Voxel-based quantitative analysis of brain images from (1)(8)F-FDG PET with a block-matching algorithm for spatial normalization.
Fiche publication
Date publication
mars 2012
Auteurs
Membres identifiés du Cancéropôle Est :
Pr KARCHER Gilles, Pr WOLF Didier
Tous les auteurs :
Person C, Louis-Dorr V, Poussier S, Commowick O, Malandain G, Maillard L, Wolf D, Gillet N, Roch V, Karcher G, Marie PY
Lien Pubmed
Résumé
OBJECTIVE: Statistical Parametric Mapping (SPM) is widely used for the quantitative analysis of brain images from (1)(8)F fluorodeoxyglucose positron emission tomography (FDG PET). SPM requires an initial step of spatial normalization to align all images to a standard anatomic model (the template), but this may lead to image distortion and artifacts, especially in cases of marked brain abnormalities. This study aimed at assessing a block-matching (BM) normalization algorithm, where most transformations are not directly computed on the overall brain volume but through small blocks, a principle that is likely to minimize artifacts. METHODS: Large and/or small hypometabolic areas were artificially simulated in initially normal FDG PET images to compare the results provided by statistical tests computed after either SPM or BM normalization. RESULTS: Results were enhanced by BM, compared with SPM, with regard to (i) errors in the estimation of large defects volumes (about 2-fold lower) because of a lower image distortion, and (ii) rates of false-positive foci when numerous or extended abnormalities were simulated. These observations were strengthened by analyses of FDG PET examinations from epileptic patients. CONCLUSIONS: Results obtained with the BM normalization of brain FDG PET appear more precise and robust than with SPM normalization, especially in cases of numerous or extended abnormalities.
Référence
Clin Nucl Med. 2012 Mar;37(3):268-73.