<commit_before>.. _references:

References
==========

.. [Christiaens2015] <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. 
   *Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model.*
   NeuroImage, 123 (2015) pp.  89–101 [`SD link <http://www.sciencedirect.com/science/article/pii/S1053811915007168>`__\ ]

.. [Dhollander2014] <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. 
   *Track Orientation Density Imaging (TODI) and Track Orientation Distribution (TOD) based tractography.* 
   NeuroImage, 94 (2014), pp. 312–336 [`SD link <http://www.sciencedirect.com/science/article/pii/S1053811913012676>`__\ ]

.. [Dhollander2016] <NAME>, <NAME>, and <NAME>. 
   *Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion MR data without a co-registered T1 image.* 
   ISMRM Workshop on Breaking the Barriers of Diffusion MRI (2016), pp. 5 
   [`full text link <https://www.researchgate.net/publication/307863133_Unsupervised_3-tissue_response_function_estimation_from_single-shell_or_multi-shell_diffusion_MR_data_without_a_co-registered_T1_image>`__\ ]

.. [Dhollander2018a] <NAME>, <NAME>, and <NAME>.
   *Accuracy of response function estimation algorithms for 3-tissue spherical deconvolution of diverse quality diffusion MRI data.*
   Proceedings of the International Society of Magnetic Resonance in Medicine, 26th annual meeting (2018) Paris, France.
   [`full text link <https://www.researchgate.net/publication/324770874_Accuracy_of_response_function_estimation_algorithms_for_3-tissue_spherical_deconvolution_of_diverse_quality_diffusion_MRI_data>`__\ ]

.. [Dhollander2018b] <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>.
   *Feasibility and benefits of 3-tissue constrained spherical deconvolution for studying the brains of babies.*
   Proceedings of the International Society of Magnetic Resonance in Medicine, 26th annual meeting (2018) Paris, France.
   [`full text link <https://www.researchgate.net/publication/324770875_Feasibility_and_benefits_of_3-tissue_constrained_spherical_deconvolution_for_studying_the_brains_of_babies>`__\ ]

.. [Jeurissen2014] <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. 
   *Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data.* 
   NeuroImage, 103 (2014), pp. 411–426 [`SD link <http://www.sciencedirect.com/science/article/pii/S1053811914006442>`__\ ]

.. [Reisert2011] <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. 
   *Global fiber reconstruction becomes practical.*
   NeuroImage, 54 (2011) pp. 955–962 [`SD link <http://www.sciencedirect.com/science/article/pii/S1053811910011973>`__\ ]

.. [Tax2014] <NAME>, <NAME>, S.B.Vos, <NAME>, and <NAME>.
   *Recursive calibration of the fiber response function for spherical deconvolution of diffusion MRI data.*
   NeuroImage, 86 (2014), pp. 67-80 [`SD link <https://www.sciencedirect.com/science/article/pii/S1053811913008367>`__\ ]

.. [Tournier2004] <NAME>, <NAME>, <NAME>, and <NAME>.
   *Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution.*
   NeuroImage, 23 (2004), pp. 1176-85 [`SD link <https://www.sciencedirect.com/science/article/pii/S1053811904004100>`__\ ]

.. [Tournier2007] <NAME>, <NAME>, and <NAME>.
   *Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution.*
   Neuroimage, 35 (2007), pp. 1459-72. [`SD link <https://www.sciencedirect.com/science/article/pii/S1053811907001243>`__\ ]

.. [Tournier2013] <NAME>, <NAME>, and <NAME>.
   *Determination of the appropriate b value and number of gradient directions for high-angular-resolution diffusion-weighted imaging.*
   NMR Biomed., 26 (2013), pp.  1775-86 [`Wiley link <https://onlinelibrary.wiley.com/doi/abs/10.1002/nbm.3017>`__\ ]

.. [Veraart2016a] <NAME>, <NAME>, and <NAME>.
   *Diffusion MRI noise mapping using random matrix theory.* 
   Magn. Res. Med. 76(5) (2016), pp. 1582-1593. [`Wiley link <https://doi.org/10.1002/mrm.26059>`__\ ]

.. [Veraart2016b] <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME> 
   *Denoising of diffusion MRI using random matrix theory.*
   NeuroImage 142 (2016), pp. 394-406. [`SD link <http://dx.doi.org/10.1016/j.neuroimage.2016.08.016>`__\ ]

<commit_msg>references: refer to all links as full text link; it's going to get messy otherwise<commit_after>.. _references:

