Abstract
Due to the complex noise structure of functional magnetic resonance imaging (fMRI) data, methods that rely on information within a single subject often results in unsatisfactory functional segmentation. We thus propose a new graph-theoretic method, “Group Random Walker” (GRW), that integrates group information in detecting single-subject activation. Specifically, we extend each subject’s neighborhood system in such a way that enables the states of both intra- and inter-subject neighbors to be regularized without having to establish a one-to-one voxel correspondence as required in standard fMRI group analysis. Also, the GRW formulation provides an exact, unique closed-form solution for jointly estimating the probabilistic activation maps of all subjects with global optimality guaranteed. Validation is performed on synthetic and real data to demonstrate GRW’s superior detection power over standard analysis methods.
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Ng, B., Hamarneh, G., Abugharbieh, R. (2010). Detecting Brain Activation in fMRI Using Group Random Walker . In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6362. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15745-5_41
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DOI: https://doi.org/10.1007/978-3-642-15745-5_41
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