Pharmacokinetic tomography is emerging as an important methodology for detecting abnormalities in tissue based upon spatially varying estimation of the pharmacokinetic rates governing the leakage of an injected fluorophore between blood plasma and tissue. We present a shape-based reconstruction framework of a compartment-model based formulation of this dynamic fluorescent optical tomography problem to solve for the pharmacokinetic rates and concentrations of the fluorophore from time-varying log intensity measurements of the optical signal. The compartment-model based state variable model is set up in a radial basis function parameterized level set setting. The state (concentrations) and (pharmacokinetic) parameter estimation problem is solved with an iteratively regularized Gauss–Newton filter in a trust-region framework. Reconstructions obtained using this scheme for noisy data obtained from cancer mimicking numerical phantoms of near/sub-cm sizes show a good localization of the affected regions and reasonable estimates of the pharmacokinetic rates and concentration curves.
Pharmacokinetic analysis of optical fluorophore provides physiological information of the abnormalities in tissue. Compartment modeling of the fluorophore pharmacokinetics quantify the physiological changes in the tissue. We propose a shape based tomographic reconstruction of pharmacokinetic rates, concentrations and volume fractions of the fluorophore using the time varying near infra-red measurements. Radial basis function based parametric levelsets are used to represent the boundary of the spatially varying parameters of interest. A regularized Gauss-Newton filter based scheme is used reconstruct shape, pharmacokinetic rates, volume fractions and concentration parameters. Reconstruction results for tumor mimicking numerical phantom validate our proposed approach.