Image registration framework for large-scale longitudinal MRI data sets: strategy and validation.
Advanced magnetic resonance imaging (MRI) studies often require the transformation of large numbers of images into a common space. Calculating transformations that relate each image to every other and applying them to the images on demand are theoretically possible; however, these can be computationally prohibitive. Therefore, relating each image to only one other image, then linking those transforms together to relate any two images in the database, may be an efficient alternative. Evaluated were the feasibility and validity of image registration to bring intraindividual MR images into mutual correspondence for longitudinal analysis through the concatenation of precomputed transforms. A longitudinal data set of 10 multiple sclerosis patients with nine serial dual-echo spin-echo, 1.5-T MRI scans was used. Intrasubject registrations were performed stepwise between consecutive images and direct from each time point to the baseline. Consecutive transforms were concatenated and evaluated against direct registrations by comparing the resulting transformed images (using Pearson correlation coefficient). Confounding variables such as time between scans, brain atrophy, and change in lesion load were evaluated. We found the images resampled with the direct and the concatenated transforms to be highly correlated, and there was no significant difference between methods. Differences in brain parenchymal fraction (a measure of brain atrophy) showed significant inverse correlation with the correspondence of the resampled images. Results indicate that concatenating multiple transforms that link two images together produces near-identical results to that of direct registration; thus, this method is both useful and valid.
Csapo
Istvan
I
Center for Neurological Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
Holland
Christopher M
CM
Guttmann
Charles R G
CR
D000328:Adult; D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping; D003627:Data Interpretation, Statistical; D017352:Echo-Planar Imaging / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D015233:Models, Statistical; D009103:Multiple Sclerosis / Q000175:diagnosis* / Q000473:pathology*; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results