@article {2179,
	title = {On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge},
	journal = {NeuroImage},
	volume = {240},
	year = {2021},
	doi = {https://doi.org/10.1016/j.neuroimage.2021.118367},
	author = {Alberto De Luca and Andrada Ianus and Alexander Leemans and Marco Palombo and Noam Shemesh and Hui Zhang and Daniel C Alexander and Markus Nilsson and Martijn Froeling and Geert-Jan Biessels and Mauro Zucchelli and Matteo Frigo and Enes Albay and Sara Sedlar and Abib Alimi and Samuel Deslauriers-Gauthier and Rachid Deriche and Rutger Fick and Maryam Afzali and Tomasz Pieciak and Fabian Bogusz and Santiago Aja-Fernandez and Evren Ozarslan and Derek K. Jones and Haoze Chen and Mingwu Jin and Zhijie Zhang and Fenxiang Wang and Vishwesh Nath and Prasanna Parvathaneni and Jan Morez and Jan Sijbers and Ben Jeurissen and Shreyas and Fadnavis and Stefan Endres and Ariel Rokem and Eleftherios Garyfallidis and Irina Sanchez and Vesna Prchkovska and Paulo Rodrigues and Bennet A. Landman and Kurt G Schilling}
}
@article {1690,
	title = {T1 relaxometry of crossing fibres in the human brain.},
	journal = {NeuroImage},
	year = {2016},
	month = {07/2016},
	abstract = {A comprehensive tract-based characterisation of white matter should include the ability to quantify myelin and axonal attributes irrespective of the complexity of fibre organisation within the voxel. Recently, a new experimental framework that combines inversion recovery and diffusion MRI, called inversion recovery diffusion tensor imaging (IR-DTI), was introduced and applied in an animal study. IR-DTI provides the ability to assign to each unique fibre population within a voxel a specific value of the longitudinal relaxation time, T1, which is a proxy for myelin content. Here, we apply the IR-DTI approach to the human brain in vivo on 7 healthy subjects for the first time. We demonstrate that the approach is able to measure differential tract properties in crossing fibre areas, reflecting the different myelination of tracts. We also show that tract-specific T1 has less inter-subject variability compared to conventional T1 in areas of crossing fibres, suggesting increased specificity to distinct fibre populations. Finally we show in simulations that changes in myelination selectively affecting one fibre bundle in crossing fibre areas can potentially be detected earlier using IR-DTI.},
	issn = {1095-9572},
	doi = {10.1016/j.neuroimage.2016.07.037},
	author = {Silvia De Santis and Yaniv Assaf and Ben Jeurissen and Derek K. Jones and Roebroeck, Alard}
}
@conference {1661,
	title = {Inversion Recovery DTI In Vivo at 7T in the Human Brain},
	year = {2015},
	pages = {567},
	address = {Toronto, Ontario, Canada},
	author = {Silvia De Santis and Ben Jeurissen and Derek K. Jones and Yaniv Assaf and Alard Roebroek}
}
@article {1348,
	title = {Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging.},
	journal = {Human Brain Mapping},
	volume = {34},
	number = {11},
	year = {2013},
	pages = {2747-66},
	abstract = {It has long been recognized that the diffusion tensor model is inappropriate to characterize complex fiber architecture, causing tensor-derived measures such as the primary eigenvector and fractional anisotropy to be unreliable or misleading in these regions. There is however still debate about the impact of this problem in practice. A recent study using a Bayesian automatic relevance detection (ARD) multicompartment model suggested that a third of white matter (WM) voxels contain crossing fibers, a value that, whilst already significant, is likely to be an underestimate. The aim of this study is to provide more robust estimates of the proportion of affected voxels, the number of fiber orientations within each WM voxel, and the impact on tensor-derived analyses, using large, high-quality diffusion-weighted data sets, with reconstruction parameters optimized specifically for this task. Two reconstruction algorithms were used: constrained spherical deconvolution (CSD), and the ARD method used in the previous study. We estimate the proportion of WM voxels containing crossing fibers to be \~{}90\% (using CSD) and 63\% (using ARD). Both these values are much higher than previously reported, strongly suggesting that the diffusion tensor model is inadequate in the vast majority of WM regions. This has serious implications for downstream processing applications that depend on this model, particularly tractography, and the interpretation of anisotropy and radial/axial diffusivity measures.},
	issn = {1097-0193},
	doi = {10.1002/hbm.22099},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Derek K. Jones and Jan Sijbers}
}
@conference {1341,
	title = {Assessing the implications of complex fiber configurations for DTI metrics in real data sets},
	year = {2012},
	month = {May},
	pages = {3584},
	address = {Melbourne, Australia},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Derek K. Jones and Jan Sijbers}
}
@article {1336,
	title = {The influence of complex white matter architecture on the mean diffusivity in diffusion tensor MRI of the human brain},
	journal = {NeuroImage},
	volume = {59},
	year = {2012},
	month = {2/2012},
	pages = {2208 - 2216},
	issn = {10538119},
	doi = {10.1016/j.neuroimage.2011.09.086},
	author = {Vos, Sjoerd B. and Derek K. Jones and Ben Jeurissen and Viergever, Max A. and Alexander Leemans}
}
@article {1258,
	title = {Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution},
	journal = {Human Brain Mapping},
	volume = {32},
	year = {2011},
	month = {March, 2011},
	pages = {461 - 479},
	keywords = {Diffusion MRI, Spherical deconvolution, Tractography},
	doi = {10.1002/hbm.21032},
	author = {Ben Jeurissen and Alexander Leemans and Derek K. Jones and Jacques-Donald Tournier and Jan Sijbers}
}
@conference {bjeurissaleemansdkjonesjdtournierjsijbers2010,
	title = {Counting the number of fiber orientations in diffusion MRI voxels using constrained spherical deconvolution},
	year = {2010},
	month = {January},
	address = {Utrecht, The Netherlands},
	author = {Ben Jeurissen and Alexander Leemans and Derek K. Jones and Jacques-Donald Tournier and Jan Sijbers}
}
@conference {bjeurissaleemansdkjonesjdtournierjsijbers2010,
	title = {Estimating the number of fiber orientations in diffusion MRI voxels: a constrained spherical deconvolution study},
	year = {2010},
	month = {May},
	address = {Stockholm, Sweden},
	author = {Ben Jeurissen and Alexander Leemans and Derek K. Jones and Jacques-Donald Tournier and Jan Sijbers}
}
@conference {aleemansbjeurissjsijbersdkjones2009,
	title = {ExploreDTI: A Graphical Toolbox for Processing, Analyzing, and Visualizing Diffusion MR Data},
	year = {2009},
	month = {April},
	address = {Honolulu, USA},
	author = {Alexander Leemans and Ben Jeurissen and Jan Sijbers and Derek K. Jones}
}
