Compartment model-based nonlinear unmixing for kinetic analysis of dynamic PET images
Abstract
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
Domains
Computer Science [cs] Artificial Intelligence [cs.AI] Computer Science [cs] Computer Vision and Pattern Recognition [cs.CV] Computer Science [cs] Machine Learning [cs.LG] Computer Science [cs] Image Processing [eess.IV] Computer Science [cs] Signal and Image Processing Physics [physics] Physics [physics] Data Analysis, Statistics and Probability [physics.data-an]
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