A Variational Bayesian inference method for parametric imaging of PET data.
Neuroimage
; 150: 136-149, 2017 04 15.
Article
em En
| MEDLINE
| ID: mdl-28213113
ABSTRACT
In dynamic Positron Emission Tomography (PET) studies, compartmental models provide the richest information on the tracer kinetics of the tissue. Inverting such models at the voxel level is however quite challenging due to the low signal-to-noise ratio of the time activity curves. In this study, we propose the use of a Variational Bayesian (VB) approach to efficiently solve this issue and thus obtain robust quantitative parametric maps. VB was adapted to the non-uniform noise distribution of PET data. Moreover, we propose a novel hierarchical scheme to define the model parameter priors directly from the images in case such information are not available from the literature, as often happens with new PET tracers. VB was initially tested on synthetic data generated using compartmental models of increasing complexity, providing accurate (%bias<2%±2%, root mean square error<15%±5%) parameter estimates. When applied to real data on a paradigmatic set of PET tracers (L-[1-11C]leucine, [11C]WAY100635 and [18F]FDG), VB was able to generate reliable parametric maps even in presence of high noise in the data (unreliable estimates<11%±5%).
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
/
Mapeamento Encefálico
/
Tomografia por Emissão de Pósitrons
/
Modelos Neurológicos
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Neuroimage
Assunto da revista:
DIAGNOSTICO POR IMAGEM
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
Itália