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


This paper addresses the problem of neuro-anatomical registration across individuals for functional [15O] water PET activation studies. A new algorithm for three-dimensional (3-D) nonlinear 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 (2-D) histogram of matched image intensities, subject to a regularizer that ensures smoothness of the displacement field. The effect of the nonlinear structural registration is studied when it is computed on anatomical MR scans and applied to coregistered [15O] water PET scans from the same subjects: in this experiment, a study of visually guided saccadic eye movements. The performance of the nonlinear warp is evaluated using multivariate functional signal and noise measures. These measures prove to be useful for comparing different intersubject registration approaches, e.g., affine versus nonlinear. A comparison of 12-parameter affine registration versus non-linear registration demonstrates that the proposed nonlinear 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-to-noise ratio (SNR). Furthermore, registration of PET scans using the 12-parameter affine transformations that align the coregistered MR images does not improve registration, compared to 12-parameter affine alignment of the PET images directly.

DOI: 10.1109/42.768840


Citations per Year

86 Citations

Semantic Scholar estimates that this publication has 86 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Kjems1999EnhancingTM, title={Enhancing the Multivariate Signal of [15O] water PET Studies with a New Non-Linear Neuroanatomical Registration Algorithm}, author={Ulrik Kjems and Stephen C. Strother and Jon R. Anderson and Ian Law and Lars Kai Hansen}, journal={IEEE transactions on medical imaging}, year={1999}, volume={18 4}, pages={306-19} }