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Conference Papers Year : 2013

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

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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Dates and versions

hal-00854437 , version 1 (27-08-2013)

Identifiers

Cite

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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