Abstract : We address the issue of jointly detecting brain activity and estimating underlying brain hemodynamics from functional MRI data. We adopt the so-called Joint Detection Estimation (JDE) framework that takes spatial dependencies between voxels into account. We recast the JDE into a missing data framework and derive a Variational Expectation-Maximization (VEM) algorithm for its inference. It follows a new algorithm that has interesting advantages over the previously used intensive simulation methods (Markov Chain Monte Carlo, MCMC): tests on artificial data show that the VEM-JDE is more robust to model mis-specification while additional tests on real data confirm that it achieves similar performance in much less computation time.
https://www.hal.inserm.fr/inserm-00635384
Contributor : Michel Dojat
<>
Submitted on : Tuesday, October 25, 2011 - 11:07:37 AM
Last modification on : Thursday, March 14, 2019 - 7:54:04 AM
Document(s) archivé(s) le : Monday, December 5, 2016 - 12:57:34 AM
File
Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed
until : jamais
Lotfi Chaari, Florence Forbes, Thomas Vincent, Michel Dojat, Philippe Ciuciu. Variational solution to the joint detection estimation of brain activity in fMRI. MICCAI 2011 - 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, Sep 2011, Toronto, Canada. pp.260-268, ⟨10.1007/978-3-642-23629-7_32⟩. ⟨inserm-00635384⟩
