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|
|Abstract||This 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.
|Keywords||image registration, warping, mutual information, Non-linear warping, stereo-tactic registration, inter-subject registration, voxel similarity measures.|
|Type||Journal paper [With referee]|
|Journal||IEEE Transactions on Medical Imaging|
|Year||1999 Month April Vol. 18 No. 4 pp. 306-319|
|Electronic version(s)||[pdf] [ps]|
|BibTeX data|| [bibtex]|
|IMM Group(s)||Intelligent Signal Processing|