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Functional MRI (fMRI) time-series studies are plagued by varying degrees of subject head motion. Faithful headmotion correction is essential to accurately detect brain activation using statistical analyses of these time-series.Mutual information (MI) based slice-to-volume (SV) registration is used for motion estimation when the rate ofchange of head position is large. SV registration accounts for head motion between slice acquisitions by estimatingan independent rigid transformation for each slice in the time-series. Consequently each MI optimization usesintensity counts from a single time-series slice, making the algorithm susceptible to noise for low complexity endslices(i.e., slices near the top of the head scans). This work focuses on improving the accuracy of MI-based SVregistration of end-slices by using joint probability density priors derived from registered high complexity centerslices(i.e., slices near the middle of the head scans). Results show that the use of such priors can significantlyimprove SV registration accuracy

Improved fMRI time-series registration using joint probability density priors