I. Aganj, C. Lenglet, G. Sapiro, E. Yacoub, K. Ugurbil et al., Reconstruction of the orientation distribution function in single-and multipleshell q-ball imaging within constant solid angle. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine, Society of Magnetic Resonance in Medicine, vol.64, pp.554-566, 2010.

A. L. Alexander, K. M. Hasan, M. Lazar, J. S. Tsuruda, and D. L. Parker, Analysis of partial volume effects in diffusion-tensor MRI, Magnetic Resonance in Medicine, vol.37, issue.5, pp.770-780, 2001.
DOI : 10.1002/mrm.1105

Y. Assaf, Can we use d i f f u s i o n M R I a s a b i o -marker of neurodegenerative processes? BioEssays : news and reviews in molecular, pp.1235-1245, 2008.

Y. Bai, X. Han, and J. L. Prince, Super-resolution reconstruction of MR brain images, Proc. of 38th Annual Conference on Information Sciences and Systems (CISS?Äô04), 2004.

J. Banerjee and C. V. Jawahar, Super-Resolution of Text Images Using Edge-Directed Tangent Field. Document Analysis Systems, pp.76-83, 2008.

P. J. Basser, J. Mattiello, and D. 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

A. Buades, B. Coll, and J. M. Morel, A Non-Local Algorithm for Image Denoising, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.60-65, 2005.
DOI : 10.1109/CVPR.2005.38

F. Calamante, S. H. Oh, J. D. Tournier, S. Y. Park, Y. D. Son et al., Super-resolution t r a c k -density imaging of thalamic substructures: Comparison with high-resolution anatomical magnetic resonance imaging at, 2012.

F. Calamante, J. D. Tournier, G. D. Jackson, and A. Connelly, Track-density imaging (TDI): Super-resolution white matter imaging using whole-brain track-density mapping, NeuroImage, vol.53, issue.4, pp.1233-1243, 2010.
DOI : 10.1016/j.neuroimage.2010.07.024

F. Calamante, J. D. Tournier, N. D. Kurniawan, Z. Yang, E. Gyengesi et al., Super-resolution track-density imaging studies of mouse brain: Comparison to histology, NeuroImage, vol.59, issue.1, pp.286-296, 2012.
DOI : 10.1016/j.neuroimage.2011.07.014

E. Carmi, S. Liu, N. Alon, A. Fiat, and D. Fiat, Resolution enhancement in MRI, Magnetic Resonance Imaging, vol.24, issue.2, pp.133-154, 2006.
DOI : 10.1016/j.mri.2005.09.011

M. Chamberland, D. Fortin, and M. Descoteaux, Real-time fiber tractography : Interactive parameter tuning for neurosurgical interventions, Proceeding of: Human Brain Mapping (HBM), 2012.

C. Clouchoux, P. Coupe, J. V. Manjon, N. Guizard, M. Bouyssi-kobar et al., A novel approach for high-resolution image reconstruction for in-vivo fetal brain MRI. Organization for Human Brain Mapping, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00645477

N. Tractometer-ayache, H. Delingette, P. Golland, and K. Mori, Online Evaluation System for Tractography, Medical Image Computing and Computer-Assisted Intervention ?Äì MICCAI 2012, pp.699-706

P. Coupe, S. F. Eskildsen, J. V. Manjon, V. S. Fonov, and D. L. Collins, Simultaneous segmentation and grading of anatomical structures for patient's classification: Application to Alzheimer's disease, NeuroImage, vol.59, issue.4, pp.3736-3747, 2012.
DOI : 10.1016/j.neuroimage.2011.10.080

P. Coupe, V. Fonov, J. V. Manjon, and D. L. Collins, Template Construction using a Patch-based Robust Estimator. Organization for Human Brain Mapping, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00645471

P. Coupe, P. Yger, S. Prima, P. Hellier, C. Kervrann et al., An Optimized Blockwise Nonlocal Means Denoising Filter for 3-D Magnetic Resonance Images, IEEE Transactions on Medical Imaging, vol.27, issue.4, pp.425-441, 2008.
DOI : 10.1109/TMI.2007.906087

URL : https://hal.archives-ouvertes.fr/inserm-00169658

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, pp.2080-2095, 2007.
DOI : 10.1109/TIP.2007.901238

M. Descoteaux, N. Wiest-daessle, S. Prima, C. Barillot, and R. Deriche, Impact of Rician adapted Non-Local Means filtering on HARDI. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical I m a g e C, pp.122-130, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00423318

T. Dyrby, H. Lundell, M. Liptrot, W. Burke, M. Ptito et al., Interpolation of DWI prior to DTI reconstruction, and its validation, 2011.

