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[PUBLICATION] Compartment Model-based Nonlinear Unmixing for Kinetic Analysis of Dynamic PET Images

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Dates

from November 14, 2022 to December 5, 2022

A research by Yanna Cruz Cavalcanti, Thomas Oberlin, Vinicius Ferraris, Nicolas Dobigeon, Maria Ribeiro et Clovis Tauber, published in Medical Image Analysis

When no arterial input function is available, quantification of dynamic PET images requires a previous step devoted to the extraction of a reference time-activity curve (TAC). Factor analysis is often applied for this purpose. This paper introduces a novel approach that conducts a new kind of nonlinear factor analysis relying on a compartment model, and computes the kinetic parameters of specific binding tissues jointly. To this end, it capitalizes on data-driven parametric imaging methods to provide a physical description of the underlying PET data, directly relating the specific binding with the kinetics of the non-specific binding in the corresponding tissues. This characterization is introduced into the factor analysis formulation to yield a novel nonlinear unmixing model designed for PET image analysis. This model also explicitly introduces global kinetic parameters that allow for a direct estimation of a binding potential that represents the ratio at equilibrium of specifically bound radioligand to the concentration of nondisplaceable radioligand in each non-specific binding tissue. The performance of the method is evaluated on synthetic and real data to demonstrate its potential interest.
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