P. Basser, J. Mattiello, and D. Lebihan, Estimation of the Effective Self-Diffusion Tensor from the NMR Spin Echo, Journal of Magnetic Resonance, Series B, vol.103, issue.3, pp.247-254, 1994.
DOI : 10.1006/jmrb.1994.1037

URL : https://hal.archives-ouvertes.fr/hal-00349722

B. C. Vemuri, Y. Chen, M. Rao, T. Mcgraw, Z. Wang et al., Fiber tract mapping from diffusion tensor MRI, Proceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision, 2001.
DOI : 10.1109/VLSM.2001.938885

URL : http://ufdc.ufl.edu/UF00095471/00001/pdf

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

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

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

A. Mishra, Y. Lu, J. Meng, A. W. Anderson, and Z. Ding, Unified framework for anisotropic interpolation and smoothing of diffusion tensor images, NeuroImage, vol.31, issue.4, pp.1525-1535, 2006.
DOI : 10.1016/j.neuroimage.2006.02.031

C. Poupon, A. Roche, J. Dubois, J. F. Mangin, and F. Poupon, Real-time MR diffusion tensor and Q-ball imaging using Kalman filtering, Medical Image Analysis, vol.12, issue.5, pp.527-534, 2008.
DOI : 10.1016/j.media.2008.06.004

S. Tang, Y. Fan, H. Zhu, P. Yap, W. Gao et al., Regularization of diffusion tensor field using coupled robust anisotropic diffusion, Mathematical Methods in Biomedical Image Analysis, pp.52-57, 2009.

A. Tristán-vega and S. Aja-fernández, DWI filtering using joint information for DTI and HARDI, Medical Image Analysis, vol.14, issue.2, pp.205-218, 2010.
DOI : 10.1016/j.media.2009.11.001

E. O. Stejskal and J. E. Tanner, Spin Diffusion Measurements: Spin Echoes in the Presence of a Time???Dependent Field Gradient, The Journal of Chemical Physics, vol.42, issue.1, 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

P. G. Batchelor, M. Moakher, D. Atkinson, F. Calamante, and A. Connelly, A rigorous framework for diffusion tensor calculus, Magnetic Resonance in Medicine, vol.103, issue.1, pp.221-225, 2005.
DOI : 10.1002/mrm.20334

P. Fillard, X. Pennec, V. Arsigny, and N. Ayache, Clinical DT-MRI Estimation, Smoothing, and Fiber Tracking With Log-Euclidean Metrics, IEEE Transactions on Medical Imaging, vol.26, issue.11, pp.1472-1482, 2007.
DOI : 10.1109/TMI.2007.899173

URL : https://hal.archives-ouvertes.fr/inria-00502645

G. Hamarneh and J. Hradsky, Bilateral Filtering of Diffusion Tensor Magnetic Resonance Images, IEEE Transactions on Image Processing, vol.16, issue.10, pp.2463-2475, 2007.
DOI : 10.1109/TIP.2007.904964

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

C. F. Westin and S. Maier, A dual tensor basis solution to the Stejskal-Tanner equations for DT-MRI, International Society of Magnetic Resonance in Medicine (ISMRM), 2002.

C. G. Koay and P. J. Basser, Analytically exact correction scheme for signal extraction from noisy magnitude MR signals, Journal of Magnetic Resonance, vol.179, issue.2, pp.317-322, 2006.
DOI : 10.1016/j.jmr.2006.01.016

M. Descoteaux, N. Wiest-daessl, S. Prima, C. Barillot, and R. Deriche, Impact of Rician Adapted Non-Local Means Filtering on HARDI, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), pp.122-130, 2008.
DOI : 10.1007/978-3-540-85990-1_15

URL : https://hal.archives-ouvertes.fr/inria-00423318

P. Coupé, P. Yger, and C. Barillot, Fast Non Local Means Denoising for 3D MR Images, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), pp.33-40, 2006.
DOI : 10.1007/11866763_5

N. Wiest-daesslé, S. Prima, P. Coupé, S. P. Morrissey, and C. Barillot, Non-Local Means Variants for Denoising of Diffusion-Weighted and Diffusion Tensor MRI, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), vol.10, issue.2, pp.344-351, 2007.
DOI : 10.1007/978-3-540-75759-7_42

N. Wiest-daesslé, S. Prima, P. Coupe, S. P. Morrissey, and C. Barillot, Rician Noise Removal by Non-Local Means Filtering for Low Signal-to-Noise Ratio MRI: Applications to DT-MRI, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), vol.11, issue.2, pp.171-179, 2008.
DOI : 10.1007/978-3-540-85990-1_21

C. 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.217-352, 2007.
DOI : 10.1016/j.sigpro.2006.02.049

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

P. Fillard, V. Arsigny, X. Pennec, P. M. Thompson, and N. Ayache, Extrapolation of Sparse Tensor Fields: Application to the Modeling of Brain Variability, Information Processing in Medical Imaging, vol.19, pp.27-38, 2005.
DOI : 10.1007/11505730_3

URL : https://hal.archives-ouvertes.fr/inria-00502688

B. C. Vemuri, M. Liu, S. I. Amari, and F. Nielsen, Total Bregman Divergence and Its Applications to DTI Analysis, IEEE Transactions on Medical Imaging, vol.30, issue.2, pp.475-483, 2011.
DOI : 10.1109/TMI.2010.2086464

M. Liu, B. C. Vemuri, S. I. Amari, and F. Nielsen, Total Bregman divergence and its applications to shape retrieval, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.3463-3468, 2010.
DOI : 10.1109/CVPR.2010.5539979

M. Liu and B. C. Vemuri, Robust and efficient regularized boosting using total Bregman divergence, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995686

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3860759