| HAL-Inria | Publications, software ... of Inria's scientists |
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 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
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
File
vincent.pdf
Files produced by the author(s)