Bayesian Joint Detection-Estimation of cerebral vasoreactivity from ASL fMRI data

Thomas Vincent 1 Jan Warnking 2 Marjorie Villien 2 Alexandre Krainik 2 Philippe Ciuciu 3 Florence Forbes 1, *
* Corresponding author
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
3 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
NEUROSPIN - Service NEUROSPIN, Inria Saclay - Ile de France
Abstract : Although the study of cerebral vasoreactivity using fMRI is mainly conducted through the BOLD fMRI modality, owing to its relatively high signal-to-noise ratio (SNR), ASL fMRI provides a more interpretable measure of cerebral vasoreactivity than BOLD fMRI. Still, ASL suffers from a low SNR and is hampered by a large amount of physiological noise. The current contribution aims at improving the re- covery of the vasoreactive component from the ASL signal. To this end, a Bayesian hierarchical model is proposed, enabling the recovery of per- fusion levels as well as fitting their dynamics. On a single-subject ASL real data set involving perfusion changes induced by hypercapnia, the approach is compared with a classical GLM-based analysis. A better goodness-of-fit is achieved, especially in the transitions between baseline and hypercapnia periods. Also, perfusion levels are recovered with higher sensitivity and show a better contrast between gray- and white matter.
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Thomas Vincent, Jan Warnking, Marjorie Villien, Alexandre Krainik, Philippe Ciuciu, et al.. Bayesian Joint Detection-Estimation of cerebral vasoreactivity from ASL fMRI data. MICCAI 2013 - 16th International Conference on Medical Image Computing and Computer Assisted Intervention, Scientific Council of Japan, Sep 2013, Nagoya, Japan. pp.616-623, ⟨10.1007/978-3-642-40763-5_76⟩. ⟨hal-00854437⟩

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