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Robust voxel-wise Joint Detection Estimation of Brain Activity in fMRI

Lotfi Chaari 1 Florence Forbes 1 Thomas Vincent 1 Philippe Ciuciu 2
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann, Inria Grenoble - Rhône-Alpes
2 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
Inria Saclay - Ile de France, NEUROSPIN - Service NEUROSPIN
Abstract : We address the issue of jointly detecting brain activity and estimating brain hemodynamics from functional MRI data. To this end, we adopt the so-called Joint-Detection-Estimation (JDE) framework introduced in [ Makni et al, NeuroImage 2008] and augmented in [Vincent et al, IEEE TMI 2010]. An inherent difficulty is to find the right spatial scale at which brain hemodynamics estimation makes sense. The voxel level is clearly not appropriate as estimating a full hemodynamic response function (HRF) from a single voxel time course may suffer from a poor signal-to-noise-ratio and lead to potentially misleading results (non-physiological HRF shapes). More robust estimation can be obtained by considering groups of voxels ( i.e. parcels) with some functional homogeneity properties. Current JDE approaches are therefore based on an initial parcellation but with no guarantee of its optimality or goodness. In this work, we propose a joint parcellation-detection-estimation (JPDE) procedure that incorporates an additional parcel estimation step solving this way both the parcellation choice and robust HRF estimation issues. As in [Chaari et al, MICCAI 2011], inference is carried out in a Bayesian setting using variational approximation techniques for computational efficiency.
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Submitted on : Saturday, September 7, 2013 - 4:50:26 PM
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Lotfi Chaari, Florence Forbes, Thomas Vincent, Philippe Ciuciu. Robust voxel-wise Joint Detection Estimation of Brain Activity in fMRI. ICIP 2012 - 19th IEEE International Conference on Image Processing, Sep 2012, Orlando, United States. pp.1273-1276, ⟨10.1109/ICIP.2012.6467099⟩. ⟨hal-00859386⟩



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