D. L. Bihan, J. F. Mangin, C. Poupon, C. A. Clark, S. Pappata et al., Diffusion tensor imaging: Concepts and applications, Journal of Magnetic Resonance Imaging, vol.44, issue.4, pp.534-546, 2001.
DOI : 10.1002/jmri.1076

URL : https://hal.archives-ouvertes.fr/hal-00349820

O. Coulon, D. C. Alexander, and S. Arridge, Diffusion tensor magnetic resonance image regularization, Medical Image Analysis, vol.8, issue.1, pp.47-67, 2004.
DOI : 10.1016/j.media.2003.06.002

S. Basu, P. T. Fletcher, and R. T. Whitaker, Rician Noise Removal in Diffusion Tensor MRI, In: MICCAI, issue.1, pp.117-125, 2006.
DOI : 10.1007/11866565_15

M. Martín-fernández, C. F. Westin, and C. Alberola-lópez, 3D Bayesian Regularization of Diffusion Tensor MRI Using Multivariate Gaussian Markov Random Fields, In: MICCAI, issue.1, pp.351-359, 2004.
DOI : 10.1007/978-3-540-30135-6_43

C. A. Castano-moraga, C. Lenglet, R. Deriche, and J. Ruiz-alzola, A Riemannian approach to anisotropic filtering of tensor fields, Signal Processing, vol.87, issue.2, pp.263-276, 2007.
DOI : 10.1016/j.sigpro.2006.02.049

URL : https://hal.archives-ouvertes.fr/inria-00426951

R. Deriche, D. Tschumperle, C. Lenglet, and M. Rousson, Variational approaches to the estimation, regularization and segmentation of diffusion tensor images, Mathematical Models in Computer Vision: The Handbook. 2005 edn, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00336537

Z. Wang, B. C. Vemuri, Y. Chen, and T. H. Mareci, A Constrained Variational Principle for Direct Estimation and Smoothing of the Diffusion Tensor Field From Complex DWI, IEEE Transactions on Medical Imaging, vol.23, issue.8, pp.930-939, 2004.
DOI : 10.1109/TMI.2004.831218

J. Weickert, C. Feddern, M. Welk, B. Burgeth, and T. Brox, PDEs for Tensor Image Processing, pp.399-414, 2006.
DOI : 10.1007/3-540-31272-2_25

P. Fillard, V. Arsigny, X. Pennec, and N. Ayache, Clinical DT-MRI estimation, smoothing and fiber tracking with log-Euclidean metrics, Proceedings of the IEEE International Symposium on Biomedical Imaging Crystal Gateway Marriott, pp.786-789, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00502645

E. Stejskal and J. Tanner, Spin Diffusion Measurements: Spin Echoes in the Presence of a Time???Dependent Field Gradient, The Journal of Chemical Physics, vol.42, issue.1, pp.288-292, 1965.
DOI : 10.1063/1.1695690

R. Salvador, A. Pea, D. K. Menon, T. A. Carpenter, J. D. Pickard et al., Formal characterization and extension of the linearized diffusion tensor model, Human Brain Mapping, vol.42, issue.2, pp.144-155, 2005.
DOI : 10.1002/hbm.20076

N. Azzabou, N. Paragios, F. Guichard, and F. Cao, Variable Bandwidth Image Denoising Using Image-based Noise Models, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383216

J. B. Hiriart-urruty and C. Lemaréchal, Fundamentals of Convex Analysis, 2001.
DOI : 10.1007/978-3-642-56468-0

D. P. Bertsekas, Nonlinear Programming, Athena Scientific, 1999.

C. J. Galban, S. Maderwald, K. Uffmann, A. De-greiff, and M. E. Ladd, Diffusive sensitivity to muscle architecture: a magnetic resonance diffusion tensor imaging study of the human calf, European Journal of Applied Physiology, vol.227, issue.3, pp.253-262, 2004.
DOI : 10.1007/s00421-004-1186-2

C. J. Galban, S. Maderwald, K. Uffmann, and M. E. Ladd, A diffusion tensor imaging analysis of gender differences in water diffusivity within human skeletal muscle, NMR in Biomedicine, vol.71, issue.8, 2005.
DOI : 10.1002/nbm.975

T. Joachims, Making large-scale support vector machine learning practical Advances in Kernel Methods: Support Vector Machines, 1998.