Unmixing dynamic PET images with variable specific binding kinetics

TL;DR

This paper introduces a novel linear unmixing approach for dynamic PET images that explicitly models spatial variability in specific binding kinetics, improving factor estimation and interpretability over traditional methods.

Contribution

It proposes a new unmixing method that accounts for spatial fluctuations in binding kinetics, enhancing analysis of dynamic PET data.

Findings

Outperforms conventional methods in synthetic data tests

Improves factor estimation and proportion extraction in real data

Shows potential for better understanding of tissue-specific binding

Abstract

To analyze dynamic positron emission tomography (PET) images, various generic multivariate data analysis techniques have been considered in the literature, such as principal component analysis (PCA), independent component analysis (ICA), factor analysis and nonnegative matrix factorization (NMF). Nevertheless, these conventional approaches neglect any possible nonlinear variations in the time activity curves describing the kinetic behavior of tissues with specific binding, which limits their ability to recover a reliable, understandable and interpretable description of the data. This paper proposes an alternative analysis paradigm that accounts for spatial fluctuations in the exchange rate of the tracer between a free compartment and a specifically bound ligand compartment. The method relies on the concept of linear unmixing, usually applied on the hyperspectral domain, which combines NMF with a sum-to-one constraint that ensures an exhaustive description of the mixtures. The spatial variability of the signature corresponding to the specific binding tissue is explicitly modeled through a perturbed component. The performance of the method is assessed on both synthetic and real data and is shown to compete favorably when compared to other conventional analysis methods. The proposed method improved both factor estimation and proportions extraction for specific binding. Modeling the variability of the specific binding factor has a strong potential impact for dynamic PET image analysis.