Robust and efficient linear registration of white-matter fascicles in the space of streamlines

Garyfallidis, Eleftherios; Ocegueda, Omar; Wassermann, Demian; Descoteaux, Maxime

doi:10.1016/j.neuroimage.2015.05.016

Cited by 84Advanced Neuroimaging Techniques and ApplicationsMedical Image Segmentation TechniquesFunctional Brain Connectivity Studies

Robust and efficient linear registration of white-matter fascicles in the space of streamlines

Garyfallidis, Eleftherios, Ocegueda, Omar, Wassermann, Demian, Descoteaux, Maxime·2015·NeuroImage

DOI 10.1016/j.neuroimage.2015.05.016

Open this paper in CoChat — free account

Abstract

The neuroscientific community today is very much interested in analyzing specific white matter bundles like the arcuate fasciculus, the corticospinal tract, or the recently discovered Aslant tract to study sex differences, lateralization and many other connectivity applications. For this reason, experts spend time manually segmenting these fascicles and bundles using streamlines obtained from diffusion MRI tractography. However, to date, there are very few computational tools available to register these fascicles directly so that they can be analyzed and their differences quantified across populations. In this paper, we introduce a novel, robust and efficient framework to align bundles of streamlines directly in the space of streamlines. We call this framework Streamline-based Linear Registration. We first show that this method can be used successfully to align individual bundles as well as whole brain streamlines. Additionally, if used as a piecewise linear registration across many bundles, we show that our novel method systematically provides higher overlap (Jaccard indices) than state-of-the-art nonlinear image-based registration in the white matter. We also show how our novel method can be used to create bundle-specific atlases in a straightforward manner and we give an example of a probabilistic atlas construction of the optic radiation. In summary, Streamline-based Linear Registration provides a solid registration framework for creating new methods to study the white ma…

Integrity signals

Registry

Crossref · 10.1016/j.neuroimage.2015.05.016

Cites

91 of 91 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Most widely used streamline labeling approaches perform linear registration, while many studies have proposed non-linear approaches to enable deformations of the streamlines.
“Most widely used approaches perform a linear registration (Garyfallidis et al., 2015; O’Donnell et al., 2012), while many studies have proposed to perform non-linear approaches to enable deformations of the streamlines (Benou et al., 2019; Chandio and Garyfallidis, 2020; Olivetti et al., 2016).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Garyfallidis et al., 2015

Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography
Fischler, M. A., & Bolles, R. C. (1981) · Communications of the ACM · doi:10.1145/358669.358692 · Cited by 25,893 · see citing works
A Method for Registration of 3-D Shapes
Besl, P., & Mckay, N. D. (1992) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.121791 · Cited by 22,163 · see citing works
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
FSL
Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.015 · Cited by 11,314 · see citing works
FreeSurfer
Fischl, B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.021 · Cited by 9,526 · see citing works
Voxel-Based Morphometry—The Methods
Ashburner, J., & Friston, K. J. (2000) · NeuroImage · doi:10.1006/nimg.2000.0582 · Cited by 8,676 · see citing works
A fast diffeomorphic image registration algorithm
Ashburner, J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.007 · Cited by 8,122 · see citing works
A Limited Memory Algorithm for Bound Constrained Optimization
Byrd, R. H., Lu, P., Nocedal, J. M., & Zhu, C. (1995) · SIAM Journal on Scientific Computing · doi:10.1137/0916069 · Cited by 6,267 · see citing works
Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data
Smith, S. M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T. E., Mackay, C., Watkins, K., Ciccarelli, O., Cader, M., Matthews, P., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.02.024 · Cited by 6,035 · see citing works
Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain
Avants, B., Epstein, C. L., Grossman, M., & Gee, J. C. (2007) · Medical Image Analysis · doi:10.1016/j.media.2007.06.004 · Cited by 5,618 · see citing works
Algorithm 778: L-BFGS-B
Zhu, C., Byrd, R. H., Lu, P., & Nocedal, J. M. (1997) · ACM Transactions on Mathematical Software · doi:10.1145/279232.279236 · Cited by 3,474 · see citing works
Iterative point matching for registration of free-form curves and surfaces
Zhang, Z. (1994) · International Journal of Computer Vision · doi:10.1007/bf01427149 · Cited by 2,557 · see citing works
Method for registration of 3-D shapes
Besl, P. J., & McKay, N. D. (1992) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.57955 · Cited by 2,448 · see citing works
Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration
Klein, A., Andersson, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M.-C., Christensen, G. E., Collins, D. L., Gee, J., Hellier, P., et al. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.12.037 · Cited by 2,227 · see citing works
Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution
Tournier, J., Calamante, F., & Connelly, A. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.02.016 · Cited by 2,191 · see citing works
Perisylvian language networks of the human brain
Catani, M., Jones, D. K., & ffytche, D. H. (2005) · Annals of Neurology · doi:10.1002/ana.20319 · Cited by 1,832 · see citing works
Tracking neuronal fiber pathways in the living human brain
Conturo, T. E., Lori, N. F., Cull, T. S., Akbudak, E., Snyder, A. Z., Shimony, J. S., McKinstry, R. C., Burton, H., & Raichle, M. E. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.18.10422 · Cited by 1,774 · see citing works
Reproducibility of quantitative tractography methods applied to cerebral white matter
Wakana, S., Caprihan, A., Panzenboeck, M. M., Fallon, J. H., Perry, M., Gollub, R. L., Hua, K., Zhang, J., Jiang, H., Dubey, P., et al. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.02.049 · Cited by 1,686 · see citing works
A diffusion tensor imaging tractography atlas for virtual in vivo dissections.
CATANI, M., & THIEBAUTDESCHOTTEN, M. (2008) · Cortex · doi:10.1016/j.cortex.2008.05.004 · Cited by 1,642 · see citing works
Virtual in vivo interactive dissection of white matter fasciculi in the human brain
Catani, M., Howard, R. J., Pajevic, S., & Jones, D. K. (2002) · NeuroImage · doi:10.1006/nimg.2002.1136 · Cited by 1,637 · see citing works
Medical Image Computing and Computer-Assisted Intervention
Yang, G., Hawkes, D. J., Rueckert, D., Noble, J. A., & Taylor, C. (2009) · Research Explorer (The University of Manchester) · Cited by 1,591 · see citing works
Deformable medical image registration: a survey
Sotiras, A., Davatzikos, C., & Paragios, N. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2265603 · Cited by 1,587 · see citing works
Dipy, a library for the analysis of diffusion MRI data
Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., van der Walt, S., Descoteaux, M., Nimmo-Smith, I., & Contributors, D. (2014) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00008 · Cited by 1,371 · see citing works
Self-citation
MRtrix: Diffusion tractography in crossing fiber regions
Tournier, J., Calamante, F., & Connelly, A. (2012) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22005 · Cited by 1,370 · see citing works
Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information
Smith, R. E., Tournier, J.-D., Calamante, F., & Connelly, A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.005 · Cited by 1,216 · see citing works
