Assessment of peripheral tissue perfusion by optical dynamic fluorescence imaging and nonlinear regression modeling

Yujung Kang; Jungsul Lee; Kihwan Kwon; Chulhee Choi

doi:10.1117/12.841620

2 March 2010 Assessment of peripheral tissue perfusion by optical dynamic fluorescence imaging and nonlinear regression modeling

Yujung Kang, Jungsul Lee, Kihwan Kwon, Chulhee Choi

Author Affiliations +

Proceedings Volume 7548, Photonic Therapeutics and Diagnostics VI; 75483L (2010) https://doi.org/10.1117/12.841620
Event: SPIE BiOS, 2010, San Francisco, California, United States

Abstract

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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Yujung Kang, Jungsul Lee, Kihwan Kwon, and Chulhee Choi "Assessment of peripheral tissue perfusion by optical dynamic fluorescence imaging and nonlinear regression modeling", Proc. SPIE 7548, Photonic Therapeutics and Diagnostics VI, 75483L (2 March 2010); https://doi.org/10.1117/12.841620

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