MICCAI 2013 - LNCS 8149-8151

Bayesian Joint Detection-Estimation of Cerebral Vasoreactivity from ASL fMRI Data

Thomas Vincent¹, Jan Warnking², Marjorie Villien², Alexandre Krainik², Philippe Ciuciu³, and Florence Forbes¹

¹INRIA, MISTIS, Grenoble University, LJK, Grenoble, France

²INSERM U836-UJF-CEA-CHU (GIN), Grenoble, France

³CEA/DSV/I2BM NeuroSpin center, Bât. 145, F-91191, Gif-sur-Yvette, 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 recovery of the vasoreactive component from the ASL signal. To this end, a Bayesian hierarchical model is proposed, enabling the recovery of perfusion 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.

Keywords: fMRI, ASL, cerebral vasoreactivity, deconvolution, Bayesian analysis, Monte Carlo Markov Chain inference

LNCS 8150, p. 616 ff.

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