Real-time multi-peak tractography for instantaneous connectivity display
Open accessAbstract
The computerized process of reconstructing white matter tracts from diffusion MRI (dMRI) data is often referred to as tractography. Tractography is nowadays central in structural connectivity since it is the only non-invasive technique to obtain information about brain wiring. Most publicly available tractography techniques and most studies are based on a fixed set of tractography parameters. However, the scale and curvature of fiber bundles can vary from region to region in the brain. Therefore, depending on the area of interest or subject (e.g., healthy control vs. tumor patient), optimal tracking parameters can be dramatically different. As a result, a slight change in tracking parameters may return different connectivity profiles and complicate the interpretation of the results. Having access to tractography parameters can thus be advantageous, as it will help in better isolating those which are sensitive to certain streamline features and potentially converge on optimal settings which are area-specific. In this work, we propose a real-time fiber tracking (RTT) tool which can instantaneously compute and display streamlines. To achieve such real-time performance, we propose a novel evolution equation based on the upsampled principal directions, also called peaks, extracted at each voxel of the dMRI dataset. The technique runs on a single Computer Processing Unit (CPU) without the need for Graphical Unit Processing (GPU) programming. We qualitatively illustrate and quantitatively evaluate our novel multi-peak RTT technique on phantom and human datasets in comparison with the state of the art offline tractography from MRtrix, which is robust to fiber crossings. Finally, we show how our RTT tool facilitates neurosurgical planning and allows one to find fibers that infiltrate tumor areas, otherwise missing when using the standard default tracking parameters.
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Tractography results can be sensitive to parameter choices within a given algorithm, such as seeding and stopping thresholds.
“On the other hand, tractography results can also be sensitive to the choice of parameters (such as seeding and stopping thresholds) within a certain algorithm (Chamberland et al., 2014; Côté et al., 2013; Gong et al., 2018; Moldrich et al., 2010; Xie et al., 2020).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Chamberland et al., 2014
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Self-citationDimensionless, Scale Invariant, Edge Weight Metric for the Study of Complex Structural Networks
Colón-Pérez, L. M., Spindler, C., Goicochea, S., Triplett, W. T., Parekh, M. B., Montie, E. W., Carney, P. R., Price, C. C., & Mareci, T. H. (2015) · PLoS ONE · doi:10.1371/journal.pone.0131493 · Cited by 15 · see citing works
Asymmetric fiber trajectory distribution estimation using streamline differential equation
Feng, Y., & He, J. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101686 · Cited by 15 · see citing works
Tract-specific MRI measures explain learning and recall differences in multiple sclerosis
Winter, M., Tallantyre, E. C., Brice, T. A. W., Robertson, N. P., Jones, D. K., & Chamberland, M. (2021) · Brain Communications · doi:10.1093/braincomms/fcab065 · Cited by 15 · see citing works
Self-citationTheaud, G., Edde, M., Dumont, M., Zotti, C., Zucchelli, M., Deslauriers‐Gauthier, S., Rachid Deriche, B., Jodoin, P., & Descoteaux, M. (2022) · Frontiers in Neuroimaging · doi:10.3389/fnimg.2022.917806 · Cited by 14 · see citing works
Self-citationObtaining Representative Core Streamlines for White Matter Tractometry of the Human Brain
Chamberland, M., St‐Jean, S., Tax, C. M. W., & Jones, D. K. (2019) · Mathematics and visualization · doi:10.1007/978-3-030-05831-9_28 · Cited by 13 · see citing works
