Probabilitic fusion of hemodynamic parameter maps.
Hemodynamic parameter maps (HPMs) depict values of parameters of hemodynamic response, namely, gain, lag, and dispersion, in response to neuronal activation. Large intensity values of any of the HPMs indicate greater likelihoods of brain activation. We apply the probabilistic data fusion equation to combine three HPMs. The statistical map of the fused probability map derived from HPMs is compared with statistical parameter mapping (SPM) in a visual experiment. The statistical hemodynamic parameter map (SHPM) gives better contrast to distinguish activated voxels from nonactivated voxels.
Rajapakse
J C
JC
School of Applied Science, Nanyang Technological University, Singapore.
D001931:Brain Mapping; D002560:Cerebrovascular Circulation; D005583:Fourier Analysis; D006439:Hemodynamics; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D015233:Models, Statistical; D011336:Probability