Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information
Abstract
Diffusion MRI streamlines tractography suffers from a number of inherent limitations, one of which is the accurate determination of when streamlines should be terminated. Use of an accurate streamlines propagation mask from segmentation of an anatomical image confines the streamlines to the volume of the brain white matter, but does not take full advantage of all of the information available from such an image. We present a modular addition to streamlines tractography, which makes more effective use of the information available from anatomical image segmentation, and the known properties of the neuronal axons being reconstructed, to apply biologically realistic priors to the streamlines generated; we refer to this as "Anatomically-Constrained Tractography". Results indicate that some of the known false positives associated with tractography algorithms are prevented, such that the biological accuracy of the reconstructions should be improved, provided that state-of-the-art streamlines tractography methods are used.
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?
Common termination criteria based on heuristic thresholds are not adequate to ensure biologically plausible streamline generation.
“These common termination criteria are therefore not adequate to ensure biologically plausible streamline generation”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Smith et al., 2012
Anatomically constrained tractography and similar methods constrain streamline propagation and termination using knowledge of where neuronal fibers locate, preventing streamlines that cannot represent biological connectivity.
“the Anatomically-Constrained Tractography (ACT) framework (Smith et al., 2012) and similar methods (Girard and Descoteaux, 2012; Lemkaddem et al., 2014; Yeh et al., 2017) ensure that both streamline propagation and termination are constrained based on knowledge of where neuronal fibers locate, e.g., connecting between gray matter areas via the white matter.”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Smith et al., 2012
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
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
Self-citationDTI mapping of human brain connectivity: Statistical fibre tracking and virtual dissection
Hagmann, P., Thiran, J.-P., Jonasson, L., Vandergheynst, P., Clarke, S., Maeder, P., & Meuli, R. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00142-3 · Cited by 326 · 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-citationDeterministic diffusion fiber tracking improved by quantitative anisotropy
Yeh, F.-C., Verstynen, T. D., Wang, Y., Fernández-Miranda, J. C., & Tseng, W.-Y. I. (2013) · PLoS ONE · doi:10.1371/journal.pone.0080713 · Cited by 1,121 · 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
Self-citationDiffusion 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
“More advanced criteria are based on anatomical priors, and reject tracks that terminate in cerebrospinal fluid or within white matter and only accept tracks that connect different gray matter regions.(44,46)”
BackgroundTowards quantitative connectivity analysis: reducing tractography biases
Girard, G., Whittingstall, K., Deriche, R., & Descoteaux, M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.04.074 · Cited by 324 · 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
“anywhere in the brain, the Anatomically-Constrained Tractography (ACT) framework [414] and similar methods [164, 515]”
Self-citationThe effects of SIFT on the reproducibility and biological accuracy of the structural connectome
Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.10.004 · Cited by 266 · see citing works
Self-citationTractometer: 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
The contribution of geometry to the human connectome
Roberts, J. A., Perry, A., Lord, A. R., Roberts, G., Mitchell, P. B., Smith, R. E., Calamante, F., & Breakspear, M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.09.009 · Cited by 213 · see citing works
Self-citationMapping 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
Self-citationSchilling, K. G., Rheault, F., Petit, L., Hansen, C. B., Nath, V., Yeh, F.-C., Girard, G., Barakovic, M., Rafael-Patino, J., Yu, T., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118502 · Cited by 182 · see citing works
