Fakultät Informatik und Mathematik
Regensburg Center for Artificial Intelligence
Regensburg Center of Biomedical Engineering
Regensburg Center of Health Sciences and Technology

Prof. Dr. rer nat. Christoph Palm

Andreas Mang, Julia A. Schnabel, William R. Crum, Marc Modat, Oscar Camara-Rey, Christoph Palm, Gisele Brasil Caseiras, H. Rolf Jäger, Sébastien Ourselin, Thorsten M. Buzug, David J. Hawkes

The consistency of parametric registration in multi-temporal magnetic resonance (MR) imaging studies was evaluated.
Materials and methods
Serial MRI scans of adult patients with a brain tumor (glioma) were aligned by parametric registration. The performance of low-order spatial alignment (6/9/12 degrees of freedom) of different 3D serial MR-weighted images is evaluated. A registration protocol for the alignment of all images to one reference coordinate system at baseline is presented. Registration results were evaluated for both, multimodal intra-timepoint and mono-modal multi-temporal registration. The latter case might present a challenge to automatic intensity-based registration algorithms due to ill-defined correspondences. The performance of our algorithm was assessed by testing the inverse registration consistency. Four different similarity measures were evaluated to assess consistency.
Careful visual inspection suggests that images are well aligned, but their consistency may be imperfect. Sub-voxel inconsistency within the brain was found for allsimilarity measures used for parametric multi-temporal registration. T1-weighted images were most reliable for establishing spatial correspondence between different timepoints.
The parametric registration algorithm is feasible for use in this application. The sub-voxel resolution mean displacement error of registration transformations demonstrates that the algorithm converges to an almost identical solution for forward and reverse registration.