Detection of brain activation from MRI data by likelihood-ratio test

Abstract

An image processing strategy for functional magnetic resonance imaging (FMRI) data set, consisting of K sequential images of the same slice of brain tissue, is considered. An algorithm of detection based on the likelihood-ratio test is introduced. The noise model and signal model are established by analysing the FMRI. Due to data having a poor signal-to-noise ratio, and also in order to make more reliable detection, the algorithm is carried out in two stages: coarse detection followed by a fine one. Jumps in mean from non stimulation periods to stimulation ones in the time-course series data are used as decision criteria. The detection method is applied to experimental FMRI data from the motor cortex and compared with the cross-correlation method and Student’s t-test.