title: 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 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.
authors.affiliation: INRIA, MISTIS, Grenoble University, LJK, Grenoble, France.
authors.forename: Thomas
authors.initials: T
authors.lastname: Vincent
authors.affiliation: INSERM U836-UJF-CEA-CHU (GIN), Grenoble, France.
authors.forename: Jan
authors.initials: J
authors.lastname: Warnking
authors.affiliation: INSERM U836-UJF-CEA-CHU (GIN), Grenoble, France.
authors.forename: Marjorie
authors.initials: M
authors.lastname: Villien
authors.affiliation: INSERM U836-UJF-CEA-CHU (GIN), Grenoble, France.
authors.forename: Alexandre
authors.initials: A
authors.lastname: Krainik
authors.affiliation: CEA/DSV/I2BM NeuroSpin center, Bat. 145, F-91191 Gif-sur-Yvette, France.
authors.forename: Philippe
authors.initials: P
authors.lastname: Ciuciu
authors.affiliation: INRIA, MISTIS, Grenoble University, LJK, Grenoble, France.
authors.forename: Florence
authors.initials: F
authors.lastname: Forbes
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001783:Blood Flow Velocity / Q000502:physiology; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D002560:Cerebrovascular Circulation / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity