J. Sijbers, Signal and noise estimation from magnetic resonance images, 1998.

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

P. Fillard, . Arsigny, N. Pennec, and . Ayache, Clinical DT-MRI estimation, smoothing and fiber tracking with Log-Euclidean metrics, IEEE Transactions on Medical Imaging, 2007.
DOI : 10.1109/isbi.2006.1625034

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

D. Le-bihan, . Breton, . Lallemand, . Grenier, and . Cabanis, MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders., Radiology, vol.161, issue.2, pp.401-407, 1986.
DOI : 10.1148/radiology.161.2.3763909

URL : https://hal.archives-ouvertes.fr/hal-00349714

P. Basser, D. Mattiello, and . Lebihan, MR diffusion tensor spectroscopy and imaging, Biophysical Journal, vol.66, issue.1, pp.259-267, 1994.
DOI : 10.1016/S0006-3495(94)80775-1

URL : https://hal.archives-ouvertes.fr/hal-00349721

K. Hasan, D. Parker, and A. Alexander, Comparison of gradient encoding schemes for diffusion-tensor MRI, Journal of Magnetic Resonance Imaging, vol.15, issue.5, pp.769-780, 2001.
DOI : 10.1002/jmri.1107

R. Rathore, Necessary and sufficient conditions for the admissibility of dti gradient vectors, Proceedings of the Annual Meeting of the International Society for Magnetic Resonance in Medicine, 2007.

P. Bevington, Data Reduction and Error Analysis for the Physical Sciences, Computers in Physics, vol.7, issue.4, 1969.
DOI : 10.1063/1.4823194

Z. Wang, . Vemuri, T. Chen, and . 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

S. Kay, Fundamentals of statistical signal processing: estimation theory, 1993.

C. Lenglet, Geometric and Variational Methods for Diffusion Tensor MRI Processing, 2006.
URL : https://hal.archives-ouvertes.fr/tel-00457463

D. Tuch, Diffusion MRI of Complex Tissue Structure. PhD thesis, massachusetts institute of technology, 2002.

M. Descoteaux, . Angelino, R. Fitzgibbons, and . Deriche, Regularized, fast and robust analytical q-ball imaging transformation (linéaire) entre le modèle des fonctions harmoniques sphériques et le tenseur d'ordre supérieur. Ceci permetégalementpermetégalement d'utiliser les mesures d'anisotropie définies sur les tenseurs d'ordre supérieur pour le modèle des fonction harmoniques sphériques. Parmi les formalismes de modélisation pour le HARDI, on a ´ egalement cité l'imagerie qball, Magn. Res. in Med, 2007.

. Descoteaux, 17] dérivent uné elégante méthode pour le calcul de la transformée de Funk-Radon, pour aboutiràaboutirà une solution analytique et de faible complexité algorithmique. Le même schéma de régularisationrégularisationà base de l'opérateur de Laplace-Beltrami permet de stabiliser l'estimation de la direction d'une fibre

R. Basser, D. Mattiello, and . Lebihan, MR diffusion tensor spectroscopy and imaging, Biophysical Journal, vol.66, issue.1, pp.259-267, 1994.
DOI : 10.1016/S0006-3495(94)80775-1

URL : https://hal.archives-ouvertes.fr/hal-00349721

C. , C. , and D. Le-bihan, Water diffusion compartmentation and anisotropy at high b values in the human brain, Magn Reson Med, vol.44, issue.6, pp.852-859, 2000.
URL : https://hal.archives-ouvertes.fr/hal-00349825

D. Alexander, G. J. Barker, and S. Arridge, Detection and modeling of non-Gaussian apparent diffusion coefficient profiles in human brain data, Magnetic Resonance in Medicine, vol.46, issue.2, pp.331-340, 2002.
DOI : 10.1002/mrm.10209

M. Descoteaux, E. Angelino, S. Fitzgibbons, and R. Deriche, Apparent diffusion coefficients from high angular resolution diffusion imaging: Estimation and applications, Magnetic Resonance in Medicine, vol.50, issue.2, pp.395-410, 2006.
DOI : 10.1002/mrm.20948

J. Sijbers, Signal and noise estimation from magnetic resonance images, 1998.

C. Lenglet, Geometric and Variational Methods for Diffusion Tensor MRI Processing, 2006.
URL : https://hal.archives-ouvertes.fr/tel-00457463

J. Sijbers, A. J. Dekker, . Scheunders, and . Van-dyck, Maximum-likelihood estimation of Rician distribution parameters, IEEE Transactions on Medical Imaging, vol.17, issue.3, pp.357-361, 1998.
DOI : 10.1109/42.712125

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

N. Papadakis, C. Xing, L. Huang, T. Hall, and . Carpenter, A Comparative Study of Acquisition Schemes for Diffusion Tensor Imaging Using MRI, Journal of Magnetic Resonance, vol.137, issue.1, pp.67-82, 1999.
DOI : 10.1006/jmre.1998.1673

K. Hasan, D. Parker, and A. Alexander, Comparison of gradient encoding schemes for diffusion-tensor MRI, Journal of Magnetic Resonance Imaging, vol.15, issue.5, pp.769-780, 2001.
DOI : 10.1002/jmri.1107

J. Peter, S. Basser, and . Pajevic, Dealing with uncertainty in diffusion tensor MR data, Israel Journal of Chemistry, vol.43, pp.129-144, 2003.

J. Mangin, C. Poupon, C. A. Clark, D. L. Bihan, and I. Bloch, Eddycurrent distortion correction and robust tensor estimation for mr diffusion imaging, MICCAI '01 : Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.186-194, 2001.

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

C. Chefd-'hotel, D. Tschumperlé, R. Deriche, and O. Faugeras, Regularizing Flows for Constrained Matrix-Valued Images, Journal of Mathematical Imaging and Vision, vol.20, issue.1/2, pp.147-162, 2004.
DOI : 10.1023/B:JMIV.0000011324.14508.fb

P. Thomas-fletcher, C. Lu, M. Stephen, S. Pizer, and . Joshi, Principal Geodesic Analysis for the Study of Nonlinear Statistics of Shape, IEEE Transactions on Medical Imaging, vol.23, issue.8, pp.995-1005, 2004.
DOI : 10.1109/TMI.2004.831793

P. Fillard, V. Arsigny, N. Ayache, and X. Pennec, A Riemannian Framework for the Processing of Tensor-Valued Images, Deep Structure, Singularities, and Computer Vision (DSSCV), number 3753 in LNCS, pp.112-123, 2005.
DOI : 10.1007/11577812_10

URL : https://hal.archives-ouvertes.fr/inria-00615994

M. Descoteaux, E. Angelino, S. Fitzgibbons, and R. Deriche, A Fast and Robust ODF Estimation Algorithm in Q-Ball Imaging, 3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006., pp.81-84, 2006.
DOI : 10.1109/ISBI.2006.1624857