Title :
Extended Kalman Filtering for the Modeling and Analysis of ICG Pharmacokinetics in Cancerous Tumors Using NIR Optical Methods
Author :
Alacam, B. ; Yazici, B. ; Intes, X. ; Chance, B.
Author_Institution :
Dept. of Electr., Comput., & Syst. Eng., Rensselaer Polytech. Inst., Troy, NY
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
Keywords :
Kalman filters; bio-optics; cancer; cellular biophysics; information theory; patient diagnosis; physiological models; tumours; Fischer rats; ICG pharmacokinetics; NIR optical methods; adenocarcinoma tumor cells; cancerous tumors; compartmental modeling; extended Kalman filtering; indocyanine green; information theoretic criteria; measurement noise; multiple-compartment systems; multiple-measurement systems; near infrared techniques; tumor differentiation; Filtering; Kalman filters; Measurement uncertainty; Monitoring; Neoplasms; Noise measurement; Noise robustness; Optical filters; Parameter estimation; Recursive estimation; Compartmental analysis; extended Kalman filter; indocyanine green; pharmacokinetics; tumor characterization; Adenocarcinoma; Algorithms; Animals; Cell Line, Tumor; Computer Simulation; Diagnosis, Computer-Assisted; Indocyanine Green; Metabolic Clearance Rate; Models, Biological; Rats; Rats, Inbred F344; Spectrophotometry, Infrared;
Journal_Title :
Biomedical Engineering, IEEE Transactions on
DOI :
10.1109/TBME.2006.881796