Fiche publication
Date publication
janvier 2010
Journal
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Auteurs
Membres identifiés du Cancéropôle Est :
Pr HEITZ Fabrice
,
Dr NOBLET Vincent
Tous les auteurs :
Grigis A, Noblet V, Renard F, Heitz F, Armspach JP, Rumbach L
Lien Pubmed
Résumé
This paper presents a longitudinal change detection framework for detecting relevant modifications in diffusion MRI, with application to Multiple Sclerosis (MS). The proposed method is based on multivariate statistical testings which were initially introduced for tensor population comparison. We use these methods in the context of longitudinal change detection by considering several strategies to build sets of tensors characterizing the variability of each voxel. These testing tools have been considered either for the comparison of tensor eigenvalues or eigenvectors, thus enabling to differentiate orientation and diffusivity changes. Results on simulated MS lesion evolutions and on real data are presented. Interestingly, experiments on an MS patient highlight the ability of the proposed approach to detect changes in non evolving lesions (according to conventional MRI) and around lesions (in the normal appearing white matter), which might open promising perspectives for the follow-up of the MS pathology.
Mots clés
Algorithms, Brain, pathology, Data Interpretation, Statistical, Diffusion Tensor Imaging, methods, Humans, Image Enhancement, methods, Image Interpretation, Computer-Assisted, methods, Multiple Sclerosis, pathology, Multivariate Analysis, Pattern Recognition, Automated, methods, Reproducibility of Results, Sensitivity and Specificity
Référence
Med Image Comput Comput Assist Interv. 2010 ;13(Pt 2):117-24
