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Bayesian joint detection-estimation of cerebral vasoreactivity from ASL fMRI data
Thomas Vincent1, Jan Warnking2, Marjorie Villien2
1INRIA, MISTIS, Grenoble University, LJK, Grenoble, France.
Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|March 1, 2014
Summary
This study introduces a Bayesian model to improve arterial spin labeling (ASL) functional MRI (fMRI) for measuring cerebral vasoreactivity. The new method enhances signal recovery, offering better sensitivity and contrast for perfusion changes.
Area of Science:
- Neuroimaging
- Physiology
- Biomedical Engineering
Background:
- Cerebral vasoreactivity is crucial for understanding brain function and is often studied using functional magnetic resonance imaging (fMRI).
- While Blood-Oxygen-Level-Dependent (BOLD) fMRI is common, Arterial Spin Labeling (ASL) fMRI offers a more direct measure of perfusion but suffers from low signal-to-noise ratio (SNR) and physiological noise.
Purpose of the Study:
- To enhance the recovery of the vasoreactive component from ASL fMRI signals.
- To develop a more interpretable and sensitive method for measuring cerebral blood flow dynamics.
Main Methods:
- A Bayesian hierarchical model was developed to improve the signal-to-noise ratio (SNR) and physiological noise resilience in ASL fMRI.
- The model enables simultaneous recovery of perfusion levels and fitting of their dynamic changes.
- The proposed method was evaluated on a single-subject ASL dataset during hypercapnia-induced perfusion changes and compared against a standard GLM analysis.
Main Results:
- The Bayesian hierarchical model demonstrated a superior goodness-of-fit compared to the classical GLM, particularly during transitions between baseline and hypercapnia states.
- Perfusion levels were recovered with increased sensitivity using the proposed method.
- A better contrast between gray and white matter perfusion was observed, indicating improved spatial resolution and accuracy.
Conclusions:
- The proposed Bayesian hierarchical model significantly improves the analysis of ASL fMRI data for cerebral vasoreactivity.
- This approach offers enhanced sensitivity, better gray-white matter contrast, and improved dynamic fitting of perfusion changes, outperforming traditional GLM methods.
