Statistical detection of longitudinal changes between apparent diffusion coefficient images: application to multiple sclerosis.
The automatic analysis of longitudinal changes between Diffusion Tensor Imaging (DTI) acquisitions is a promising tool for monitoring disease evolution. However, few works address this issue and existing methods are generally limited to the detection of changes between scalar images characterizing diffusion properties, such as Fractional Anisotropy or Mean Diffusivity, while richer information can be exploited from the whole set of Apparent Diffusion Coefficient (ADC) images that can be derived from a DTI acquisition. In this paper, we present a general framework for detecting changes between two sets of ADC images and we investigate the performance of four statistical tests. Results are presented on both simulated and real data in the context of the follow-up of multiple sclerosis lesion evolution.
Boisgontier
Hervé
H
Université de Strasbourg, CNRS, UMR 7005, LSIIT, France. h.boisgontier@unistra.fr
Noblet
Vincent
V
Renard
Félix
F
Heitz
Fabrice
F
Rumbach
Lucien
L
Armspach
Jean-Paul
JP
D000465:Algorithms; D001921:Brain / Q000473:pathology*; D003627:Data Interpretation, Statistical; 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