Physiologically Informed Bayesian Analysis of ASL fMRI Data

Aina Frau-Pascual 1, 2 Thomas Vincent 1 Jennifer Sloboda 1 Philippe Ciuciu 3, 2 Florence Forbes 1
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : Arterial Spin Labelling (ASL) functional Magnetic Resonance Imaging (fMRI) data provides a quantitative measure of blood perfusion, that can be correlated to neuronal activation. In contrast to BOLD measure, it is a direct measure of cerebral blood flow. However, ASL data has a lower SNR and resolution so that the recovery of the perfusion response of interest suffers from the contamination by a stronger hemodynamic component in the ASL signal. In this work we consider a model of both hemodynamic and perfusion components within the ASL signal. A physiological link between these two components is analyzed and used for a more accurate estimation of the perfusion response function in particular in the usual ASL low SNR conditions.
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Communication dans un congrès
M. Jorge Cardoso; Ivor Simpson; Tal Arbel; Doina Precup; Annemie Ribbens. BAMBI 2014 - First International Workshop on Bayesian and grAphical Models for Biomedical Imaging, Sep 2014, Boston, United States. Springer International Publishing, 8677, pp.37 - 48, 2014, Lecture Notes in Computer Science. <10.1007/978-3-319-12289-2_4>
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https://hal.inria.fr/hal-01100266
Contributeur : Aina Frau-Pascual <>
Soumis le : mardi 6 janvier 2015 - 11:07:08
Dernière modification le : jeudi 9 février 2017 - 15:48:11
Document(s) archivé(s) le : mercredi 3 juin 2015 - 17:11:29

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Aina Frau-Pascual, Thomas Vincent, Jennifer Sloboda, Philippe Ciuciu, Florence Forbes. Physiologically Informed Bayesian Analysis of ASL fMRI Data. M. Jorge Cardoso; Ivor Simpson; Tal Arbel; Doina Precup; Annemie Ribbens. BAMBI 2014 - First International Workshop on Bayesian and grAphical Models for Biomedical Imaging, Sep 2014, Boston, United States. Springer International Publishing, 8677, pp.37 - 48, 2014, Lecture Notes in Computer Science. <10.1007/978-3-319-12289-2_4>. <hal-01100266>

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