Shape based pharmacokinetic fluorescence optical tomography

Gottam, Omprakash; Naik, Naren; Gambhir, Sanjay

doi:10.1117/12.2319358

Cited by 1 publication

(1 citation statement)

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“…Preliminary results from such a formulation in a Levenberg-Marquardt setting have been recently presented. 41 In our pharmacokinetic tomographic settings, the (static) boundary of the tumor is reconstructed along with the dynamic concentrations within as well as the state-variable model's pharmacokinetic parameters and volume fractions. Decay of the concentrations is assured by the structure of the state ordinary differential equation (ODE)-model's coefficient-matrix.…”

Section: Introductionmentioning

confidence: 99%

Parameterized level-set based pharmacokinetic fluorescence optical tomography using the regularized Gauss–Newton filter

2018

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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. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Introductionmentioning

confidence: 99%