Compartment model-based nonlinear unmixing for kinetic analysis of dynamic PET images - Signaux et Images Accéder directement au contenu
Article Dans Une Revue Medical Image Analysis Année : 2023

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

Yanna Cruz Cavalcanti
Vinicius Ferraris
  • Fonction : Auteur
  • PersonId : 1287560
  • IdRef : 225232170
Maria Ribeiro
Clovis Tauber

Résumé

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
Fichier principal
Vignette du fichier
Cavalcanti_MEDIA_2023_preprint.pdf (3.08 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03928633 , version 1 (07-01-2023)

Identifiants

Citer

Yanna Cruz Cavalcanti, Thomas Oberlin, Vinicius Ferraris, Nicolas Dobigeon, Maria Ribeiro, et al.. Compartment model-based nonlinear unmixing for kinetic analysis of dynamic PET images. Medical Image Analysis, 2023, 84, pp.102689. ⟨10.1016/j.media.2022.102689⟩. ⟨hal-03928633⟩
69 Consultations
34 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More