B. Tunc, Individualized Map of White Matter Pathways: Connectivity-Based Paradigm for Neurosurgical Planning, Neurosurgery, vol.79, issue.4, pp.568-577, 2016.

O. Pasternak, 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, Quantification of microscopic diffusion anisotropy disentangles effects of orientation dispersion from microstructure: applications in healthy volunteers and in brain tumors, NeuroImage, vol.104, pp.241-252, 2015.

A. Tabesh, Estimation of tensors and tensor-derived measures in diffusional kurtosis imaging, Magn Reson Med, 2010.

J. Zhang, 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, Characterizing the distribution of anisotropic micro-structural environments with diffusion-weighted imaging (DIAMOND), International Conference on Medical Image Computing and ComputerAssisted 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.3172-3193, 2018.

B. Scherrer and S. K. Warfield, Why multiple b-values are required for multi-tensor models. Evaluation with a constrained log-Euclidean model, Proc. IEEE Int Biomedical Imaging: From Nano to Macro Symp, pp.1389-1392, 2010.

D. C. Alexander, 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 twocompartment tensor model, International Society for Magnetic Resonance in Medicine Meeting, 2004.

M. Bergamino, Applying a free-water correction to diffusion imaging data uncovers stressrelated neural pathology in depression, NeuroImage: Clinical, vol.10, pp.336-342, 2016.

E. Ofori, Longitudinal changes in free-water within the substantia nigra of Parkinson's disease. Brain, vol.138, pp.2322-2331, 2015.

P. J. Planetta, Free-water imaging in Parkinson's disease and atypical parkinsonism, Brain, vol.139, issue.2, pp.495-508, 2015.

R. G. Burciu, Free-water and BOLD imaging changes in Parkinson's disease patients chronically treated with a MAO-B inhibitor. Human brain mapping, vol.37, pp.2894-2903, 2016.

L. K. Oestreich, Characterizing white matter changes in chronic schizophrenia: A free-water imaging multi-site study, vol.189, pp.153-161, 2017.

, No reuse allowed without permission. was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint, 2019.

D. Parker, The Role of Bias Field Correction in the Free Water Elimination Problem"\. in ISMRM, MICCAI. 2018. Granada, Spain: Brainlesion Workshop, vol.17, 2018.

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.1572-1581, 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.1131-1138, 2016.

E. Garyfallidis, Dipy, a library for the analysis of diffusion MRI data. Front Neuroinform, vol.8, p.8, 2014.

E. Caruyer, Phantomas: a flexible software library to simulate diffusion MR phantoms, ISMRM, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00944644

T. D. Satterthwaite, Structural Brain Abnormalities in Youth With Psychosis Spectrum Symptoms, JAMA Psychiatry, 2016.

J. V. Manjon, 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. L. 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.1063-1078, 2016.

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

K. Oishi, 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, GLISTR: Glioma Image Segmentation and Registration, IEEE Trans Med Imaging, vol.31, issue.10, pp.1941-54, 2012.

A. R. Hoy, Optimization of a free water elimination two-compartment model for diffusion tensor imaging, NeuroImage, vol.103, pp.323-333, 2014.

N. J. Tustison, N4ITK: Improved N3 Bias Correction, IEEE Transactions on Medical Imaging, vol.29, issue.6, pp.1310-1320, 2010.

J. Helenius, Diffusion-Weighted MR Imaging in Normal Human Brains in Various Age Groups, AJNR Am J Neuroradiol, vol.23, issue.2, pp.194-199, 2002.

R. Wang, Diffusion Toolkit: A Software Package for Diffusion Imaging Data Processing and Tractography, in Intl Soc Mag Reson Med, p.3720, 2007.

E. Garyfallidis, Recognition of white matter bundles using local and global streamline-based registration and clustering Neuroimage, 2017.

K. G. Abdullah, Use of diffusion tensor imaging in glioma resection, Neurosurgical focus, vol.34, issue.4, p.1, 2013.

K. G. Schilling, Limits to anatomical accuracy of diffusion tractography using modern approaches, NeuroImage, vol.185, pp.1-11, 2019.

R. Liao, Performance of unscented Kalman filter tractography in edema: Analysis of the two-tensor model, NeuroImage: Clinical, vol.15, pp.819-831, 2017.

J. Lecoeur, Addressing the Challenge of Edema in Fiber Tracking, Medical Image Computing and Computer-Assisted Intervention MICCAI 2014, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01072222

S. S. Brem, Central nervous system cancers. Natl Compr Canc Netw, vol.9, pp.352-400, 2011.

N. Sanai, Phase 0 trial of AZD1775 in first-recurrence glioblastoma patients, Clinical Cancer Research, vol.24, issue.16, pp.3820-3828, 2018.

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

S. Mori and P. C. Zijl, Fiber tracking: principles and strategies -a technical review, NMR in Biomedicine, vol.15, issue.7-8, pp.468-480, 2002.

B. Tunc, Automated tract extraction via atlas based Adaptive Clustering, Neuroimage, vol.102, issue.2, pp.596-607, 2014.

B. Tunc, Measuring Disruption of the Structural Connectome in Diffuse Traumatic Brain Injury. in OHBM, 2017.

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

A. A. Ould-ismail, G. Amouzandeh, and S. C. Grant, Structural connectivity within neural ganglia: A default small-world network, Neuroscience, vol.337, pp.276-284, 2016.