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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)

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 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.

Keywords

Root Mean Square Error Markov Chain Monte Carlo Arterial Spin Label Hemodynamic Response Function Markov Chain Monte Carlo Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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