Detecting Brain Activation in fMRI Using Group Random Walker
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 subjects 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 GRWs superior detection power over standard analysis methods.
fMRI graphical models group analysis random walker
Bernard Ng Ghassan Hamarneh Rafeef Abugharbieh
Biomedical Signal and Image Computing Lab, The University of British Columbia Medical Image Analysis Lab, Simon Fraser University
国际会议
北京
英文
331-338
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）