Self-citationOn PTV definition for glioblastoma based on fiber tracking of diffusion tensor imaging data
Witulla, B., Goerig, N. L., Putz, F., Frey, B., Engelhorn, T., Dörfler, A., Uder, M., Fietkau, R., Bert, C., & Laun, F. B. (2020) · PLoS ONE · doi:10.1371/journal.pone.0227146 · Cited by 12 · see citing works
Reymbaut, A., Caron, A. V., Gilbert, G., Szczepankiewicz, F., Nilsson, M., Warfield, S. K., Descoteaux, M., & Scherrer, B. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101988 · Cited by 12 · see citing works
Self-citationFiberweb: Diffusion Visualization and Processing in the Browser
Ledoux, L.-P., Morency, F. C., Cousineau, M., Houde, J., Whittingstall, K., & Descoteaux, M. (2017) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2017.00054 · Cited by 12 · see citing works
Self-citationJarret, J., Boré, A., Bedetti, C., Descoteaux, M., & Brambati, S. M. (2021) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2021.109435 · Cited by 9 · see citing works
Self-citationWhite matter fiber analysis using kernel dictionary learning and sparsity priors
Kumar, K., Kaleem Siddiqi, A., & Desrosiers, C. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.06.002 · Cited by 8 · see citing works
Chamberland, M., Winter, M., Brice, T. A. W., Jones, D. K., & Tallantyre, E. C. (2021) · Mathematics and visualization · doi:10.1007/978-3-030-73018-5_18 · Cited by 7 · see citing works
Self-citationMultiXplore: Visual exploration platform for multimodal neuroimaging data
Bakhshmand, S. M., KHAN, A. R., de Ribaupierre, S., & Eagleson, R. A. (2017) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2017.07.006 · Cited by 7 · see citing works
Visualization of Diffusion Propagator and Multiple Parameter Diffusion Signal
Vaillancourt, O., Chamberland, M., Houde, J., & Descoteaux, M. (2015) · Mathematics and visualization · doi:10.1007/978-3-319-15090-1_10 · Cited by 6 · see citing works
Self-citationMeyer, M., Scala, G. D., Edde, M., Dilharreguy, B., Radat, F., Michèle Allard, A., & Chanraud, S. (2017) · Behavioral and Brain Functions · doi:10.1186/s12993-017-0124-5 · Cited by 6 · see citing works
Fiblets for Real‐Time Rendering of Massive Brain Tractograms
Schertzer, J., Mercier, C., Rousseau, S., & Boubekeur, T. (2022) · Computer Graphics Forum · doi:10.1111/cgf.14486 · Cited by 5 · see citing works
Visual exploration of HARDI fibers with probabilistic tracking
Liang, R., Wang, Z., Zhang, S., Feng, Y., Li, J., Ma, X., Chen, W., & Tate, D. F. (2015) · Information Sciences · doi:10.1016/j.ins.2015.04.045 · Cited by 5 · see citing works
Real-time interactive tractography analysis for multimodal brain visualization tool: MultiXplore
Bakhshmand, S. M., de Ribaupierre, S., & Eagleson, R. A. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2253907 · Cited by 4 · see citing works
Jordan, K., Keshavan, A., Caverzasi, E., Osorio, J. A., Papinutto, N., Amirbekian, B., Berger, M. S., & Henry, R. G. (2020) · Journal of Neuroimaging · doi:10.1111/jon.12713 · Cited by 4 · see citing works
Verschuur, A. S., Tax, C. M. W., Boomsma, M. F., Carlson, H. L., van Wezel‐Meijler, G., King, R., Leemans, A., & Leijser, L. M. (2024) · Frontiers in Radiology · doi:10.3389/fradi.2024.1416672 · Cited by 3 · see citing works
Interactive DTI-Based Constrained Tractography using OpenGL Pipeline
Wu, S.-T., Voltoline, R., & Benites, R. L. (2023) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi59091.2023.10347149 · Cited by 3 · see citing works
Visual interactive exploration and clustering of brain fiber tracts
Xu, C., Liu, Y., Jiang, Z., Sun, G., Li, J., & Liang, R. (2020) · Journal of Visualization · doi:10.1007/s12650-020-00642-1 · Cited by 3 · see citing works
Witt, A. A., Fleishman, S., Houston, D., Prock, L. E., Sweeney, G., McGonigle, T., Vandekar, S., Chamberland, M., Stubblefield, S. K., McKnight, C. D., et al. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.72 · Cited by 2 · see citing works