Topography of the human corpus callosum revisited—Comprehensive fiber tractography using diffusion tensor magnetic resonance imaging
Hofer, S., & Frahm, J. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.05.044 · Cited by 1,197 · see citing works
Deformable templates using large deformation kinematics
Christensen, G. E., Rabbitt, R. D., & Miller, M. I. (1996) · IEEE Transactions on Image Processing · doi:10.1109/83.536892 · Cited by 1,146 · see citing works
A lateralized brain network for visuospatial attention
de Schotten, M. T., Dell'Acqua, F., Forkel, S. J., Simmons, A., Vergani, F., Murphy, D. G. M., & Catani, M. (2011) · Nature Neuroscience · doi:10.1038/nn.2905 · Cited by 1,103 · see citing works
MRI Atlas of Human White Matter
Mori, S. (2005) · Cited by 1,103 · see citing works
Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging
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
MRI atlas of human white matter
Ling, C. L., & Rumpel, H. (2006) · Concepts in Magnetic Resonance Part A · doi:10.1002/cmr.a.20052 · Cited by 1,021 · see citing works
Robust registration of 2D and 3D point sets
Fitzgibbon, A. W. (2003) · Image and Vision Computing · doi:10.1016/j.imavis.2003.09.004 · Cited by 925 · see citing works
PET-CT image registration in the chest using free-form deformations
Mattes, D., Haynor, D. R., Vesselle, H. J., Lewellen, T. K., & Eubank, W. B. (2003) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.809072 · Cited by 867 · see citing works
Tract profiles of white matter properties: automating fiber-tract quantification
Yeatman, J. D., Dougherty, R. F., Myall, N. J., Wandell, B. A., & Feldman, H. M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0049790 · Cited by 840 · see citing works
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 · see citing works
Self-citation
Robust Point Set Registration Using Gaussian Mixture Models
Jian, B., & Vemuri, B. C. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.223 · Cited by 682 · see citing works
Ubiquitous Log Odds: A Common Representation of Probability and Frequency Distortion in Perception, Action, and Cognition
Zhang, H., & Maloney, L. T. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00001 · Cited by 679 · see citing works
Symmetries in human brain language pathways correlate with verbal recall
Catani, M., Allin, M. P. G., Husain, M., Pugliese, L., Mesulam, M. M., Murray, R. M., & Jones, D. K. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0702116104 · Cited by 650 · see citing works
Consistent image registration
Christensen, G. E., & Johnson, H. J. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.932742 · Cited by 638 · see citing works
Apparent Fibre Density: A novel measure for the analysis of diffusion-weighted magnetic resonance images
Raffelt, D., Tournier, J.-D., Rose, S., Ridgway, G., Henderson, R., Crozier, S., Salvado, O., & Connelly, A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.045 · Cited by 616 · see citing works
Imaging cortical association tracts in the human brain using diffusion‐tensor‐based axonal tracking
Mori, S., Kaufmann, W. E., Davatzikos, C. A., Stieltjes, B., Amodei, L., Fredericksen, K., Pearlson, G. D., Melhem, E. R., Solaiyappan, M., Raymond, G. V., et al. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10074 · Cited by 558 · see citing works
The correlation ratio as a new similarity measure for multimodal image registration
Roche, A., Malandain, G., Pennec, X., & Ayache, N. (1998) · Lecture notes in computer science · doi:10.1007/bfb0056301 · Cited by 549 · see citing works
Connectivity-Based Parcellation of Broca's Area
Anwander, A., Tittgemeyer, M., von Cramon, D., Friederici, A., & Knosche, T. (2006) · Cerebral Cortex · doi:10.1093/cercor/bhk034 · Cited by 531 · see citing works
A novel frontal pathway underlies verbal fluency in primary progressive aphasia
Catani, M., Mesulam, M. M., Jakobsen, E., Malik, F., Martersteck, A., Wieneke, C. A., Thompson, C. K., de Schotten, M. T., Dell’Acqua, F., Weıntraub, S., et al. (2013) · Brain · doi:10.1093/brain/awt163 · Cited by 480 · see citing works
Atlas of Human Brain Connections
Catani, M., & Thiebaut de Schotten, M. (2012) · doi:10.1093/med/9780199541164.001.0001 · Cited by 467 · see citing works
Remark on “algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound constrained optimization”
Morales, J. L., & Nocedal, J. (2011) · ACM Transactions on Mathematical Software · doi:10.1145/2049662.2049669 · Cited by 460 · see citing works
Variational problems on flows of diffeomorphisms for image matching
Dupuis, P., Grenander, U., & Miller, M. I. (1998) · Quarterly of Applied Mathematics · doi:10.1090/qam/1632326 · Cited by 447 · see citing works
Methodological considerations on tract-based spatial statistics (TBSS)
Bach, M., Laun, F. B., Leemans, A., Tax, C. M. W., Biessels, G. J., Stieltjes, B., & Maier-Hein, K. H. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.021 · Cited by 445 · see citing works
Automatic Tractography Segmentation Using a High-Dimensional White Matter Atlas
O’Donnell, L. J., & Westin, C. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.906785 · Cited by 402 · see citing works
