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A Variational Bayesian inference method for parametric imaging of PET data.
Castellaro, M; Rizzo, G; Tonietto, M; Veronese, M; Turkheimer, F E; Chappell, M A; Bertoldo, A.
Afiliación
  • Castellaro M; Department of Information Engineering, University of Padova, Italy.
  • Rizzo G; Department of Information Engineering, University of Padova, Italy.
  • Tonietto M; Department of Information Engineering, University of Padova, Italy.
  • Veronese M; Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
  • Turkheimer FE; Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
  • Chappell MA; Institute of Biomedical Engineering, University of Oxford, Old Road Campus Research Building, Old Road Campus, Headington, Oxford, United Kingdom.
  • Bertoldo A; Department of Information Engineering, University of Padova, Italy. Electronic address: bertoldo@dei.unipd.it.
Neuroimage ; 150: 136-149, 2017 04 15.
Article en En | MEDLINE | ID: mdl-28213113
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%).
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Mapeo Encefálico / Tomografía de Emisión de Positrones / Modelos Neurológicos Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Neuroimage Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2017 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Procesamiento de Imagen Asistido por Computador / Mapeo Encefálico / Tomografía de Emisión de Positrones / Modelos Neurológicos Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Neuroimage Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2017 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Estados Unidos