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Non-local robust detection of DTI white matter differences with small databases.

Olivier Commowick 1, * Aymeric Stamm 1 
* Corresponding author
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Diffusion imaging, through the study of water diffusion, allows for the characterization of brain white matter, both at the population and individual level. In recent years, it has been employed to detect brain abnormalities in patients suffering from a disease, e.g., from multiple sclerosis (MS). State-of-the-art methods usually utilize a database of matched (age, sex, ...) controls, registered onto a template, to test for differences in the patient white matter. Such approaches however suffer from two main drawbacks. First, registration algorithms are prone to local errors, thereby degrading the comparison results. Second, the database needs to be large enough to obtain reliable results. However, in medical imaging, such large databases are hardly available. In this paper, we propose a new method that addresses these two issues. It relies on the search for samples in a local neighborhood of each pixel to increase the size of the database. Then, we propose a new test based on these samples to perform a voxelwise comparison of a patient image with respect to a population of controls. We demonstrate on simulated and real MS patient data how such a framework allows for an improve detection power and a better robustness and reproducibility, even with a small database.
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Submitted on : Wednesday, October 10, 2012 - 9:33:21 PM
Last modification on : Thursday, January 20, 2022 - 4:19:53 PM
Long-term archiving on: : Friday, January 11, 2013 - 2:45:09 AM


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Olivier Commowick, Aymeric Stamm. Non-local robust detection of DTI white matter differences with small databases.. MICCAI 2012 - 15th International Conference on Medical Image Computing and Computer Assisted Intervention, Oct 2012, Nice, France. pp.476-84, ⟨10.1007/978-3-642-33454-2_59⟩. ⟨inserm-00716094⟩



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