Medical Image Computing and Computer Assisted Intervention – MICCAI 2019
Shen, D. G., Liu, T., Peters, T. M., Staib, L. H., Essert, C., Zhou, S., Yap, P., & Khan, A. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32251-9 · Cited by 395 · see citing works
DTI 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
Towards 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
Self-citation
QuickBundles, a Method for Tractography Simplification
Garyfallidis, E., Brett, M., Correia, M., Williams, G. B., & Nimmo‐Smith, I. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00175 · Cited by 315 · see citing works
Self-citation
Global fiber reconstruction becomes practical
Reisert, M., Mader, I., Anastasopoulos, C., Weigel, M., Schnell, S., & Kiselev, V. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.09.016 · Cited by 312 · see citing works
Structural connectomics in brain diseases
Griffa, A., Baumann, P., Thiran, J., & Hagmann, P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.056 · Cited by 306 · see citing works
Morphometry of anatomical shape complexes with dense deformations and sparse parameters
Durrleman, S., Prastawa, M. W., Charon, N., Korenberg, J. R., Joshi, S. C., Gerig, G., & Trouvé, A. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.043 · Cited by 288 · see citing works
Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T
Landman, B. A., Farrell, J. A. D., Jones, C. K., Smith, S. A., Prince, J. L., & Mori, S. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.02.056 · Cited by 287 · see citing works
Symmetric diffeomorphic registration of fibre orientation distributions
Raffelt, D., Tournier, J.-D., Fripp, J., Crozier, S., Connelly, A., & Salvado, O. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.02.014 · Cited by 281 · see citing works
Lateralization of the arcuate fasciculus from childhood to adulthood and its relation to cognitive abilities in children
Lebel, C. A., & Beaulieu, C. (2009) · Human Brain Mapping · doi:10.1002/hbm.20779 · Cited by 281 · see citing works
High-Dimensional Spatial Normalization of Diffusion Tensor Images Improves the Detection of White Matter Differences: An Example Study Using Amyotrophic Lateral Sclerosis
Zhang, H. G., Avants, B., Yushkevich, P. A., Woo, J. H., Wang, S., McCluskey, L. F., Elman, L. B., Melhem, E. R., & Gee, J. C. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2007.906784 · Cited by 263 · see citing works
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011
Gabor Fichtinger, A., Peters, T., & Martel, A. L. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-23626-6 · Cited by 262 · see citing works
Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas
Guevara, P., Duclap, D., Poupon, C., Marrakchi‐Kacem, L., Fillard, P., Bihan, D. L., Leboyer, M., Houenou, J., & Mangin, J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.071 · Cited by 210 · see citing works
Group analysis of DTI fiber tract statistics with application to neurodevelopment
Goodlett, C., Fletcher, P. T., Gilmore, J. H., & Gerig, G. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.10.060 · Cited by 184 · see citing works
Volumetric, connective, and morphologic changes in the brains of children with chromosome 22q11.2 deletion syndrome: an integrative study
Simon, T. J., Ding, L., Bish, J. P., McDonald‐McGinn, D. M., Zackai, E. H., & Gee, J. C. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.11.018 · Cited by 181 · see citing works
Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis
COROUGE, I., FLETCHER, P., JOSHI, S., GOUTTARD, S., & GERIG, G. (2006) · Medical Image Analysis · doi:10.1016/j.media.2006.07.003 · Cited by 177 · see citing works
Combined Volumetric and Surface Registration
Postelnicu, G. M., Zöllei, L., & Fischl, B. R. (2008) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2008.2004426 · Cited by 172 · see citing works
Improved DTI registration allows voxel-based analysis that outperforms Tract-Based Spatial Statistics
Schwarz, C., Reid, R. I., Gunter, J., Senjem, M., Przybelski, S., Zuk, S., Whitwell, J., Vemuri, P., Josephs, K., Kantarci, K., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.026 · Cited by 171 · see citing works
Structure-specific statistical mapping of white matter tracts
Yushkevich, P. A., Zhang, H. G., Simon, T. J., & Gee, J. C. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.01.013 · Cited by 166 · see citing works
Mapping of Histologically Identified Long Fiber Tracts in Human Cerebral Hemispheres to the MRI Volume of a Reference Brain: Position and Spatial Variability of the Optic Radiation
Bürgel, U., Schormann, T., Schleicher, A., & Zilles, K. (1999) · NeuroImage · doi:10.1006/nimg.1999.0497 · Cited by 140 · see citing works
The CONNECT project: Combining macro- and micro-structure
Assaf, Y., Alexander, D., Jones, D., Bizzi, A., Behrens, T. E., Clark, C., Cohen, Y., Dyrby, T., Hüppi, P., Knoesche, T., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.055 · Cited by 135 · see citing works
A Fast and Log-Euclidean Polyaffine Framework for Locally Linear Registration
