Comparison of Stochastic and Variational Solutions to ASL fMRI Data Analysis

  • Aina Frau-Pascual
  • Florence Forbes
  • Philippe Ciuciu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9349)


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 model (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.


Root Mean Square Error Markov Chain Monte Carlo Arterial Spin Label Hemodynamic Response Function Markov Chain Monte Carlo Approach 
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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Aina Frau-Pascual
    • 1
    • 3
  • Florence Forbes
    • 1
  • Philippe Ciuciu
    • 2
    • 3
  1. 1.INRIA, Univ. Grenoble Alpes, LJKGrenobleFrance
  2. 2.CEA/DSV/I2BM/NeuroSpinGif-sur-YvetteFrance
  3. 3.INRIA/CEA Parietal team, NeuroSpinGif-sur-YvetteFrance

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