B. Tunc, M. Ingalhalikar, D. Parker, J. Lecoeur, N. Singh et al., Individualized Map of White Matter Pathways: Connectivity-Based Paradigm for Neurosurgical Planning, Neurosurgery, vol.79, issue.4, pp.568-77, 2016.

O. Pasternak, N. Sochen, Y. Gur, N. Intrator, and Y. Assaf, Free water elimination and mapping from diffusion MRI. Magnetic resonance in medicine: official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine, vol.62, pp.717-747, 2009.

F. Szczepankiewicz, S. Lasi?, D. Van-westen, P. C. Sundgren, E. Englund et al., Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: applications in healthy volunteers and in brain tumors, NeuroImage, vol.104, pp.241-52, 2015.

A. Tabesh, J. H. Jensen, B. A. Ardekani, and J. A. Helpern, Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging, Magn Reson Med, 2010.

J. Zhang, M. V. Jones, M. T. Mcmahon, S. Mori, and P. A. Calabresi, In vivo and ex vivo diffusion tensor imaging of cuprizone-induced demyelination in the mouse corpus callosum, Magn Reson Med, vol.67, issue.3, pp.750-759, 2012.

B. Scherrer, A. Schwartzman, M. Taquet, S. P. Prabhu, M. Sahin et al., Characterizing the distribution of anisotropic micro-structural environments with diffusion-weighted imaging (DIAMOND), Medical Image Computing and Computer-Assisted Intervention, vol.16, pp.518-544, 2013.

D. S. Novikov, V. G. Kiselev, and S. N. Jespersen, On modeling. Magnetic resonance in medicine, vol.79, pp.3172-93, 2018.

B. Scherrer and S. K. Warfield, Why multiple b-values are required for multi-tensor models, Proc IEEE Int Biomedical Imaging: From Nano to Macro Symp, pp.1389-92, 2010.

D. C. Alexander, C. Pierpaoli, P. J. Basser, and J. C. Gee, Spatial transformations of diffusion tensor magnetic resonance images, IEEE Trans Med Imaging, vol.20, issue.11, pp.1131-1140, 2001.

C. Pierpaoli and D. Jones, Removing CSF contamination in brain DT-MRIs by using a two-compartment tensor model, Medicine Meeting, 2004.

M. Bergamino, O. Pasternak, M. Farmer, M. E. Shenton, and J. P. Hamilton, Applying a free-water correction to diffusion imaging data uncovers stress-related neural pathology in depression, NeuroImage: Clinical, vol.10, pp.336-378, 2016.

E. Ofori, O. Pasternak, P. J. Planetta, H. Li, R. G. Burciu et al., Longitudinal changes in free-water within the substantia nigra of Parkinson's disease, Brain, vol.138, issue.8, pp.2322-2353, 2015.

P. J. Planetta, E. Ofori, O. Pasternak, R. G. Burciu, P. Shukla et al., Free-water imaging in Parkinson's disease and atypical parkinsonism, Brain, vol.139, issue.2, pp.495-508, 2015.

R. G. Burciu, E. Ofori, P. Shukla, O. Pasternak, J. W. Chung et al., Free-water and BOLD imaging changes in Parkinson's disease patients chronically treated with a MAO-B inhibitor, Human brain mapping, vol.37, issue.8, pp.2894-903, 2016.

L. K. Oestreich, A. E. Lyall, O. Pasternak, Z. Kikinis, D. T. Newell et al., Characterizing white matter changes in chronic schizophrenia: A free-water imaging multi-site study, Schizophrenia research, vol.189, pp.153-61, 2017.

D. Parker, A. Oi, and S. , The Role of Bias Field Correction in the Free Water Elimination Problem, \. ISMRM, 2018.

C. Montreal,

A. A. Ould-ismail, D. Parker, M. Hernandez-fernandez, S. Brem, S. Alexander et al., Characterizing Peritumoral Tissue Using Free Water Elimination in Clinical DTI. MICCAI, Granada, 2018.
URL : https://hal.archives-ouvertes.fr/inserm-01867347

A. R. Hoy, S. R. Kecskemeti, and A. L. Alexander, Free water elimination diffusion tractography: A comparison with conventional and fluid-attenuated inversion recovery, diffusion tensor imaging acquisitions, Journal of Magnetic Resonance Imaging, vol.42, issue.6, pp.1572-81, 2015.

C. Kodiweera and Y. Wu, Data of NODDI diffusion metrics in the brain and computer simulation of hybrid diffusion imaging (HYDI) acquisition scheme. Data in brief, vol.7, pp.1131-1139, 2016.

T. D. Satterthwaite, D. H. Wolf, M. E. Calkins, S. N. Vandekar, G. Erus et al., Structural Brain Abnormalities in Youth With Psychosis Spectrum Symptoms, JAMA Psychiatry, 2016.

