Variational solution to the joint detection estimation of brain activity in fMRI - Inserm - Institut national de la santé et de la recherche médicale Accéder directement au contenu
Communication Dans Un Congrès Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Année : 2011

Variational solution to the joint detection estimation of brain activity in fMRI

Résumé

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.
Fichier sous embargo
Fichier sous embargo
Date de visibilité indéterminée
Loading...

Dates et versions

inserm-00635384 , version 1 (25-10-2011)

Identifiants

Citer

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⟩
290 Consultations
2 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More