title: Generalized likelihood ratio tests for change detection in diffusion tensor images: application to multiple sclerosis.
abstract: 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.
authors.affiliation: University of Strasbourg, CNRS, Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection, UMR 7005, Bd Sébastien Brant, 67412 Illkirch Cedex, France.
authors.forename: Hervé
authors.initials: H
authors.lastname: Boisgontier
authors.forename: Vincent
authors.initials: V
authors.lastname: Noblet
authors.forename: Fabrice
authors.initials: F
authors.lastname: Heitz
authors.forename: Lucien
authors.initials: L
authors.lastname: Rumbach
authors.forename: Jean-Paul
authors.initials: JP
authors.lastname: Armspach
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009103:Multiple Sclerosis / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity