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Comparison of Stochastic and Variational Solutions to ASL fMRI Data Analysis

Aina Frau-Pascual 1, 2 Florence Forbes 1 Philippe Ciuciu 3, 2
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
Inria Saclay - Ile de France, NEUROSPIN - Service NEUROSPIN
Abstract : Functional Arterial Spin Labeling (fASL) MRI can provide a quantitative measurement of changes of cerebral blood flow induced by stimulation or task performance. fASL data is commonly analysed using a general linear model (GLM) with regressors based on the canonical hemodynamic response function. In this work, we consider instead a joint detection-estimation (JDE) framework which has the advantage of allowing the extraction of both task-related perfusion and hemodynamic responses not restricted to canonical shapes. Previous JDE attempts for ASL have been based on computer intensive sampling (MCMC) methods. Our contribution is to provide a comparison with an alternative variational expectation-maximization (VEM) algorithm on synthetic and real data.
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Aina Frau-Pascual, Florence Forbes, Philippe Ciuciu. Comparison of Stochastic and Variational Solutions to ASL fMRI Data Analysis. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, Oct 2015, Munich, Germany. pp.85-92, ⟨10.1007/978-3-319-24553-9_11⟩. ⟨hal-01249018⟩

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