d, m; parameters which depend on an arterial input function (AIF) into a lower extremity
and p; perfusion rates in the lower extremity) were estimated by the nonlinear regression modeling method. We
have confirmed the validity of our new method by applying the method to a normal control and a patient with
peripheral arterial occlusion disease (PAOD). PAOD patient showed a unique AIF curve pattern, which was
caused by collateral blood flow bypassing the occluded major artery. The lower extremity tissue perfusion rate
of the PAOD patient was estimated as about 35% of those of normal values. These results indicate that ICG
perfusion imaging method is sensitive enough to diagnose PAOD and capable of diagnosing functional arterial
diseases.
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