Compartment model-based nonlinear unmixing for kinetic analysis of dynamic PET images

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.

Automated summary

When arterial input function is not available, a reference time-activity curve (TAC) needs to be extracted for quantification of dynamic PET images. This paper introduces a novel nonlinear factor analysis approach that utilizes a compartment model to compute the kinetic parameters of specific binding tissues jointly. It integrates data-driven parametric imaging methods to establish a physical description of the PET data, thereby relating specific binding with non-specific binding kinetics in the tissues of interest. The performance of the method is evaluated using synthetic and real data, demonstrating its potential significance.