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.
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Ruan, S., Jaggi, C., Constans, J.M., Bloyet, D. (1995). Detection of Brain Activation from MRI Data by Likelihood-Ratio Test. In: Ayache, N. (eds) Computer Vision, Virtual Reality and Robotics in Medicine. CVRMed 1995. Lecture Notes in Computer Science, vol 905. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-49197-2_43
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DOI: https://doi.org/10.1007/978-3-540-49197-2_43
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