Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model
Open accessAbstract
Diffusion-weighted imaging and tractography provide a unique, non-invasive technique to study the macroscopic structure and connectivity of brain white matter in vivo. Global tractography methods aim at reconstructing the full-brain fiber configuration that best explains the measured data, based on a generative signal model. In this work, we incorporate a multi-shell multi-tissue model based on spherical convolution, into a global tractography framework, which allows to deal with partial volume effects. The required tissue response functions can be estimated from and hence calibrated to the data. The resulting track reconstruction is quantitatively related to the apparent fiber density in the data. In addition, the fiber orientation distribution for white matter and the volume fractions of gray matter and cerebrospinal fluid are produced as ancillary results. Validation results on simulated data demonstrate that this data-driven approach improves over state-of-the-art streamline and global tracking methods, particularly in the valid connection rate. Results in human brain data correspond to known white matter anatomy and show improved modeling of partial voluming. This work is an important step toward detecting and quantifying white matter changes and connectivity in healthy subjects and patients.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Global tractography aims to find the full track configuration that most optimally fits the acquired data.
“Global tractography aims to find the full track (i.e., whole brain) configuration that most optimally fits the acquired data (Reisert et al. 2011; Mangin et al. 2013; Christiaens et al. 2015b).”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Christiaens et al. 2015b
Cites
Advances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630 · see citing works
The basis of anisotropic water diffusion in the nervous system—A technical review.
Beaulieu, C. (2002) · NMR in Biomedicine · doi:10.1002/nbm.782 · Cited by 4,653 · see citing works
Le Bihan, D., Breton, E., Lallemand, D., Grenier, P., Cabanis, E., & Laval-Jeantet, M. (1986) · Radiology · doi:10.1148/radiology.161.2.3763909 · Cited by 3,912 · see citing works
White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI
Jones, D. K., Knösche, T. R., & Turner, R. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.081 · Cited by 2,479 · cited 1× · see citing works
“Streamline tractography, due to its dependence on the seeding distribution and hard thresholds on curvature, FA, or fODF amplitude, lacks any quantitative correspondence to the data and is therefore not suited for measuring connectivity (Jones et al., 2013).”
High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity
Tuch, D. S., Reese, T. G., Wiegell, M. R., Makris, N., Belliveau, J. W., & Wedeen, V. J. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10268 · Cited by 1,548 · see citing works
Jeurissen, B., Leemans, A., Tournier, J., Jones, D. K., & Sijbers, J. (2013) · Human Brain Mapping · doi:10.1002/hbm.22099 · Cited by 1,088 · see citing works
SIFT: Spherical-deconvolution informed filtering of tractograms
Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.11.049 · Cited by 752 · see citing works
Tractometer: Towards validation of tractography pipelines
Côté, M.-A., Girard, G., Boré, A., Garyfallidis, E., Houde, J.-C., & Descoteaux, M. (2013) · Medical Image Analysis · doi:10.1016/j.media.2013.03.009 · Cited by 228 · see citing works
Mangin, J.-F., Fillard, P., Cointepas, Y., Le Bihan, D., Frouin, V., & Poupon, C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.009 · Cited by 121 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation
Tournier, J.-D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C.-H., & Connelly, A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116137 · Cited by 2,974 · see citing works
Self-citationTractography-based connectomes are dominated by false-positive connections
Maier-Hein, K. H., Neher, P., Houde, J.-C., Côté, M.-A., Garyfallidis, E., Zhong, J., Chamberland, M., Yeh, F.-C., Lin, Y.-C., Ji, Q., et al. (2016) · doi:10.1101/084137 · Cited by 1,361 · see citing works
Diffusion MRI fiber tractography of the brain
Jeurissen, B., Descoteaux, M., Mori, S., & Leemans, A. (2019) · NMR in Biomedicine · doi:10.1002/nbm.3785 · Cited by 574 · see citing works
Zhang, F., Daducci, A., He, Y., Schiavi, S., Seguin, C., Smith, R. E., Yeh, C.-H., Zhao, T., & O’Donnell, L. J. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2021.118870 · Cited by 302 · see citing works
“[313, 267, 163, 387, 312, 98, 486], or iteratively perturbing a set of candidate trajectories [542, 43, 284, 103]).”
BackgroundMapping Structural Connectivity Using Diffusion <scp>MRI</scp> : Challenges and Opportunities
Yeh, C., Jones, D. K., Liang, X., Descoteaux, M., & Connelly, A. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27188 · Cited by 187 · see citing works
Microstructure informed tractography: pitfalls and open challenges
Daducci, A., Dal Palú, A., Descoteaux, M., & Thiran, J.-P. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00247 · Cited by 138 · see citing works
An afferent white matter pathway from the pulvinar to the amygdala facilitates fear recognition
McFadyen, J., Mattingley, J. B., & Garrido, M. I. (2019) · eLife · doi:10.7554/elife.40766 · Cited by 115 · see citing works
Girard, G., Caminiti, R., Battaglia-Mayer, A., St-Onge, E., Ambrosen, K. S., Eskildsen, S. F., Krug, K., Dyrby, T. B., Descoteaux, M., Thiran, J.-P., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117201 · Cited by 83 · see citing works
Sinke, M. R. T., Otte, W. M., Christiaens, D., Schmitt, O., Leemans, A., van der Toorn, A., Sarabdjitsingh, R. A., Joëls, M., & Dijkhuizen, R. M. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1628-y · Cited by 70 · see citing works
Self-citation
- Authors
- Christiaens, D., Reisert, M., Dhollander, T., Sunaert, S., Suetens, P., & Maes, F.
- Affiliations
- Universitair Ziekenhuis Leuven (BE)
- University of Freiburg (DE)
- Florey Institute of Neuroscience and Mental Health (AU)
- Corresponding author
- Christiaens, Daan
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2015-12
- Volume
- 123
- Pages
- 89-101
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1133283280
- Cited by
- 152
- References
- 9
- Citations by year
- 2026: 4 · 2025: 19 · 2024: 17 · 2023: 10 · 2022: 17 · 2021: 12 · 2020: 18 · 2019: 18 · 2018: 18 · 2017: 9 · 2016: 8 · 2015: 2
- FWCI
- 9.09
- Type
- journal article
- Language
- en
- Open access
- green
- lirias.kuleuven.be
- Licence
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- FreiDok plus (Universitätsbibliothek Freiburg) (submittedVersion)
- Lirias (submittedVersion, other-oa)
- Funders
- Agency for Innovation by Science and Technology (IWT) (121013)
- KU Leuven Concerted Research Action (GOA/11/006)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- spherical convolution, partial volume effect, fiber orientation distribution, white matter connectivity, fiber density, white matter
- MeSH terms
- Brain, Computer Simulation, Humans, Image Processing, Computer-Assisted, Markov Chains, Monte Carlo Method, Signal Processing, Computer-Assisted, Reproducibility of Results, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, White Matter, Gray Matter
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
