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Updated: May 28, 2026
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
Generalized likelihood ratio tests for change detection in diffusion tensor images: application to multiple sclerosis
Hervé Boisgontier1, Vincent Noblet, Fabrice Heitz
1University 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.
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.
Insights
This study introduces a new framework for analyzing changes in Diffusion Tensor Imaging (DTI) scans, offering complementary insights for monitoring Multiple Sclerosis (MS) progression.
Area of Science:
- Medical Imaging
- Neuroimaging
- Biophysics
Background:
- Subtle changes in serial MRI scans are crucial for disease monitoring.
- Diffusion Tensor Imaging (DTI) shows promise for neurodegenerative diseases like Multiple Sclerosis (MS).
- Few methods exist for change detection specifically in DTI serial acquisitions.
Purpose of the Study:
- To develop a comprehensive framework for detecting changes between two DTI scans.
- To evaluate different levels of diffusion imaging representation for change detection.
- To apply a statistical method for analyzing DTI changes in MS patient follow-up.
Main Methods:
- A framework analyzing Apparent Diffusion Coefficient (ADC) images, diffusion tensor fields, and scalar diffusion property images.
- Utilizing the Generalized Likelihood Ratio Test (GLRT) for statistical change detection.
- Assuming a Gaussian diffusion model with additive Gaussian noise on ADCs.
Main Results:
- The proposed framework effectively detects changes across different DTI representations.
- Tests provide useful and complementary information for MS patient follow-up.
- Demonstrated ability on both synthetic and real patient imaging data.
Conclusions:
- The developed framework offers a robust method for analyzing DTI changes.
- This approach enhances the monitoring of neurodegenerative diseases, particularly MS.
- The complementary information from different DTI representations aids disease progression assessment.
