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Proceedings Paper

Improved fMRI time-series registration using joint probability density priors
Author(s): Roshni Bhagalia; Jeffrey A. Fessler; Boklye Kim; Charles R. Meyer
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Paper Abstract

Functional MRI (fMRI) time-series studies are plagued by varying degrees of subject head motion. Faithful head motion 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 of change of head position is large. SV registration accounts for head motion between slice acquisitions by estimating an independent rigid transformation for each slice in the time-series. Consequently each MI optimization uses intensity 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 SV registration 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 significantly improve SV registration accuracy.

Paper Details

Date Published: 27 March 2009
PDF: 9 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72590J (27 March 2009); doi: 10.1117/12.811421
Show Author Affiliations
Roshni Bhagalia, Univ. of Michigan (United States)
Jeffrey A. Fessler, Univ. of Michigan (United States)
Boklye Kim, Univ. of Michigan (United States)
Charles R. Meyer, Univ. of Michigan (United States)

Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)

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