Registration and Analysis of White Matter Group Differences with a Multi-Fiber Model

Maxime Taquet 1, 2, * Benoit Scherrer 1 Olivier Commowick 3 Jurriaan Peters 4, 1 Mustafa Sahin 4 Benoît Macq 2 Simon Warfield 1
* Corresponding author
1 CRL - Computational Radiology Laboratory [Boston]
2 ICTEAM - Institute of Information and Communication Technologies, Electronics and Applied Mathematics
3 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
4 Department of Neurology, Children's Hospital [Boston]
Abstract : iffusion magnetic resonance imaging has been used exten- sively to probe the white matter in vivo. Typically, the raw diffusion images are used to reconstruct a diffusion tensor image (DTI). The incapacity of DTI to represent crossing fibers leaded to the development of more sophisticated diffusion models. Among them, multi-fiber models represent each fiber bundle independently, allowing the direct extraction of diffusion features for population analysis. However, no method exists to properly register multi-fiber models, seriously limiting their use in group comparisons. This paper presents a registration and atlas construction method for multi-fiber models. The validity of the registration is demonstrated on a dataset of 45 subjects, including both healthy and unhealthy subjects. Morphometry analysis and tract-based statistics are then carried out, proving that multi-fiber models registration is better at detecting white matter local differences than single tensor registration.
keyword : Di usion Imaging Multi-Fiber Models Registration White Matter
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Submitted on : Sunday, October 14, 2012 - 5:30:21 PM
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Maxime Taquet, Benoit Scherrer, Olivier Commowick, Jurriaan Peters, Mustafa Sahin, et al.. Registration and Analysis of White Matter Group Differences with a Multi-Fiber Model. 15th International Conference on Medical Image Computing and Computer Assisted Intervention, Oct 2012, Nice, France. pp.313-320. ⟨inserm-00716110⟩

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