V. Arsigny, P. Fillard, X. Pennec, and N. Ayache, Fast and Simple Calculus on Tensors in the Log-Euclidean Framework, MICCAI'05, pp.115-122, 2005.
DOI : 10.1007/11566465_15
URL : https://hal.archives-ouvertes.fr/inria-00502669

S. Basu, P. , T. Fletcher, and R. T. Whitaker, Rician Noise Removal in Diffusion Tensor MRI, MICCAI'2006, pp.117-125, 2006.
DOI : 10.1007/11866565_15

A. Buades, B. Coll, and J. M. , A Review of Image Denoising Algorithms, with a New One, Multiscale Modeling & Simulation, vol.4, issue.2, pp.490-530, 2005.
DOI : 10.1137/040616024
URL : https://hal.archives-ouvertes.fr/hal-00271141

B. Chen and E. Hsu, Pde denoising of MR diffusion tensor imaging data, ISBI'04, pp.1040-1042, 2004.

D. L. Collins, A. P. Zijdenbos, V. Kollokian, J. G. Sled, N. J. Kabani et al., Design and construction of a realistic digital brain phantom, IEEE Transactions on Medical Imaging, vol.17, issue.3, pp.17463-468, 1998.
DOI : 10.1109/42.712135

P. Coupé, P. Yger, and C. Barillot, Fast Non Local Means Denoising for 3D MR Images, MICCAI'2006, pp.33-40, 2006.
DOI : 10.1007/11866763_5

P. T. Fletcher and S. C. Joshi, Principal Geodesic Analysis on Symmetric Spaces: Statistics of Diffusion Tensors, CVAMIA and MMBIA 2004, pp.87-98, 2004.
DOI : 10.1007/978-3-540-27816-0_8

T. Gasser, L. Sroka, and C. Steinmetz, Residual variance and residual pattern in nonlinear regression, Biometrika, vol.73, issue.3, pp.625-633, 1986.
DOI : 10.1093/biomet/73.3.625

J. E. Lee, M. K. Chung, and A. L. Alexander, Evaluation of anisotropic filters for diffusion tensor imaging, Biomedical Imaging: Macro to Nano 3rd IEEE International Symposium on, pp.77-78, 2006.

J. Mangin, C. Poupon, C. Clark, D. L. Bihan, and I. Bloch, Distortion correction and robust tensor estimation for MR diffusion imaging, Medical Image Analysis, vol.6, issue.3, pp.191-198, 2002.
DOI : 10.1016/S1361-8415(02)00079-8
URL : https://hal.archives-ouvertes.fr/hal-00349706

S. Pajevic and C. Pierpaoli, Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: Application to white matter fiber tract mapping in the human brain, Magnetic Resonance in Medicine, vol.820, issue.3, pp.526-540, 1999.
DOI : 10.1002/(SICI)1522-2594(199909)42:3<526::AID-MRM15>3.0.CO;2-J

X. Pennec, P. Fillard, and N. Ayache, A Riemannian Framework for Tensor Computing, International Journal of Computer Vision, vol.6, issue.2, pp.41-66, 2006.
DOI : 10.1007/s11263-005-3222-z
URL : https://hal.archives-ouvertes.fr/inria-00070743

P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, issue.7, pp.629-639, 1990.
DOI : 10.1109/34.56205

W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, Numerical Recipes: The Art of Scientific Computing, 1992.

L. I. Rudin, S. Osher, and E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, vol.60, issue.1-4, pp.259-268, 1992.
DOI : 10.1016/0167-2789(92)90242-F

D. Tschumperlé and R. Deriche, Variational frameworks for DT-MRI estimation, regularization and visualization, Proceedings Ninth IEEE International Conference on Computer Vision, pp.116-121, 2003.
DOI : 10.1109/ICCV.2003.1238323

Z. Wang and B. C. Vemuri, DTI segmentation using an information theoretic tensor dissimilarity measure, IEEE Transactions on Medical Imaging, vol.24, issue.10, pp.1267-1277, 2005.
DOI : 10.1109/TMI.2005.854516

N. Wiest-daesslé, S. Prima, S. P. Morrissey, and C. Barillot, VALIDATION OF A NEW OPTIMISATION ALGORITHM FOR REGISTRATION TASKS IN MEDICAL IMAGING, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007.
DOI : 10.1109/ISBI.2007.356783