Arsigny, V., Commowick, O., Ayache, N., & Pennec, X. (2009) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-008-0135-9 · Cited by 134 · see citing works
Automatic clustering of white matter fibers in brain diffusion MRI with an application to genetics
Jin, Y., Shi, Y., Zhan, L., Gutman, B. A., de Zubicaray, G. I., McMahon, K. L., Wright, M. J., Toga, A. W., & Thompson, P. M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.04.048 · Cited by 119 · see citing works
Unbiased Groupwise Registration of White Matter Tractography
O’Donnell, L. J., Wells, W. M., Golby, A. J., & Westin, C.-F. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33454-2_16 · Cited by 112 · see citing works
Identification and interpretation of microstructural abnormalities in motor pathways in adolescents born preterm
Groeschel, S., Tournier, J.-D., Northam, G., Baldeweg, T., Wyatt, J., Vollmer, B., & Connelly, A. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.10.034 · Cited by 99 · see citing works
Information Processing in Medical Imaging
Székely, G. J., & Hahn, H. K. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22092-0 · Cited by 98 · 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
Self-citation
Registration, atlas estimation and variability analysis of white matter fiber bundles modeled as currents
Durrleman, S., Fillard, P., Xavier Pennec, B., Trouvé, A., & Ayache, N. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.11.056 · Cited by 84 · see citing works
Multiscale white matter fiber tract coregistration: A new feature‐based approach to align diffusion tensor data
Leemans, A., Sijbers, J., Backer, S. D., Vandervliet, E. J. M., & Parizel, P. M. (2006) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20898 · Cited by 71 · see citing works
Improved tractography alignment using combined volumetric and surface registration
Zöllei, L., Stevens, A. A., Huber, K. E., Kakunoori, S., & Fischl, B. R. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.01.101 · Cited by 64 · see citing works
On Describing Human White Matter Anatomy: The White Matter Query Language
Wassermann, D., Makris, N., Yogesh Rathi, A., Shenton, M. E., Kikinis, R., Kubicki, M. R., & Westin, C. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40811-3_81 · Cited by 53 · see citing works
Self-citation
Partition-based mass clustering of tractography streamlines
Visser, E., Nijhuis, E. H. J., Buitelaar, J. K., & Zwiers, M. P. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.038 · Cited by 49 · see citing works
Nonlinear Registration of Diffusion MR Images Based on Fiber Bundles
Ziyan, U., Sabuncu, M. R., O’Donnell, L. J., & Westin, C. (2007) · Springer eBooks · doi:10.1007/978-3-540-75757-3_43 · Cited by 46 · see citing works
White Matter Bundle Registration and Population Analysis Based on Gaussian Processes
Wassermann, D., Yogesh Rathi, A., Sylvain Bouix, A., Kubicki, M. R., Kikinis, R., Shenton, M. E., & Westin, C. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22092-0_27 · Cited by 43 · see citing works
Self-citation
Co-registration of White Matter Tractographies by Adaptive-Mean-Shift and Gaussian Mixture Modeling
Zvitia, O., Mayer, A., Shadmi, R., Miron, S., & Greenspan, H. K. (2009) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2009.2029097 · Cited by 38 · see citing works
Automated tract extraction via atlas based Adaptive Clustering
Tunç, B., Parker, W. A., Ingalhalikar, M., & Verma, R. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.08.021 · Cited by 36 · see citing works
A Mathematical Framework for the Registration and Analysis of Multi-Fascicle Models for Population Studies of the Brain Microstructure
Taquet, M., Scherrer, B., Commowick, O., Peters, J. M., Şahin, M., Macq, B. M. M., & Warfield, S. K. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2289381 · Cited by 36 · see citing works
Tractography in the Study of the Human Brain: A Neurosurgical Perspective
Fortin, D., Aubin-Lemay, C., Boré, A., Girard, G., Houde, J., Whittingstall, K., & Descoteaux, M. (2012) · Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · doi:10.1017/s0317167100015560 · Cited by 30 · see citing works
Self-citation
A Supervised Framework for the Registration and Segmentation of White Matter Fiber Tracts
Mayer, A., Zimmerman‐Moreno, G., Shadmi, R., Batikoff, A., & Greenspan, H. K. (2010) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2067222 · Cited by 30 · see citing works
Cross-Subject Comparison of Local Diffusion MRI Parameters
Smith, S. M., Kindlmann, G., & Jbabdi, S. (2013) · Elsevier eBooks · doi:10.1016/b978-0-12-374709-9.00008-0 · Cited by 21 · see citing works
Adaptive mean-shift registration of white matter tractographies
Zvitia, O., Mayer, A., & Greenspan, H. K. (2008) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2008.4541090 · Cited by 10 · see citing works
Bundles of interest based registration of White Matter tractographies
Mayer, A., & Greenspan, H. K. (2008) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2008.4541147 · Cited by 7 · see citing works