J. V. Manjon, P. Coupe, C. L. Buades, A. Collins, D. L. Robles et al., Diffusion weighted image denoising using overcomplete local PCA, PLoS One, vol.8, issue.9, p.73021, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00871777

J. Andersson and S. N. Sotiropoulos, An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging, Neuroimage, vol.125, pp.1063-78, 2016.

S. M. Smith, Fast robust automated brain extraction, vol.17, pp.143-55, 2002.

K. Oishi, A. Faria, H. Jiang, X. Li, K. Akhter et al., Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and Alzheimer's disease participants, Neuroimage, vol.46, issue.2, pp.486-99, 2009.

A. Gooya, K. M. Pohl, M. Bilello, L. Cirillo, G. Biros et al., GLISTR: Glioma Image Segmentation and Registration, IEEE Trans Med Imaging, vol.31, issue.10, pp.1941-54, 2012.

E. Garyfallidis, M. Brett, B. Amirbekian, A. Rokem, S. Van-der-walt et al., Dipy, a library for the analysis of diffusion MRI data, Front Neuroinform, vol.8, issue.8, 2014.

E. Caruyer, A. Daducci, M. Descoteaux, J. Houde, J. Thiran et al., Phantomas: a flexible software library to simulate diffusion MR phantoms. ISMRM, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00944644

A. R. Hoy, C. G. Koay, S. R. Kecskemeti, and A. L. Alexander, Optimization of a free water elimination two-compartment model for diffusion tensor imaging, NeuroImage, vol.103, pp.323-356, 2014.

N. J. Tustison, B. B. Avants, P. A. Cook, Y. Zheng, A. Egan et al., N4ITK: Improved N3 Bias Correction, IEEE Transactions on Medical Imaging, vol.29, issue.6, pp.1310-1330, 2010.

J. Helenius, L. Soinne, J. Perkio, O. Salonen, A. Kangasmaki et al., Diffusion-Weighted MR Imaging in Normal Human Brains in Various Age Groups, AJNR Am J Neuroradiol, vol.23, issue.2, pp.194-203, 2002.

R. Wang, T. Benner, A. G. Sorensen, and V. J. Wedeen, Diffusion Toolkit: A Software Package for Diffusion Imaging Data Processing and Tractography2007

E. Garyfallidis, . Cô-té-m-a, F. Rheault, J. Sidhu, J. Hau et al., Recognition of white matter bundles using local and global streamline-based registration and clustering Neuroimage, 2017.

H. Zhang, T. Schneider, C. A. Wheeler-kingshott, and A. Dc, NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain, NeuroImage, vol.61, pp.1000-1016, 2012.

K. G. Abdullah, D. Lubelski, P. Nucifora, and S. Brem, Use of diffusion tensor imaging in glioma resection, Neurosurgical focus, vol.34, issue.4, 2013.

K. G. Schilling, V. Nath, C. Hansen, P. Parvathaneni, J. Blaber et al., Limits to anatomical accuracy of diffusion tractography using modern approaches, NeuroImage, vol.185, pp.1-11, 2019.

R. Liao, L. Ning, Z. Chen, L. Rigolo, S. Gong et al., Performance of unscented Kalman filter tractography in edema: Analysis of the two-tensor model, NeuroImage: Clinical, vol.15, pp.819-850, 2017.

J. Lecoeur, E. Caruyer, M. Elliott, S. Brem, L. Macyszyn et al., Addressing the Challenge of Edema in Fiber Tracking, Medical Image Computing and Computer-Assisted Intervention MICCAI, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01072222

S. S. Brem, P. J. Bierman, H. Brem, N. Butowski, M. C. Chamberlain et al., Central nervous system cancers. Natl Compr Canc Netw, vol.9, pp.352-400, 2011.

N. Sanai, J. Li, J. Boerner, K. Stark, J. Wu et al., Phase 0 trial of AZD1775 in first-recurrence glioblastoma patients, Clinical Cancer Research, vol.24, issue.16, pp.3820-3828, 2018.

D. Parker, L. , and S. , Tracking Through Edema: Enhanced Neurosurgical Planning Using Advanced Diffusion Modeling of the Peritumoral Tissue Microstructure, Society for Neuro-Oncology Annual Scientific Meeting (SNO), 2018.

S. Mori and Z. Pcmv, Fiber tracking: principles and strategies-a technical review, NMR in Biomedicine, vol.15, issue.7-8, pp.468-80, 2002.

B. Tunc, W. A. Parker, M. Ingalhalikar, and R. Verma, Automated tract extraction via atlas based Adaptive Clustering, Neuroimage, vol.102, issue.2, pp.596-607, 2014.

B. Tunc, B. Solmaz, D. Parker, J. Whyte, T. Hart et al., Measuring Disruption of the Structural Connectome in Diffuse Traumatic Brain Injury, 2017.

P. Mukherjee, Diffusion tensor imaging and fiber tractography in acute stroke, Neuroimaging Clin N Am, vol.15, issue.3, pp.655-65, 2005.