Generalized likelihood ratio tests for change detection in diffusion tensor images: application to multiple sclerosis.
Fiche publication
Date publication
janvier 2012
Journal
Medical image analysis
Auteurs
Membres identifiés du Cancéropôle Est :
Pr HEITZ Fabrice, Dr NOBLET Vincent
Tous les auteurs :
Boisgontier H, Noblet V, Heitz F, Rumbach L, Armspach JP
Lien Pubmed
Résumé
The automatic analysis of subtle changes between MRI scans is an important tool for monitoring disease evolution. Several methods have been proposed to detect changes in serial conventional MRI but few works have considered Diffusion Tensor Imaging (DTI), which is a promising modality for monitoring neurodegenerative disease and particularly Multiple Sclerosis (MS). In this paper, we introduce a comprehensive framework for detecting changes between two DTI acquisitions by considering different levels of representation of diffusion imaging, namely the Apparent Diffusion Coefficient (ADC) images, the diffusion tensor fields, and scalar images characterizing diffusion properties such as the fractional anisotropy and the mean diffusivity. The proposed statistical method for change detection is based on the Generalized Likelihood Ratio Test (GLRT) that has been derived for the different diffusion imaging representations, based on the core assumption of a Gaussian diffusion model and of an additive Gaussian noise on the ADCs. Results on synthetic and real images demonstrate the ability of the different tests to bring useful and complementary information in the context of the follow-up of MS patients.
Mots clés
Algorithms, Brain, pathology, Diffusion Magnetic Resonance Imaging, methods, Humans, Image Enhancement, methods, Image Interpretation, Computer-Assisted, methods, Multiple Sclerosis, pathology, Pattern Recognition, Automated, methods, Reproducibility of Results, Sensitivity and Specificity
Référence
Med Image Anal. 2012 Jan;16(1):325-38