Skip to Main content Skip to Navigation
Conference papers

Bayesian BOLD and perfusion source separation and deconvolution from functional ASL imaging

Thomas Vincent 1 Florence Forbes 1 Philippe Ciuciu 2
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann, Inria Grenoble - Rhône-Alpes
2 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
Inria Saclay - Ile de France, NEUROSPIN - Service NEUROSPIN
Abstract : In many neuroscience applications, the Arterial Spin Labeling (ASL) fMRI modality arises as a preferable choice to the standard BOLD modality due to its ability to provide a quantitative measure of the Cerebral Blood Flow (CBF). Such a quantification is central but generally performed without consideration of a specific modeling of the perfusion component in the signal often handled via standard GLM approaches using the BOLD canonical response function as regressor. In this work, we propose a novel Bayesian hierarchical model of the ASL signal which allows activation detection and both the extraction of a perfusion and a hemodynamic component. Validation on synthetic and real data sets from event-related ASL show the ability of our model to address the source separation and double deconvolution problems inherent to ASL data analysis.
keyword : ASL BOLD fMRI
Complete list of metadata

Cited literature [12 references]  Display  Hide  Download
Contributor : Philippe Ciuciu Connect in order to contact the contributor
Submitted on : Saturday, September 7, 2013 - 5:00:00 PM
Last modification on : Monday, December 13, 2021 - 9:16:02 AM
Long-term archiving on: : Sunday, December 8, 2013 - 2:50:15 AM


Files produced by the author(s)




Thomas Vincent, Florence Forbes, Philippe Ciuciu. Bayesian BOLD and perfusion source separation and deconvolution from functional ASL imaging. ICASSP 2013 - IEEE International Conference on Acoustics, Speech, and Signal Processing, May 2013, Vancouver, Canada. pp.1003-1007, ⟨10.1109/ICASSP.2013.6637800⟩. ⟨hal-00859373⟩



Les métriques sont temporairement indisponibles