Flagged first, then by citations

Sorted by citations

Tractography-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
Self-citation
Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review
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
“form a linear registration [347, 180], while many studies have proposed to perform non-linear approaches to enable deformations of the streamlines [355, 51, 84].”
Background
Recognition of white matter bundles using local and global streamline-based registration and clustering
Garyfallidis, E., Côté, M.-A., Rheault, F., Sidhu, J., Hau, J., Petit, L., Fortin, D., Cunanne, S., & Descoteaux, M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.015 · Cited by 279 · see citing works
Self-citation
Automated white matter fiber tract identification in patients with brain tumors
O’Donnell, L. J., Suter, Y., Rigolo, L., Kahali, P., Zhang, F., Norton, I. H., Albi, A., Olubiyi, O. I., Meola, A., Essayed, W. I., et al. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.11.023 · Cited by 129 · see citing works
Bundle analytics, a computational framework for investigating the shapes and profiles of brain pathways across populations
Chandio, B. Q., Risacher, S. L., Pestilli, F., Bullock, D. N., Yeh, F.-C. F., Koudoro, S., Rokem, A., Harezlak, J., & Garyfallidis, E. (2020) · Scientific Reports · doi:10.1038/s41598-020-74054-4 · Cited by 122 · see citing works
Self-citation
Evaluating the Reliability of Human Brain White Matter Tractometry
Kruper, J., Yeatman, J. D., Richie-Halford, A., Bloom, D., Grotheer, M., Caffarra, S., Kiar, G., Karipidis, I. I., Roy, E. A., Chandio, B. Q., et al. (2021) · Aperture Neuro · doi:10.52294/e6198273-b8e3-4b63-babb-6e6b0da10669 · Cited by 92 · see citing works
Self-citation
The visual white matter: The application of diffusion MRI and fiber tractography to vision science
Rokem, A., Takemura, H., Bock, A. S., Scherf, K. S., Behrmann, M., Wandell, B. A., Fine, I., Bridge, H., & Pestilli, F. (2017) · Journal of Vision · doi:10.1167/17.2.4 · Cited by 84 · see citing works
The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services
Avesani, P., McPherson, B., Hayashi, S., Caiafa, C. F., Henschel, R., Garyfallidis, E., Kitchell, L. M., Bullock, D. N., Patterson, A. J., Olivetti, E., et al. (2019) · Scientific Data · doi:10.1038/s41597-019-0073-y · Cited by 79 · see citing works
Self-citation
Fast Automatic Segmentation of White Matter Streamlines Based on a Multi-Subject Bundle Atlas
Labra, N., Guevara, P., Duclap, D., Houenou, J., Poupon, C., Mangin, J., & Figueroa, M. E. (2016) · Neuroinformatics · doi:10.1007/s12021-016-9316-7 · Cited by 47 · see citing works
Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation
Bertò, G., Bullock, D. N., Astolfi, P., Hayashi, S., Zigiotto, L., Annicchiarico, L., Corsini, F., BENEDICTIS, A. D., Sarubbo, S., Pestilli, F., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117402 · Cited by 44 · see citing works
Deep Diffusion MRI Registration (DDMReg): A Deep Learning Method for Diffusion MRI Registration
Zhang, F., Wells, W. M., & O’Donnell, L. J. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3139507 · Cited by 42 · see citing works
Fiberprint: A subject fingerprint based on sparse code pooling for white matter fiber analysis
Kumar, K., Desrosiers, C., Kaleem Siddiqi, A., Olivier Colliot, A., & Toews, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.083 · Cited by 33 · see citing works
Identifying Vulnerable Brain Networks in Mouse Models of Genetic Risk Factors for Late Onset Alzheimer’s Disease
Badea, A., Wu, W., Shuff, J., Wang, M. S., Anderson, R. J., Qi, Y. P., Johnson, G. A., Wilson, J. G., Koudoro, S., Garyfallidis, E., et al. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00072 · Cited by 31 · see citing works
Self-citation
DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks
Liu, F., Feng, J. H., Chen, G., Wu, Y., Hong, Y., Yap, P., & Shen, D. G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-35817-4_11 · Cited by 31 · see citing works
Image registration: Maximum likelihood, minimum entropy and deep learning
Sedghi, A., O’Donnell, L. J., Kapur, T., Miller, E. G., Mousavi, P., & Wells, W. M. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101939 · Cited by 30 · see citing works
Parsimonious Approximation of Streamline Trajectories in White Matter Fiber Bundles
Gori, P., Olivier Colliot, A., Marrakchi‐Kacem, L., Worbe, Y., Fallani, F. D. V., Chávez, M., Poupon, C., Hartmann, A., Ayache, N., & Durrleman, S. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2591080 · Cited by 26 · see citing works
FIESTA: Autoencoders for accurate fiber segmentation in tractography
Dumais, F., Legarreta, J. H., Lemaire, C., Poulin, P., Rheault, F., Petit, L., Baraković, M., Magon, S., Descoteaux, M., & Jodoin, P. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120288 · Cited by 26 · see citing works
Self-citation
Sheet Probability Index (SPI): Characterizing the geometrical organization of the white matter with diffusion MRI
Tax, C. M. W., Haije, T. D., Fuster, A., Westin, C., Viergever, M. A., Florack, L. M. J., & Leemans, A. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.07.042 · Cited by 23 · see citing works
Framework for shape analysis of white matter fiber bundles
