Enhancing the Multivariate Signal of 15O water PET Studies With a New Non-Linear Neuroanatomical Registration Algorithm

Ulrik Kjems, Stephen C. Storther, Jon Anderson, Ian Law, Lars Kai Hansen

AbstractThis paper addresses the problem of neuro-anatomical registration across individuals for functional [15O]water PET activation studies. A new algorithm for 3D non-linear structural registration (warping) of MR scans is presented. The method performs a hierarchically scaled search for a displacement field
maximizing one of several voxel similarity measures derived from the two dimensional histogram of matched image intensities, subject to a regularizer that ensures smoothness of the displacement field. The effect of the non-line
ar structural registration is studied when it is computed on anatomical MR scans
and applied to co-registered [15O] water PET scans from the same subjects; in this experiment a study of visually guided saccadic eye movements.

The performance of the non-linear warp is evaluated using multivariate functional signal and noise measures. These measures prove to be useful for comparing different inter-subject registration approaches, e.g. affine versus non-linear.

A comparison of 12-parameter affine registration versus non-linear registration demonstrates that the proposed non-linear method increases the number of voxels retained in the cross-subject mask. We demonstrate that improved structural registration may result in an improved multivariate functional signal-tonoise ratio. Furthermore registration of PET scans using the 12-parameter affine transformations that align the co-registered MR images does not improve registration compared to 12-parameter affine alignment of the PET images directly.
Keywordsimage registration, warping, mutual information, Non-linear warping, stereo-tactic registration, inter-subject registration, voxel similarity measures.
TypeJournal paper [With referee]
JournalIEEE Transactions on Medical Imaging
Year1999    Month April    Vol. 18    No. 4    pp. 306-319
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BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing