Extended Kalman Filtering for the Modeling and Analysis of ICG Pharmacokinetics in Cancerous Tumors Using NIR Optical Methods

Abstract

Compartmental modeling of indocyanine green (ICG) pharmacokinetics, as measured by near infrared (NIR) techniques, has the potential to provide diagnostic information for tumor differentiation. In this paper, we present three different compartmental models to model the pharmacokinetics of ICG in cancerous tumors. We introduce a systematic and robust approach to model and analyze ICG pharmacokinetics based on the extended Kalman filtering (EKF) framework. The proposed EKF framework effectively models multiple-compartment and multiple-measurement systems in the presence of measurement noise and uncertainties in model dynamics. It provides simultaneous estimation of pharmacokinetic parameters and ICG concentrations in each compartment. Moreover, the recursive nature of the Kalman filter estimator potentially allows real-time monitoring of time varying pharmacokinetic rates and concentration changes in different compartments. Additionally, we introduce an information theoretic criteria for the best compartmental model order selection, and residual analysis for the statistical validation of the estimates. We tested our approach using the ICG concentration data acquired from four Fischer rats carrying adenocarcinoma tumor cells. Our study indicates that, in addition to the pharmacokinetic rates, the EKF model may provide parameters that may be useful for tumor differentiation.

A novel image‐guided surgery of hepatocellular carcinoma by indocyanine green fluorescence imaging navigation

“…It is hypothesized that ICG accumulates in regions of tissue possessing leaky capillaries of vessels [13] or disturbance of bile secretion. Indeed, there is heterogeneity of ICG accumulation in the same HCC tumor as seen in Figure 2C.…”

Implicit and explicit prior information in near-infrared spectral imaging: accuracy, quantification and diagnostic value

“…Incorporation of the drug kinetics in the forward or inverse photon propagation models has been shown in just one or two studies [95], and there is a promise that including an accurate kinetic model in the inversion problem may improve accuracy in quantification. More complex kinetic or tissue modelling procedures have been studied by some groups [103]. Additionally, several studies have indicated that better knowledge of the drug concentration a priori would improve the ability to define the best acquisition parameters [127], indicating that inclusion in the data acquisition or system design level could also be beneficial.…”

A simulation study of the variability of indocyanine green kinetics and using structurala prioriinformation in dynamic contrast enhanced diffuse optical tomography (DCE-DOT)

“…Therefore, we showed that the peak concentration was not an informative parameter when it was used to make comparisons between different cases. This was previously mentioned by Alacam et al 2006a. In fact, it is seen from the structure of the equation (4) that the rate constant, k ep , is more related to the rising and the descending slopes of the kinetic curve whereas K trans is more affected by the peak value since it appears as an amplitude factor in the triple exponential function. Therefore, especially the accuracy of the recovered K trans is limited by the spatial resolution and depth penetration of the DOT.…”