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Robust Extrapolation Scheme for Fast Estimation of 3D Ising Field Partition Functions: Application to Within-Subject fMRI Data Analysis

Abstract : In this paper, we present a first numerical scheme to estimate Partition Functions (PF) of 3D Ising fields. Our strategy is applied to the context of the joint detection-estimation of brain activity from functional Magnetic Resonance Imaging (fMRI) data, where the goal is to automatically recover activated regions and estimate region-dependent, hemodynamic filters. For any region, a specific binary Markov random field may embody spatial correlation over the hidden states of the voxels by modeling whether they are activated or not. To make this spatial regularization fully adaptive, our approach is first based upon it, classical path-sampling method to approximate a small subset of reference PFs corresponding to prespecified regions. Then, file proposed extrapolation method allows its to approximate the PFs associated with the Ising fields defined over the remaining brain regions. In comparison with preexisting approaches, our method is robust; to topological inhomogeneities in the definition of the reference regions. As a result, it strongly alleviates the computational burden and makes spatially adaptive regularization of whole brain fMRI datasets feasible.
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https://hal-cea.archives-ouvertes.fr/cea-00470658
Contributor : Philippe Ciuciu <>
Submitted on : Wednesday, April 7, 2010 - 11:16:06 AM
Last modification on : Monday, June 15, 2020 - 6:21:47 PM
Long-term archiving on: : Thursday, July 8, 2010 - 8:43:39 PM

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Laurent Risser, Thomas Vincent, Philippe Ciuciu, J. Idier. Robust Extrapolation Scheme for Fast Estimation of 3D Ising Field Partition Functions: Application to Within-Subject fMRI Data Analysis. 12th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2009), Sep 2009, Londres, United Kingdom. pp.975-983. ⟨cea-00470658⟩

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