Technical Program

Paper Detail

Paper:	SA-AM-OS4.1
Session:	Diffusion Tensor Imaging
Time:	Saturday, April 8, 11:10 - 11:30
Presentation:	Oral
Title:	Clinical DT-MRI Estimation, Smoothing and Fiber Tracking with Log-Euclidean Metrics
Authors:	Pierre Fillard; INRIA Epidaure/Asclepios Project
	Vincent Arsigny; INRIA Epidaure/Asclepios Project
	Xavier Pennec; INRIA Epidaure/Asclepios Project
	Nicholas Ayache; INRIA Epidaure/Asclepios Project
Abstract:	Diffusion tensor MRI is an imaging modality that is gaining importance in clinical applications. However, in a clinical environment, data have to be acquired rapidly, often at the detriment of the image quality. We propose a new variational framework that specifically targets low quality DT-MRI. The Rician nature of the noise on the images leads us to a maximum likelihood strategy to estimate the tensor field. To further reduce the noise, we optimally exploit the spatial correlation by adding to the estimation an anisotropic regularization term. This criterion is easily optimized thanks to the use of the recently introduced Log-Euclidean metrics. Results on real clinical data show promising improvements of fiber tracking in the brain and the spinal cord.

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