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Conference papers
Bayesian BOLD and perfusion source separation and deconvolution from functional ASL imaging
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.
Cited literature [12 references]
https://hal.inria.fr/hal-00859373
Contributor : Philippe Ciuciu <>
Submitted on : Saturday, September 7, 2013 - 5:00:00 PM
Last modification on : Tuesday, November 24, 2020 - 4:38:03 PM
Long-term archiving on: : Sunday, December 8, 2013 - 2:50:15 AM
Contributor : Philippe Ciuciu <>
Submitted on : Saturday, September 7, 2013 - 5:00:00 PM
Last modification on : Tuesday, November 24, 2020 - 4:38:03 PM
Long-term archiving on: : Sunday, December 8, 2013 - 2:50:15 AM
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