@article {Wenzel403P, author = {Fabian Wenzel and Stewart Young and Florian Wilke and Ralph Buchert}, title = {A quantitative comparison of spatial normalization techniques for statistical analysis using FDG-PET brain scans}, volume = {48}, number = {supplement 2}, pages = {403P--403P}, year = {2007}, publisher = {Society of Nuclear Medicine}, abstract = {1695 Objectives: Spatial normalization is an essential preprocessing step in voxel-based statistical analysis of brain FDG-PET images. The normalization technique can have significant impact on the results of statistical analysis, with a potential impact on clinical diagnosis. The present study aimed at a quantitative comparison of different spatial normalization methods using both synthetic ground-truth and visual evaluation by an expert (as documented in the medical record) as gold standard. Methods: 116 subjects were included retrospectively in the analysis. Based on visual evaluation by an expert reader the brain FDG-PET scans of these subjects had been classified as Alzheimer{\textquoteright}s disease (AD, n=49), cortico-basal disease (CBD, n=9), fronto-temporal disease (FTD, n=18), Lewy-body disease (LBD, n=14), and normal (n=26, also serving as normal controls for subsequent two-sample t-tests). In addition, subjects with artificial lesions were simulated by inserting (i) 3 artificial patterns of hypo-metabolism based on anatomical structures as well as (ii) typical AD and FTD patterns on the 26 normal patients (SGT, n=5x26). Each image was spatially normalized using SPM, NeuroStat and a B-spline based technique. The results of statistical analysis were evaluated by computing the rates of correctly and incorrectly detected hypometabolic voxels (for all SGT images) and by classification of the pattern of hypometablism as normal, AD, CBD, FTD, or LBD based on visual evaluation (of a subset of 375 images). Results: The rates of correctly and incorrectly detected hypometabolic voxels with respect to the synthetic ground-truth revealed differences between normalization techniques with an average correlation coefficient of 0.8, varying with the area used as synthetic lesion. Pattern classification by visual evaluation demonstrated an average sensitivity of 80\% with subtle differences between normalization techniques. Conclusions: A method is presented enabling direct quantitative comparison of spatial normalization techniques. It could be observed that the dependence of voxel-based statistical analysis on the normalization technique varies considerably between brain regions. All tested techniques provide comparable results.}, issn = {0161-5505}, URL = {https://jnm.snmjournals.org/content/48/supplement_2/403P.4}, eprint = {https://jnm.snmjournals.org/content}, journal = {Journal of Nuclear Medicine} }