P. Fillard, M. Descoteaux, A. Goh, S. Gouttard, B. Jeurissen et al., Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom, NeuroImage, vol.56, issue.1, pp.220-234, 2011.
DOI : 10.1016/j.neuroimage.2011.01.032

URL : https://hal.archives-ouvertes.fr/inria-00559191

G. B. Frisoni, N. C. Fox, C. R. Jack, . Jr, P. Scheltens et al., The clinical use of structural MRI in Alzheimer disease, Nature Reviews Neurology, vol.15, issue.2, pp.67-77, 2010.
DOI : 10.1038/nrneurol.2009.215

X. Gao, K. Zhang, D. Tao, and X. Li, Image super-resolution with sparse neighbor embedding, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, pp.3194-3205, 2012.

G. Gattellaro, L. Minati, M. Grisoli, C. Mariani, F. Carella et al., White Matter Involvement in Idiopathic Parkinson Disease: A Diffusion Tensor Imaging Study, American Journal of Neuroradiology, vol.30, issue.6, pp.1222-1226, 2009.
DOI : 10.3174/ajnr.A1556

A. Gholipour, J. A. Estroff, and S. K. Warfield, Robust Super-Resolution Volume Reconstruction From Slice Acquisitions: Application to Fetal Brain MRI, IEEE Transactions on Medical Imaging, vol.29, issue.10, pp.1739-1758, 2010.
DOI : 10.1109/TMI.2010.2051680

H. Greenspan, Super-Resolution in Medical Imaging, The Computer Journal, vol.52, issue.1, pp.43-63, 2009.
DOI : 10.1093/comjnl/bxm075

H. Greenspan, G. Oz, N. Kiryati, and S. Peled, MRI inter-slice reconstruction using super-resolution, Magnetic Resonance Imaging, vol.20, issue.5, pp.437-446, 2002.
DOI : 10.1016/S0730-725X(02)00511-8

J. L. Hsu, W. Van-hecke, C. H. Bai, C. H. Lee, Y. F. Tsai et al., Microstructural white matter changes in normal aging: A diffusion tensor imaging study with higher-order polynomial regression models, NeuroImage, vol.49, issue.1, pp.32-43, 2010.
DOI : 10.1016/j.neuroimage.2009.08.031

J. Huang, R. P. Friedland, and A. P. Auchus, Diffusion Tensor Imaging of Normal-Appearing White Matter in Mild Cognitive Impairment and Early Alzheimer Disease: Preliminary Evidence of Axonal Degeneration in the Temporal Lobe, American Journal of Neuroradiology, vol.28, issue.10, pp.1943-1948, 2007.
DOI : 10.3174/ajnr.A0700

S. Jbabdi, T. E. Behrens, and S. M. Smith, Crossing fibres in tract-based spatial statistics, NeuroImage, vol.49, issue.1, pp.249-256, 2010.
DOI : 10.1016/j.neuroimage.2009.08.039

S. Kindermann, S. Osher, and P. Jones, Deblurring and Denoising of Images by Nonlocal Functionals, Multiscale Modeling & Simulation, vol.4, issue.4, pp.1091-1115, 2005.
DOI : 10.1137/050622249

Y. Lou, X. Zhang, S. Osher, and A. Bertozzi, Image Recovery via Nonlocal Operators, Journal of Scientific Computing, vol.60, issue.3, pp.185-197, 2010.
DOI : 10.1007/s10915-009-9320-2

J. Mangin, C. Poupon, C. Clark, L. Bihan, D. Bloch et al., 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

J. V. Manjon, J. Carbonell-caballero, J. J. Lull, G. Garcia-marti, L. Marti-bonmati et al., MRI denoising using Non-Local Means, Medical Image Analysis, vol.12, issue.4, pp.514-523, 2008.
DOI : 10.1016/j.media.2008.02.004

J. V. Manjon, P. Coupe, A. Buades, D. L. Collins, and M. Robles, MRI Superresolution Using Self-Similarity and Image Priors, International Journal of Biomedical Imaging, vol.17, issue.3, p.425891, 2010.
DOI : 10.1016/j.neuroimage.2005.02.018

URL : https://hal.archives-ouvertes.fr/hal-00453259

J. V. Manjon, P. Coupe, A. Buades, V. Fonov, L. Collins et al., Non-local MRI upsampling, Medical Image Analysis, vol.14, issue.6, pp.784-792, 2010.
DOI : 10.1016/j.media.2010.05.010

URL : https://hal.archives-ouvertes.fr/inserm-00506797

J. V. Manjon, P. Coupe, A. Buades, L. Collins, D. Robles et al., New methods for MRI denoising based on sparseness and self-similarity, Medical Image Analysis, vol.16, issue.1, pp.18-27, 2012.
DOI : 10.1016/j.media.2011.04.003

