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Poster — Thur Eve — 44: Linearization of Compartmental Models for More Robust Estimates of Regional Hemodynamic, Metabolic and Functional Parameters using DCE‐CT/PET Imaging
Author(s) -
Blais AR,
Dekaban M,
Lee TY
Publication year - 2014
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4894904
Subject(s) - linearization , positron emission tomography , estimation theory , statistical noise , nonlinear system , computation , nonlinear regression , algorithm , computer science , noise (video) , mathematics , nuclear medicine , physics , regression analysis , statistics , artificial intelligence , medicine , quantum mechanics , image (mathematics)
Quantitative analysis of dynamic positron emission tomography (PET) data usually involves minimizing a cost function with nonlinear regression, wherein the choice of starting parameter values and the presence of local minima affect the bias and variability of the estimated kinetic parameters. These nonlinear methods can also require lengthy computation time, making them unsuitable for use in clinical settings. Kinetic modeling of PET aims to estimate the rate parameter k 3 , which is the binding affinity of the tracer to a biological process of interest and is highly susceptible to noise inherent in PET image acquisition. We have developed linearized kinetic models for kinetic analysis of dynamic contrast enhanced computed tomography (DCE‐CT)/PET imaging, including a 2‐compartment model for DCE‐CT and a 3‐compartment model for PET. Use of kinetic parameters estimated from DCE‐CT can stabilize the kinetic analysis of dynamic PET data, allowing for more robust estimation of k 3 . Furthermore, these linearized models are solved with a non‐negative least squares algorithm and together they provide other advantages including: 1) only one possible solution and they do not require a choice of starting parameter values, 2) parameter estimates are comparable in accuracy to those from nonlinear models, 3) significantly reduced computational time. Our simulated data show that when blood volume and permeability are estimated with DCE‐CT, the bias of k 3 estimation with our linearized model is 1.97 ± 38.5% for 1,000 runs with a signal‐to‐noise ratio of 10. In summary, we have developed a computationally efficient technique for accurate estimation of k 3 from noisy dynamic PET data.