Self-citationNeurosurgical applications of clinical tractography
Bizzi, A., Yang, J. Y., Aliaga-Arias, J., Dell’Acqua, F., Lavrador, J. P., & Vergani, F. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00038-0 · Cited by 2 · see citing works
Casella, C., Kelly, B., Murillo, A., Mills-Smith, I., Parker, G., von Ruhland, C., Syed, Y. A., Dion, V., Metzler-Baddeley, C., Rosser, A., et al. (2026) · Neurobiology of Disease · doi:10.1016/j.nbd.2026.107318 · Cited by 2 · see citing works
Cluster-viz: A Tractography QC Tool
Jordan, K., Keshavan, A., Mandelli, M. L., & Henry, R. G. (2017) · Research Ideas and Outcomes · doi:10.3897/rio.3.e12394 · Cited by 1 · see citing works
A taxonomic guide to diffusion MRI tractography visualization tools
Laamoumi, M., Hendriks, T., & Chamberland, M. (2024) · NMR in Biomedicine · doi:10.1002/nbm.5267 · Cited by 1 · see citing works
Self-citationChamberland, M., Poirier, C., Hendriks, T., Shastin, D., Vilanova, A., & Leemans, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00002-1 · Cited by 1 · see citing works
Self-citationMagnetic Resonance Imaging of $$T_2$$- and Diffusion Anisotropy Using a Tiltable Receive Coil
Tax, C. M. W., Kleban, E., Baraković, M., Chamberland, M., & Jones, D. K. (2021) · Mathematics and visualization · doi:10.1007/978-3-030-56215-1_12 · Cited by 1 · see citing works
Self-citationData driven methods for characterizing individual differences in brain MRI
Kumar, K. (2018) · Espace ÉTS (ETS) · doi:10.13140/rg.2.2.12193.71525 · Cited by 1 · see citing works
Multicenter studies and harmonization: Problems, solutions, and open challenges
Tax, C. M. W., Karayumak, S. C., Schilling, K. G., Moyer, D., Landman, B. A., Jahanshad, N., & Yogesh Rathi, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00039-2
Tractography in pathological anatomy: Some general considerations
Theaud, G., Edde, M., Leemans, A., Dell’Acqua, F., Yang, J. Y., & Descoteaux, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00036-7
Self-citationDissecting white matter pathways: Automatic and semiautomatic approaches
Garyfallidis, E., Côté, M.-A., Rheault, F., & Olivetti, E. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00013-6
Multi-dimensional Parameter Space Exploration for Streamline-Specific Tractography
Vink, R., Vilanova, A., & Chamberland, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-86920-4_7
Self-citationReal-time tractography: computation and visualization
Chamberland, M., Yang, J. Y., & Aydogan, D. B. (2025) · Brain Structure and Function · doi:10.1007/s00429-025-02928-2
Self-citationBisten, J., von Bornhaupt, V., Gruen, J., Bauer, T., Rüber, T., & Schultz, T. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1168
- Authors
- Chamberland, M., Whittingstall, K., Fortin, D., Mathieu, D., & Descoteaux, M.
- Affiliations
- Université de Sherbrooke (CA)
- Corresponding author
- Chamberland, Maxime
- Descoteaux, Maxime
- Venue
- Frontiers in Neuroinformatics
- Publisher
- Frontiers Media SA
- Published
- 2014-05-30
- Volume
- 8
- Pages
- 59-59
- ISSN
- 1662-5196 (ISSN-L)
- PMC
- PMC4038925
- OpenAlex
- W2080094149
- Cited by
- 84
- References
- 48
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 3 · 2023: 4 · 2022: 4 · 2021: 7 · 2020: 11 · 2019: 7 · 2018: 9 · 2017: 11 · 2016: 6 · 2015: 11 · 2014: 1
- FWCI
- 7.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Neuroinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- Funders
- Réseau en Bio-Imagerie du Quebec
- Natural Sciences and Engineering Research Council of Canada
- 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, structural connectivity, evolution equations, principal directions, neurosurgical planning, crossing fibers
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