URL : https://hal.archives-ouvertes.fr/inserm-00601866

M. Mignotte, A non-local regularization strategy for image deconvolution, Pattern Recognition Letters, vol.29, issue.16, pp.2206-2212, 2008.
DOI : 10.1016/j.patrec.2008.08.004

K. L. Miller, C. J. Stagg, G. Douaud, S. Jbabdi, S. M. Smith et al., Diffusion imaging of whole, post-mortem human brains on a clinical MRI scanner, NeuroImage, vol.57, issue.1, pp.167-181, 2011.
DOI : 10.1016/j.neuroimage.2011.03.070

S. Mori and P. C. Van-zijl, Fiber tracking: principles and strategies - a technical review, NMR in Biomedicine, vol.14, issue.7-8, pp.468-480, 2002.
DOI : 10.1002/nbm.781

P. Mukherjee, S. W. Chung, J. I. Berman, C. P. Hess, and R. G. Henry, Diffusion Tensor MR Imaging and Fiber Tractography: Technical Considerations, American Journal of Neuroradiology, vol.29, issue.5, pp.843-852, 2008.
DOI : 10.3174/ajnr.A1052

S. Nedjati-gilani, D. C. Alexander, and G. J. Parker, Regularized superresolution for diffusion MRI. Biomedical Imaging: From Nano to Macro, pp.875-878, 2008.

T. Netsch and A. Van-muiswinkel, Quantitative Evaluation of Image-Based Distortion Correction in Diffusion Tensor Imaging, IEEE Transactions on Medical Imaging, vol.23, issue.7, pp.789-798, 2004.
DOI : 10.1109/TMI.2004.827479

H. Oouchi, K. Yamada, K. Sakai, O. Izu, T. Kubota et al., Diffusion Anisotropy Measurement of Brain White Matter Is Affected by Voxel Size: Underestimation Occurs in Areas with Crossing Fibers, American Journal of Neuroradiology, vol.28, issue.6, p.5, 2007.
DOI : 10.3174/ajnr.A0488

P. Xavier-de-fontes, F. Andrade-barroso, G. Coupé, P. Hellier, and P. , Real time ultrasound image denoising, Journal of Real-Time Image Processing, vol.25, issue.11, pp.15-22, 2011.
DOI : 10.1007/s11554-010-0158-5

URL : https://hal.archives-ouvertes.fr/inria-00476122

S. Peled and Y. Yeshurun, Superresolution in MRI: Application to human white matter fiber tract visualization by diffusion tensor imaging, Magnetic Resonance in Medicine, vol.201, issue.1, pp.29-35, 2001.
DOI : 10.1002/1522-2594(200101)45:1<29::AID-MRM1005>3.0.CO;2-Z

G. Peyré, S. Bougleux, and L. Cohen, Non-local Regularization of Inverse Problems, pp.57-68, 2008.
DOI : 10.1007/978-3-540-88690-7_5

E. Plenge, D. H. Poot, M. Bernsen, G. Kotek, G. Houston et al., Super-resolution methods in MRI: Can they improve the trade-off between resolution, signal-to-noise ratio, and acquisition time?, Magnetic Resonance in Medicine, vol.2007, issue.6, 1983.
DOI : 10.1002/mrm.24187

D. H. Poot, B. Jeurissen, Y. Bastiaensen, J. Veraart, W. Van-hecke et al., Super-resolution for multislice diffusion tensor imaging. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine, Society of Magnetic Resonance in Medicine, 2012.

D. H. Poot, V. Van-meir, and J. Sijbers, G e n e r a l a n d e f f i c i e n t s u p e r resolution method for multi-slice MRI. Medical image computing and computer-assisted intervention : MICCAI, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.615-622

S. J. Price, N. G. Burnet, T. Donovan, H. A. Green, A. Pena et al., Diffusion Tensor Imaging of Brain Tumours at 3T: A Potential Tool for Assessing White Matter Tract Invasion?, Clinical Radiology, vol.58, issue.6, pp.455-462, 2003.
DOI : 10.1016/S0009-9260(03)00115-6

M. Protter, M. Elad, H. Takeda, and P. Milanfar, Generalizing the Nonlocal-Means to Super-Resolution Reconstruction, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, pp.36-51, 2009.
DOI : 10.1109/TIP.2008.2008067

D. Raffelt, J. D. Tournier, S. Rose, G. R. Ridgway, R. Henderson et al., Apparent Fibre Density: A novel measure for the analysis of diffusion-weighted magnetic resonance images, NeuroImage, vol.59, issue.4, pp.3976-3994, 2012.
DOI : 10.1016/j.neuroimage.2011.10.045

G. Rohde, A. Barnett, P. Basser, S. Marenco, and C. Pierpaoli, Comprehensive approach for correction of motion and distortion in diffusion-weighted MRI, Magnetic Resonance in Medicine, vol.15, issue.1, pp.103-114, 2003.
DOI : 10.1002/mrm.10677