Self-citationThe impact of in-scanner head motion on structural connectivity derived from diffusion MRI
Baum, G. L., Roalf, D. R., Cook, P. A., Ciric, R., Rosen, A. F. G., Xia, C., Elliott, M. A., Ruparel, K., Verma, R., Tunç, B., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.02.041 · Cited by 116 · 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
Real-time multi-peak tractography for instantaneous connectivity display
Chamberland, M., Whittingstall, K., Fortin, D., Mathieu, D., & Descoteaux, M. (2014) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00059 · Cited by 84 · see citing works
Robust and efficient linear registration of white-matter fascicles in the space of streamlines
Garyfallidis, E., Ocegueda, O., Wassermann, D., & Descoteaux, M. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.05.016 · Cited by 84 · see citing works
Aydogan, D. B., Jacobs, R., Dulawa, S., Thompson, S. L., Francois, M. C., Toga, A. W., Dong, H., Knowles, J. A., & Shi, Y. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1663-8 · Cited by 72 · 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
Connections of the human orbitofrontal cortex and inferior frontal gyrus
Heather Hsu, C.-C., Rolls, E. T., Huang, C.-C., Chong, S. T., Zac Lo, C.-Y., Feng, J., & Lin, C.-P. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa160 · Cited by 67 · see citing works
Fiber length profiling: a novel approach to structural brain organization
Bajada, C. J., Schreiber, J., & Caspers, S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.070 · Cited by 55 · see citing works
Gong, S., Zhang, F., Norton, I., Essayed, W. I., Unadkat, P., Rigolo, L., Pasternak, O., Rathi, Y., Hou, L., Golby, A. J., et al. (2018) · PLOS ONE · doi:10.1371/journal.pone.0197056 · Cited by 51 · see citing works
Maternal depressive symptoms, neonatal white matter, and toddler social-emotional development
Lautarescu, A., Bonthrone, A. F., Pietsch, M., Batalle, D., Cordero-Grande, L., Tournier, J.-D., Christiaens, D., Hajnal, J. V., Chew, A., Falconer, S., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02073-y · Cited by 29 · see citing works
Self-citationPujol, S., Cabeen, R., Sébille, S. B., Yelnik, J., François, C., Fernandez Vidal, S., Karachi, C., Zhao, Y., Cosgrove, G. R., Jannin, P., et al. (2017) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2016.00119 · Cited by 21 · see citing works
Hierarchical microstructure informed tractography
Ocampo-Pineda, M., Schiavi, S., Rheault, F., Girard, G., Petit, L., Descoteaux, M., & Daducci, A. (2021) · Brain Connectivity · doi:10.1089/brain.2020.0907 · Cited by 19 · see citing works
Towards reliable reconstruction of the mouse brain corticothalamic connectivity using diffusion MRI
Arefin, T. M., Lee, C. H., Liang, Z., Rallapalli, H., Wadghiri, Y. Z., Turnbull, D. H., & Zhang, J. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120111 · Cited by 17 · see citing works
Popp, J. L., Thiele, J. A., Faskowitz, J., Seguin, C., Sporns, O., & Hilger, K. (2025) · Communications Biology · doi:10.1038/s42003-025-08231-4 · Cited by 17 · see citing works
Trinkle, S., Foxley, S., Wildenberg, G., Kasthuri, N., & La Rivière, P. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118576 · Cited by 8 · see citing works
- Authors
- Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A.
- Affiliations
- The University of Melbourne (AU)
- Corresponding author
- Smith, Robert E.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-09
- Volume
- 62
- Issue
- 3
- Pages
- 1924-1938
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2094435366
- Cited by
- 1,216
- References
- 3
- Citations by year
- 2026: 128 · 2025: 160 · 2024: 152 · 2023: 122 · 2022: 152 · 2021: 144 · 2020: 127 · 2019: 67 · 2018: 55 · 2017: 43 · 2016: 24 · 2015: 21 · 2014: 12 · 2013: 8 · 2012: 1
- FWCI
- 10.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Research Portal (King's College London) (submittedVersion)
- Funders
- National Health and Medical Research Council (NHMRC) of Australia
- the Australian Research Council
- Austin Health
- Dowd Foundation Research Scholarship for Neuroscience
- Victorian Government
- 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
- biological priors, white matter, connectome reconstruction, axon reconstruction
- MeSH terms
- Brain, Brain Mapping, Humans, Image Interpretation, Computer-Assisted, Diffusion Tensor Imaging
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
