Abstract
Functional Arterial Spin Labeling (fASL) MRI can provide a quantitative measurement of changes of cerebral blood flow induced by stimulation or task performance. fASL data is commonly analysed using a general linear modelĀ (GLM) with regressors based on the canonical hemodynamic response function. In this work, we consider instead a joint detection-estimation (JDE) framework which has the advantage of allowing the extraction of both task-related perfusion and hemodynamic responses not restricted to canonical shapes. Previous JDE attempts for ASL have been based on computer intensive sampling (MCMC) methods. Our contribution is to provide a comparison with an alternative variational expectation-maximization (VEM) algorithm on synthetic and real data.
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Keywords
- Root Mean Square Error
- Markov Chain Monte Carlo
- Arterial Spin Label
- Hemodynamic Response Function
- Markov Chain Monte Carlo Approach
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Frau-Pascual, A., Forbes, F., Ciuciu, P. (2015). Comparison of Stochastic and Variational Solutions to ASL fMRI Data Analysis. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9349. Springer, Cham. https://doi.org/10.1007/978-3-319-24553-9_11
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DOI: https://doi.org/10.1007/978-3-319-24553-9_11
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