F. Rousseau, Brain Hallucination, pp.497-508, 2008.
DOI : 10.1007/978-3-540-88682-2_38

F. Rousseau, A non-local approach for image super-resolution using intermodality priors???, Medical Image Analysis, vol.14, issue.4, pp.594-605, 2010.
DOI : 10.1016/j.media.2010.04.005

URL : https://hal.archives-ouvertes.fr/hal-00440313

F. Rousseau, O. A. Glenn, B. Iordanova, C. Rodriguez-carranza, D. B. Vigneron et al., Registration-Based Approach for Reconstruction of High-Resolution In Utero Fetal MR Brain Images, Academic Radiology, vol.13, issue.9, pp.1072-1081, 2006.
DOI : 10.1016/j.acra.2006.05.003

URL : https://hal.archives-ouvertes.fr/hal-00257196

Y. Sagi, I. Tavor, S. Hofstetter, S. Tzur-moryosef, T. Blumenfeld-katzir et al., Learning in the Fast Lane: New Insights into Neuroplasticity, Neuron, vol.73, issue.6, pp.1195-1203, 2012.
DOI : 10.1016/j.neuron.2012.01.025

B. Scherrer, A. Gholipour, and S. K. Warfield, Super-resolution reconstruction to increase the spatial resolution of diffusion weighted images from orthogonal anisotropic acquisitions, Medical Image Analysis, vol.16, issue.7, 2012.
DOI : 10.1016/j.media.2012.05.003

S. M. Smith, Fast robust automated brain extraction, Human Brain Mapping, vol.20, issue.3, pp.143-155, 2002.
DOI : 10.1002/hbm.10062

S. M. Smith, M. Jenkinson, H. Johansen-berg, D. Rueckert, T. E. Nichols et al., Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data, NeuroImage, vol.31, issue.4, pp.1487-1505, 2006.
DOI : 10.1016/j.neuroimage.2006.02.024

S. M. Smith, M. Jenkinson, M. W. Woolrich, C. F. Beckmann, T. E. Behrens et al., Advances in functional and structural MR image analysis and implementation as FSL, NeuroImage, vol.23, pp.208-219, 2004.
DOI : 10.1016/j.neuroimage.2004.07.051

J. D. Tournier, F. Calamante, and A. Connelly, MRtrix: Diffusion tractography in crossing fiber regions, International Journal of Imaging Systems and Technology, vol.56, issue.1, pp.53-66, 2012.
DOI : 10.1002/ima.22005

A. Tristan-vega and S. Aja-fernandez, Joint LMMSE estimation of DWI data for DTI processing. Medical image computing and computer-assisted intervention : MICCAI, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.27-34, 2008.

A. Tristan-vega and S. Aja-fernandez, Design and construction of a realistic DWI phantom for filtering performance assessment. Medical image computing and computer-assisted intervention : MICCAI, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.951-958, 2009.

A. Tristan-vega, C. F. Westin, and S. Aja-fernandez, A new methodology for the estimation of fiber populations in the white matter of the brain with the Funk???Radon transform, NeuroImage, vol.49, issue.2, pp.1301-1315, 2010.
DOI : 10.1016/j.neuroimage.2009.09.070

D. S. Tuch, Q-ball imaging, Magnetic Resonance in Medicine, vol.23, issue.6, pp.1358-1372, 2004.
DOI : 10.1002/mrm.20279

J. D. Van-ouwerkerk, Image super-resolution survey, Image and Vision Computing, vol.24, issue.10, pp.1039-1052, 2006.
DOI : 10.1016/j.imavis.2006.02.026

E. Van-reeth, I. W. Tham, C. H. Tan, and C. L. Poh, Super-resolution in magnetic resonance imaging: A review, Concepts in Magnetic Resonance Part A 40A, pp.306-325, 2012.
DOI : 10.1002/cmr.a.21249

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, pp.600-612, 2004.
DOI : 10.1109/TIP.2003.819861

N. Wiest-daessle, 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. Medical image computing and computerassisted intervention : MICCAI, International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.171-179, 2008.
URL : https://hal.archives-ouvertes.fr/inserm-00332388

J. Yang, J. Wright, T. Huang, and Y. Ma, Image Super-Resolution via Sparse Representation, IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2010.

J. Yang, J. Wright, T. S. Huang, and Y. Ma, Image Super-Resolution Via Sparse Representation, IEEE Transactions on Image Processing, vol.19, issue.11, pp.2861-2873, 2010.
DOI : 10.1109/TIP.2010.2050625

P. A. Yushkevich, J. Piven, H. C. Hazlett, R. G. Smith, S. Ho et al., User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability, NeuroImage, vol.31, issue.3, pp.1116-1128, 2006.
DOI : 10.1016/j.neuroimage.2006.01.015