Non-local robust detection of DTI white matter differences with small databases. - Archive ouverte HAL Access content directly
Conference Papers Year : 2012

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

(1) , (1)
1

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.
Fichier principal
Vignette du fichier
Miccai2012_NLMT.pdf (966.07 Ko) Télécharger le fichier
Vignette du fichier
inserm-00716094_edited.pdf (268.6 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Origin : Files produced by the author(s)

Dates and versions

inserm-00716094 , version 1 (10-10-2012)

Identifiers

Cite

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⟩
201 View
357 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More