Glozman, T., Bruckert, L., Pestilli, F., Yecies, D. W., Guibas, L. J., & Yeom, K. W. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.052 · Cited by 22 · see citing works
Neural function in DCC mutation carriers with and without mirror movements
Vosberg, D. E., Beaulé, V., Torres‐Berrío, A., Cooke, D., Chalupa, A., Jaworska, N., Cox, S. M. L., Larcher, K. M.-H., Zhang, Y., Allard, D., et al. (2019) · Annals of Neurology · doi:10.1002/ana.25418 · Cited by 22 · 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
Self-citation
Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods
He, J., Zhang, F., Pan, Y., Feng, Y., Rushmore, J., Torio, E., Yogesh Rathi, A., Makris, N., Kikinis, R., Golby, A. J., et al. (2023) · Human Brain Mapping · doi:10.1002/hbm.26497 · Cited by 19 · see citing works
BundleSeg: A Versatile, Reliable and Reproducible Approach to White Matter Bundle Segmentation
St‐Onge, E., Schilling, K. G., & Rheault, F. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47292-3_5 · Cited by 17 · see citing works
Comparison of spatial normalization strategies of diffusion MRI data for studying motor outcome in subacute-chronic and acute stroke
Moulton, E. J., Valabrègue, R., Díaz, B., Kemlin, C., Leder, S., Lehéricy, S., Samson, Y., & Rosso, C. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.002 · Cited by 17 · see citing works
Alignment of Tractograms As Graph Matching
Olivetti, E., Sharmin, N., & Avesani, P. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00554 · Cited by 17 · see citing works
TractCloud: Registration-Free Tractography Parcellation with a Novel Local-Global Streamline Point Cloud Representation
Xue, T., Chen, Y., Zhang, C., Golby, A. J., Makris, N., Yogesh Rathi, A., Cai, W., Zhang, F., & O’Donnell, L. J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43993-3_40 · Cited by 15 · see citing works
Comparison of distances for supervised segmentation of white matter tractography
Olivetti, E., Bertò, G., Gori, P., Sharmin, N., & Avesani, P. (2017) · International Workshop on Pattern Recognition in NeuroImaging · doi:10.1109/prni.2017.7981502 · Cited by 15 · see citing works
A registration method for improving quantitative assessment in probabilistic diffusion tractography
Waugh, J. L., Kuster, J. K., Makhlouf, M. L., Levenstein, J. M., Multhaupt‐Buell, T., Warfield, S. K., Sharma, N., & Blood, A. J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.12.057 · Cited by 15 · see citing works
DORIS: A diffusion MRI-based 10 tissue class deep learning segmentation algorithm tailored to improve anatomically-constrained tractography
Theaud, 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-citation
Combining white matter diffusion and geometry for tract-specific alignment and variability analysis
Benou, I., Veksler, R., Friedman, A., & Raviv, T. R. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.05.003 · Cited by 12 · see citing works
atTRACTive: Semi-automatic White Matter Tract Segmentation Using Active Learning
Peretzke, R., Maier-Hein, K. H., Bohn, J. R., Kirchhoff, Y., Roy, S., Oberli-Palma, S., Becker, D., Lenga, P., & Neher, P. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43993-3_23 · Cited by 10 · see citing works
Generalised coherent point drift for group-wise multi-dimensional analysis of diffusion brain MRI data
Ravikumar, N., Gooya, A., Beltrachini, L., Frangi, A. F., & Taylor, Z. A. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.001 · Cited by 10 · see citing works
FiberNeat: Unsupervised White Matter Tract Filtering
Chandio, B. Q., Chattopadhyay, T., Owens‐Walton, C., Reina, J. E. V., Nabulsi, L., Thomopoulos, S. I., Garyfallidis, E., & Thompson, P. M. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc48229.2022.9870877 · Cited by 10 · see citing works
Self-citation
Multi-Site Statistical Mapping of Along-Tract Microstructural Abnormalities in Bipolar Disorder with Diffusion MRI Tractometry
Nabulsi, L., Chandio, B. Q., McPhilemy, G., Martyn, F. M., Roberts, G. M. P., Hallahan, B. P., Dannlowski, U., Kircher, T. T. J., Haarman, B. C. M., Mitchell, P. B., et al. (2023) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1109/sipaim56729.2023.10373494 · Cited by 9 · see citing works
Supervised Classification of White Matter Fibers Based on Neighborhood Fiber Orientation Distributions Using an Ensemble of Neural Networks
Uğurlu, D., Fırat, Z., Türe, U., & Ünal, G. (2019) · Mathematics and visualization · doi:10.1007/978-3-030-05831-9_12 · Cited by 9 · see citing works
Nonparametric Bayes Models of Fiber Curves Connecting Brain Regions
Zhang, Z., Descoteaux, M., & Dunson, D. B. (2019) · Journal of the American Statistical Association · doi:10.1080/01621459.2019.1574582 · Cited by 9 · see citing works
Self-citation
Mapping along-tract commissural and association white matter microstructural differences in autistic children and young adults
Kim, G. S., Chandio, B. Q., Benavidez, S. M., Feng, Y., Thompson, P. M., & Lawrence, K. E. (2025) · Cerebral Cortex · doi:10.1093/cercor/bhaf291 · Cited by 8 · see citing works
Informative and Reliable Tract Segmentation for Preoperative Planning
Lucena, O., Borges, P., Cardoso, M. J., Ashkan, K., Sparks, R. E., & Sebastien Ourselin, A. (2022) · Frontiers in Radiology · doi:10.3389/fradi.2022.866974 · Cited by 8 · see citing works
