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
2 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
NEUROSPIN - Service NEUROSPIN, Inria Saclay - Ile de France
3 CEA - Commissariat à l'énergie atomique et aux énergies alternatives
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.
Type de document :
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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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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