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Conference Papers Year : 2012

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


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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Dates and versions

hal-00859386 , version 1 (07-09-2013)



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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