References
==========

.. [Christiaens2015] <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. 
   *Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model.*
   NeuroImage, 123 (2015) pp.  89–101 [`full text link <http://www.sciencedirect.com/science/article/pii/S1053811915007168>`__\ ]

.. [Dhollander2014] <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. 
   *Track Orientation Density Imaging (TODI) and Track Orientation Distribution (TOD) based tractography.* 
   NeuroImage, 94 (2014), pp. 312–336 [`full text link <http://www.sciencedirect.com/science/article/pii/S1053811913012676>`__\ ]

.. [Dhollander2016] <NAME>, <NAME>, and <NAME>. 
   *Unsupervised 3-tissue response function estimation from single-shell or multi-shell diffusion MR data without a co-registered T1 image.* 
   ISMRM Workshop on Breaking the Barriers of Diffusion MRI (2016), pp. 5 
   [`full text link <https://www.researchgate.net/publication/307863133_Unsupervised_3-tissue_response_function_estimation_from_single-shell_or_multi-shell_diffusion_MR_data_without_a_co-registered_T1_image>`__\ ]

.. [Dhollander2018a] <NAME>, <NAME>, and <NAME>.
   *Accuracy of response function estimation algorithms for 3-tissue spherical deconvolution of diverse quality diffusion MRI data.*
   Proceedings of the International Society of Magnetic Resonance in Medicine, 26th annual meeting (2018) Paris, France.
   [`full text link <https://www.researchgate.net/publication/324770874_Accuracy_of_response_function_estimation_algorithms_for_3-tissue_spherical_deconvolution_of_diverse_quality_diffusion_MRI_data>`__\ ]

.. [Dhollander2018b] <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>.
   *Feasibility and benefits of 3-tissue constrained spherical deconvolution for studying the brains of babies.*
   Proceedings of the International Society of Magnetic Resonance in Medicine, 26th annual meeting (2018) Paris, France.
   [`full text link <https://www.researchgate.net/publication/324770875_Feasibility_and_benefits_of_3-tissue_constrained_spherical_deconvolution_for_studying_the_brains_of_babies>`__\ ]

.. [Jeurissen2014] <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. 
   *Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data.* 
   NeuroImage, 103 (2014), pp. 411–426 [`full text link <http://www.sciencedirect.com/science/article/pii/S1053811914006442>`__\ ]

.. [Reisert2011] <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. 
   *Global fiber reconstruction becomes practical.*
   NeuroImage, 54 (2011) pp. 955–962 [`full text link <http://www.sciencedirect.com/science/article/pii/S1053811910011973>`__\ ]

.. [Tax2014] <NAME>, <NAME>, S.B.Vos, <NAME>, and <NAME>.
   *Recursive calibration of the fiber response function for spherical deconvolution of diffusion MRI data.*
   NeuroImage, 86 (2014), pp. 67-80 [`full text link <https://www.sciencedirect.com/science/article/pii/S1053811913008367>`__\ ]

.. [Tournier2004] <NAME>, <NAME>, <NAME>, and <NAME>.
   *Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution.*
   NeuroImage, 23 (2004), pp. 1176-85 [`full text link <https://www.sciencedirect.com/science/article/pii/S1053811904004100>`__\ ]

.. [Tournier2007] <NAME>, <NAME>, and <NAME>.
   *Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution.*
   Neuroimage, 35 (2007), pp. 1459-72. [`full text link <https://www.sciencedirect.com/science/article/pii/S1053811907001243>`__\ ]

.. [Tournier2013] <NAME>, <NAME>, and <NAME>.
   *Determination of the appropriate b value and number of gradient directions for high-angular-resolution diffusion-weighted imaging.*
   NMR Biomed., 26 (2013), pp.  1775-86 [`full text link <https://onlinelibrary.wiley.com/doi/abs/10.1002/nbm.3017>`__\ ]

.. [Veraart2016a] <NAME>, <NAME>, and <NAME>.
   *Diffusion MRI noise mapping using random matrix theory.* 
   Magn. Res. Med. 76(5) (2016), pp. 1582-1593. [`full text link <https://doi.org/10.1002/mrm.26059>`__\ ]

.. [Veraart2016b] <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME> 
   *Denoising of diffusion MRI using random matrix theory.*
   NeuroImage 142 (2016), pp. 394-406. [`full text link <http://dx.doi.org/10.1016/j.neuroimage.2016.08.016>`__\ ]


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