Pharmacokinetic-rate imaging of optical fluorophores and breast cancer diagnosis

In this thesis, we study the value of near infra-red (NIR) optical imaging and spectroscopy techniques for breast cancer detection, diagnosis, and staging. In particular, we develop new mathematical models and computational techniques to investigate the value of endogenous contrast provided by NIR imaging and spectroscopy; and the pharmacokinetic information provided by optical fluorophores, specifically, indocyanine green (ICG).;First, we developed three different compartmental models to model the pharmacokinetics of ICG for healthy and malignant tissue. We introduced a systematic and robust approach to estimate and analyze ICG pharmacokinetics based on the extended Kalman filtering (EKF) framework. Additionally, we introduce an information theoretic criteria for the best compartmental model order selection. We tested our approach using the ICG concentration data acquired from four Fischer rats carrying adenocarcinoma tumor cells. Our animal study indicates that pharmacokinetic rates are potentially useful parameters for tumor differentiation and staging.;Secondly, we develop a method of forming pharmacokinetic-rate images of ICG. To form pharmacokinetic-rate images, we first obtain a sequence of ICG concentration images using the differential diffuse optical tomography technique. We next employ a two-compartment model composed of plasma, and extracellular-extravascular space (EES), and estimate the pharmacokinetic-rates and concentrations in each compartment using the EKF framework. The pharmacokinetic-rate images of the three patient show that the rates from the tumor region and outside the tumor region are statistically different. Additionally, the ICG concentrations in plasma, and the EES compartments are higher around the tumor region agreeing with the hypothesis that around the tumor region ICG may act as a diffusible extravascular flow in compromised capillary of cancer vessels.;Thirdly, we present a new method to form pharmacokinetic-rate images of optical fluorophores directly from NIR boundary measurements. We first derive a mapping from spatially resolved pharmacokinetic-rates to NIR boundary measurements by combining compartmental modeling with a diffusion based NIR photon propagation model. We express this mapping as a state-space equation. Next, we introduce a spatio-temporal prior model for the pharmacokinetic-rate images and combine it with the state-space equation. We address the image formation problem using the EKF framework. We analyzed the computational complexity of the resulting algorithms and evaluate their performance in numerical simulations. Simulation results show that the resulting algorithms are more robust and lead to higher signal-to-noise ratio as compared to existing approaches where the reconstruction of concentrations and compartmental modeling are treated separately. Additionally, we reconstructed pharmacokinetic-rate images using in vivo data obtained from three patients with breast tumors. The reconstruction results show that the pharmacokinetic-rates of ICG are higher inside the tumor region as compared to the surrounding tissue.;Finally, we present a study on the evaluation of a set of optical features extracted from in vivo NIR spectroscopy data obtained from 116 patients with breast tumors for breast cancer diagnosis. The in vivo data was collected from 44 patients with malignant and 72 patients with benign tumors. Three features, relative blood volume concentration, oxygenation desaturation and the size of the tumor, are used to differentiate benign and malignant tumors. The diagnostic capability of these features are evaluated using different classifiers including nearest mean, neural network, support vector machine, Parzen, and normal density-based classifiers. The area under the receiver operating characteristics curve of the nearest mean classifier using the three features yields the best value of 0.91. This result suggests that relative blood volume concentration, oxygenation desaturation and size information can differentiate malignant and benign breast tumors with a relatively high precision.