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

TL;DR

This paper presents a novel nonlinear unmixing method based on compartment models for kinetic analysis of dynamic PET images, enabling direct estimation of binding potential without arterial input functions.

Contribution

It introduces a new nonlinear factor analysis approach that incorporates compartment models and global kinetic parameters for PET data analysis.

Findings

Effective on synthetic data

Demonstrates potential on real PET data

Allows direct binding potential estimation

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 the binding potential with respect to the free fractions in each non-specific binding tissue. The performance of the method is evaluated on synthetic and real data to demonstrate its potential interest.