In this work, we present spatially resolved pharmacokinetic rate images of indocyanine green (ICG) obtained from three breast cancer patients using near infrared imaging methods. We used a two-compartment model, namely, plasma and extracellular extravascular (EES), to model ICG kinetics around the tumor region. We introduced extended Kalman filtering (EKF) framework to estimate the ICG pharmacokinetic rate images. The EKF framework allows simultaneous estimation of pharmacokinetic rates and the ICG concentrations in each
compartment. Based on the pharmacokinetic rate images, we observed that the rates from inside and outside the tumor region are statistically different with a p-value of 0.0001 for each patient. Additionally, we observed that the ICG concentrations in plasma and the EES compartments are higher around the tumors agreeing with the hypothesis that ICG may act as a diffusible extravascular flow in leaky capillary of cancer vessels. Our study shows that spatially resolved pharmacokinetic rate images can be potentially useful for breast cancer screening and diagnosis.
A number of studies indicate that compartmental modeling of
indocyanine green (ICG) pharmacokinetics, as measured by near
infrared (NIR) techniques, may provide diagnostic information for
tumor differentiation. However, compartmental parameter estimation
is a highly non-linear problem with limited data available in a
clinical setting. Furthermore, pharmacokinetic parameter estimates
show statistical variation from one data set to another. Thus, a
systematic and robust approach is needed to model, estimate and
quantify ICG pharmacokinetic parameters. In this paper, we propose
to model ICG pharmacokinetics in extended Kalman filtering (EKF)
framework. EKF 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, recursive nature of the Kalman filter
estimator potentially allows real time monitoring of time varying
pharmacokinetic rates and concentration changes in different
compartments. We tested our approach using the ICG concentration
data acquired from four Fischer rats carrying adenocarcinoma tumor
cells. Our study indicates that EKF model may provide additional
parameters that may be useful for tumor differentiation.
A number of researchers have previously shown that the ultrasound RF echo of tissue exhibits (1/f)-β characteristics and developed tissue characterization methods based on the fractal parameter β. In this paper we propose Fractional Differencing Autoregressive Moving Average (FARMA) process for modeling RF ultrasound echo and develop breast tissue characterization method based on the FARMA model parameters. This model has been used to capture statistical self-similarity and long-range correlations in image textures, in wide ranging engineering and science applications, including communication network traffic. Here, we present estimation techniques to extract the model parameters, namely features, for classification purposes and tissue characterization. We show the performance of our tissue characterization procedure on several in vivo ultrasound breast images including benign and malignant tumors. The area of the receiver operator characteristics (ROC) based on 60 in vivo images yields a value of 0.79, which indicates that proposed tissue characterization method is comparable in performance with other successful methods reported in the literature.