The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Toward global tractography
Abstract
Diffusion-based tractography is an ill-posed problem, because the step-by-step reconstruction of a fibre bundle trajectory cannot afford any serious mistake in the evaluation of the local fibre orientations. Such evaluation is difficult, however, because the myriad fibres passing through a single voxel follow different directions. Modelling tractography as a global inverse problem is a simple framework which addresses the ill-posed nature of the problem. The key idea is that the results of tractography in the neighbourhood of an ambiguous local diffusion profile can help to infer the local fibre directions. This paper provides an overview of past achievements of global tractography and proposes guidelines for a future research programme in the hope that the potential of the technique will increase the interest of the community.
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“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 Mangin et al. 2013
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Cites
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?
Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S., & Woolrich, M. W. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.09.018 · Cited by 3,471
Nonuniversal critical dynamics in Monte Carlo simulations
Swendsen, R. H., & Wang, J. (1987) · Physical Review Letters · doi:10.1103/physrevlett.58.86 · Cited by 2,840
The Human Connectome Project: A data acquisition perspective
Essen, D. C. V., Uǧurbil, K., Auerbach, E. J., Barch, D. M., Behrens, T. E. J., Bucholz, R. D., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.018 · Cited by 2,638
Axcaliber: A method for measuring axon diameter distribution from diffusion MRI
Assaf, Y., Blumenfeld‐Katzir, T., Yovel, Y., & Basser, P. J. (2008) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21577 · Cited by 849
Deterministic and probabilistic tractography based on complex fibre orientation distributions
Descoteaux, M., Deriche, R., Knosche, T. R., & Anwander, A. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.2004424 · Cited by 698
Tractography: Where Do We Go from Here?
Jbabdi, S., & Johansen-Berg, H. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0033 · Cited by 614
The Geometric Structure of the Brain Fiber Pathways
Wedeen, V. J., Rosene, D. L., Wang, R., Dai, G., Mortazavi, F., Hagmann, P., Kaas, J. H., & Tseng, W.-Y. I. (2012) · Science · doi:10.1126/science.1215280 · Cited by 445
Detection of linear features in SAR images: application to road network extraction
Florence Tupin, D., Maı̂tre, H., Mangin, J.-F., Nicolas, J., & Pechersky, E. A. (1998) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.662728 · Cited by 428
Self-citationRegularization of Diffusion-Based Direction Maps for the Tracking of Brain White Matter Fascicles
Poupon, C., Clark, C., Frouin, V., Régis, J. M., Isabelle Bloch, A., Bihan, D. L., & Mangin, J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0607 · Cited by 426
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Nimsky, C., & Carl, B. (2017) · Springer eBooks · doi:10.1007/978-3-662-48694-8_10
MRI-based Measurement of Structural and Functional Connectivity in Health and Disease
Sinke, M. R. T. (2020) · Utrecht University Repository (Utrecht University) · doi:10.33540/77
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ConvTract: A Convolutional-based Framework for TractographyPreprint
Hosseini, S. M. H., Hassanpour, M., Masoudnia, S., Nazem-Zadeh, M.-R., & Asheghabadi, M. H. (2021) · doi:10.36227/techrxiv.16919056.v1
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Hosseini, S. M. H., Asheghabadi, M. H., Masoudnia, S., Nazem-Zadeh, M.-R., & Asheghabadi, M. H. (2022) · doi:10.36227/techrxiv.16919056.v3
ConvTract: A Convolutional-based Framework for TractographyPreprint
Hosseini, S. M. H., Asheghabadi, M. H., Masoudnia, S., & Nazem-Zadeh, M.-R. (2021) · doi:10.36227/techrxiv.16919056
ConvTract: A Convolutional-based Framework for TractographyPreprint
Hosseini, S. M. H., Asheghabadi, M. H., Masoudnia, S., Nazem-Zadeh, M.-R., & Asheghabadi, M. H. (2021) · doi:10.36227/techrxiv.16919056.v2
Structural network construction using diffusion MRI
Blommaert, J., & Christiaens, D. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-85280-7.00007-5
Chauvel, M., Uszynski, I., Poupon, C., & Hopkins, W. D. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1611611
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Stamile, C. (2017) · theses.fr (ABES) · doi:10.70675/0fa97c67z9c1cz45a7z9670z56432af170c6
Chauvel, M. (2023) · theses.fr (ABES) · doi:10.70675/243d3998zd318z4ce4z8d5cz99f877a66861 · Cited by 1
Wen, R., Zhang, J., & Liang, Z. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.06.723316
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Metadata
- Authors
- Mangin, J.-F., Fillard, P., Cointepas, Y., Le Bihan, D., Frouin, V., & Poupon, C.
- Affiliations
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Corresponding author
- Mangin, J.-F.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2013-10
- Volume
- 80
- Pages
- 290-296
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2138625648
- Cited by
- 121
- References
- 43
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 6 · 2023: 4 · 2022: 9 · 2021: 11 · 2020: 8 · 2019: 8 · 2018: 13 · 2017: 12 · 2016: 11 · 2015: 12 · 2014: 11 · 2013: 5
- FWCI
- 10.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- diffusion MRI, ill-posed inverse problems, white matter tracts
- MeSH terms
- Animals, Brain, Humans, Image Interpretation, Computer-Assisted, Models, Anatomic, Models, Neurological, Nerve Fibers, Myelinated, Nerve Net, Diffusion Tensor Imaging, Connectome
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