Change detection in diffusion MRI using multivariate statistical testing on tensors.
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.
Grigis
Antoine
A
University of Strasbourg, CNRS, UMR 7005, LSIIT, France. grigis@unistra.fr
Noblet
Vincent
V
Renard
Félix
F
Heitz
Fabrice
F
Armspach
Jean-Paul
JP
Rumbach
Lucien
L
D000465:Algorithms; D001921:Brain / Q000473:pathology*; D003627:Data Interpretation, Statistical; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009103:Multiple Sclerosis / Q000473:pathology*; D015999:Multivariate Analysis; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity