@conference {1599,
	title = {Assessing inter-subject variability of white matter response functions used for constrained spherical deconvolution},
	volume = {23},
	year = {2015},
	pages = {2834},
	author = {Ben Jeurissen and Jan Sijbers and Jacques-Donald Tournier}
}
@conference {1660,
	title = {Human In Vivo Myeloarchitecture Using Whole-Brain Diffusion MRI},
	year = {2015},
	pages = {1679},
	address = {Toronto, Ontario, Canada},
	author = {Fernando Calamante and Ben Jeurissen and Robert Elton Smith and Jacques-Donald Tournier and Alan Connelly}
}
@conference {1662,
	title = {Time to Move On: An FOD-Based DEC Map to Replace DTI{\textquoteright}s Trademark DEC FA},
	year = {2015},
	pages = {1027},
	author = {Thijs Dhollander and Robert Elton Smith and Jacques-Donald Tournier and Ben Jeurissen and Alan Connelly}
}
@conference {1595,
	title = {Tissue-type segmentation using non-negative matrix factorization of multi-shell diffusion-weighted MRI images},
	volume = {23},
	year = {2015},
	pages = {349},
	author = {Ben Jeurissen and Jacques-Donald Tournier and Jan Sijbers}
}
@conference {1474,
	title = {Brain Tissue Types Resolved Using Spherical Deconvolution of Multi-Shell Diffusion MRI Data},
	year = {2014},
	month = {May},
	pages = {973},
	address = {Milan, Italy},
	author = {Ben Jeurissen and Jacques-Donald Tournier and Thijs Dhollander and Alan Connelly and Jan Sijbers}
}
@article {1494,
	title = {Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data},
	journal = {NeuroImage},
	volume = {103},
	year = {2014},
	pages = {411{\textendash}426},
	abstract = {Constrained spherical deconvolution (CSD) has become one of the most widely used methods to extract white matter (WM) fibre orientation information from diffusion-weighted MRI (DW-MRI) data, overcoming the crossing fibre limitations inherent in the diffusion tensor model. It is routinely used to obtain high quality fibre orientation distribution function (fODF) estimates and fibre tractograms and is increasingly used to obtain apparent fibre density (AFD) measures. Unfortunately, CSD typically only supports data acquired on a single shell in q-space. With multi-shell data becoming more and more prevalent, there is a growing need for CSD to fully support such data. Furthermore, CSD can only provide high quality fODF estimates in voxels containing WM only. In voxels containing other tissue types such as grey matter (GM) and cerebrospinal fluid (CSF), the WM response function may no longer be appropriate and spherical deconvolution produces unreliable, noisy fODF estimates.

The aim of this study is to incorporate support for multi-shell data into the CSD approach as well as to exploit the unique b-value dependencies of the different tissue types to estimate a multi-tissue ODF. The resulting approach is dubbed multi-shell, multi-tissue CSD (MSMT-CSD) and is compared to the state-of-the-art single-shell, single-tissue CSD (SSST-CSD) approach. Using both simulations and real data, we show that MSMT-CSD can produce reliable WM/GM/CSF volume fraction maps, directly from the DW data, whereas SSST-CSD has a tendency to overestimate the WM volume in voxels containing GM and/or CSF. In addition, compared to SSST-CSD, MSMT-CSD can substantially increase the
precision of the fODF fibre orientations and reduce the presence of spurious fODF peaks in voxels containing GM and/or CSF. Both effects translate into more reliable AFD measures and tractography results with MSMT-CSD compared to SSST-CSD.},
	doi = {10.1016/j.neuroimage.2014.07.061},
	author = {Ben Jeurissen and Jacques-Donald Tournier and Thijs Dhollander and Alan Connelly and Jan Sijbers}
}
@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}
}
@conference {1340,
	title = {HARDI-based methods for fiber orientation estimation},
	year = {2012},
	note = {Received the Summa Cum Laude Merit Award for the 20th Annual ISMRM meeting.},
	month = {May},
	pages = {3585},
	address = {Melbourne, Australia},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@mastersthesis {1337,
	title = {Improved analysis of brain connectivity using high angular resolution diffusion MRI},
	volume = {PhD in Sciences},
	year = {2012},
	month = {03/2012},
	pages = {208},
	school = {University of Antwerp},
	type = {PhD thesis},
	keywords = {complex WM architecture, constrained spherical deconvolution, crossing fibers, CSD, Diffusion MRI, diffusion tensor imaging, DTI, HARDI, high angular resolution diffusion imaging, Magnetic Resonance Imaging, MRI, probabilistic tractography, Spherical deconvolution, Tractography, white matter, WM},
	author = {Ben Jeurissen}
}
@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}
}
@inproceedings {bjeurissaleemansjdtournierjsijbers2009,
	title = {Fiber Tracking on the {\textquoteright}Fiber Cup Phantom{\textquoteright} using Constrained Spherical Deconvolution},
	booktitle = {MICCAI workshop on Diffusion Modelling and the Fiber Cup (DMFC{\textquoteright}09)},
	year = {2009},
	address = {London, United Kingdom},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@conference {bjeurissaleemansjdtournierjsijbers2009,
	title = {Probabilistic Fiber Tracking using the Residual Bootstrap with Constrained Spherical Deconvolution MRI},
	year = {2009},
	month = {April},
	address = {Honolulu, USA},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@conference {bjeurissaleemansjdtournierjsijbers2008,
	title = {Bootstrap methods for estimating uncertainty in Constrained Spherical Deconvolution fiber orientations},
	year = {2008},
	month = {May},
	pages = {3324},
	address = {Toronto, Canada},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@conference {bjeurissaleemansjdtournierjsijbers2008,
	title = {Can residual bootstrap reliably estimate uncertainty in fiber orientation obtained by spherical deconvolution from diffusion-weighted MRI?},
	volume = {41},
	year = {2008},
	month = {June},
	address = {Melbourne, Australia},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
@inproceedings {bjeurissaleemansjdtournierjsijbers2008,
	title = {Estimation of Uncertainty in Constrained Spherical Deconvolution Fiber Orientations},
	booktitle = {5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro},
	year = {2008},
	pages = {907-910},
	address = {Paris, France},
	author = {Ben Jeurissen and Alexander Leemans and Jacques-Donald Tournier and Jan Sijbers}
}