White 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
Short fiber bundle filtering and test-retest reproducibility of the Superficial White Matter
Mendoza, C., Román, C., Mangin, J., Hernández, C., & Guevara, P. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1394681 · Cited by 7 · see citing works
BundleWarp: Enhancing white matter tractometry and morphometry with precise neuronal mapping using streamline-based nonlinear registration
Chandio, B. Q., Olivetti, E., Romero-Bascones, D., Thomopoulos, S. I., Villalón‐Reina, J. E., Nir, T. M., Harezlak, J., Thompson, P. M., & Garyfallidis, E. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104114 · Cited by 7 · see citing works
Self-citation
Neighborhood resolved fiber orientation distributions (NRFOD) in automatic labeling of white matter fiber pathways
Uğurlu, D., Fırat, Z., Türe, U., & Ünal, G. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.02.008 · Cited by 7 · see citing works
TractEM: Evaluation of protocols for deterministic tractography white matter atlas
Rheault, F., Bayrak, R. G., Wang, X., Schilling, K. G., Greer, J. M., Hansen, C. B., Kerley, C. I., Ramadass, K., Remedios, L. W., Blaber, J. A., et al. (2021) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2021.10.017 · Cited by 7 · see citing works
Groupwise track filtering via iterative message passing and pruning
Xia, Y., & Shi, Y. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117147 · Cited by 7 · see citing works
GeoLab: Geometry-Based Tractography Parcellation of Superficial White Matter
Vindas, N., Labra, N., Zhang, F., Xue, T., O’Donnell, L. J., & Mangin, J. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230336 · Cited by 6 · see citing works
Probabilistic normative maps of the corticospinal and cortico-reticulospinal tracts in streamline and volumetric formats
Boyne, P., Williamson, B. J., Buclez, J., Cramer, S. C., & Lin, D. J. (2025) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2025.110549 · Cited by 5 · see citing works
Deterministic versus Probabilistic Tractography: Impact on White Matter Bundle Shape
Shuai, Y., Chandio, B. Q., Feng, Y., Villalón‐Reina, J. E., Nir, T. M., Ching, C. R. K., Gari, I. B., Thomopoulos, S. I., Alibrando, J. D., John, J. P., et al. (2025) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1109/sipaim67325.2025.11283349 · Cited by 4 · see citing works
Fiber-Flux Diffusion Density for White Matter Tracts Analysis: Application to Mild Anomalies Localization in Contact Sports Players
Benou, I., Veksler, R., Friedman, A., & Raviv, T. R. (2018) · Mathematics and visualization · doi:10.1007/978-3-319-73839-0_15 · Cited by 3 · see citing works
How Does White Matter Registration Affect Tractography Alignment?
Amorosino, G., Olivetti, E., Jovicich, J., & Avesani, P. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230615 · Cited by 3 · see citing works
FetDTIAlign: A deep learning framework for affine and deformable registration of fetal brain dMRI
Li, B., Zeng, Q., Warfield, S. K., & Karimi, D. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121190 · Cited by 3 · see citing works
3D-SSF: A bio-inspired approach for dynamic multi-subject clustering of white matter tracts
Chekir, A., Hassas, S., Descoteaux, M., Côté, M.-A., Garyfallidis, E., & Oulebsir-Boumghar, F. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.01.016 · Cited by 3 · see citing works
Self-citation
Along-Tract Statistical Mapping of Microstructural Abnormalities in Bipolar Disorder: A Pilot Study
Nabulsi, L., Chandio, B. Q., Dhinagar, N. J., Laltoo, E., McPhilemy, G., Martyn, F. M., Hallahan, B. P., McDonald, C., Thompson, P. M., & Cannon, D. M. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc40787.2023.10339964 · Cited by 3 · see citing works
Nonlinear Alignment of Whole Tractograms with the Linear Assignment Problem
Olivetti, E., Gori, P., Astolfi, P., Bertò, G., & Avesani, P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50120-4_1 · Cited by 2 · see citing works
Alignment of Tractography Streamlines Using Deformation Transfer via Parallel Transport
Lizarraga, A., Lee, D., Kubicki, A., Sahib, A. K., Nunez, E., Narr, K. L., & Joshi, S. H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87615-9_9 · Cited by 2 · see citing works
MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal
Newlin, N. R., Kim, M. E., Kanakaraj, P., Yao, T., Hohman, T. J., Pechman, K. R., Beason‐Held, L. L., Resnick, S. M., Archer, D. B., Jefferson, A. L., et al. (2023) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2024.03.033 · Cited by 2 · see citing works
Deep diffusion MRI template (DDTemplate): A novel deep learning groupwise diffusion MRI registration method for brain template creation
Wang, J., Zhu, X., Zhang, W., Du, M., Wells, W. M., O’Donnell, L. J., & Zhang, F. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121401 · Cited by 2 · see citing works
Automatic Atlas-Based Segmentation of Brain White Matter in Neonates at Risk for Neurodevelopmental Disorders
Fonseca, L., van Pul, C., Lori, N. F., van den Boom, R., Andriessen, P., Buijs, J., & Vilanova, A. (2017) · Mathematics and visualization · doi:10.1007/978-3-319-61358-1_15 · Cited by 2 · see citing works
Cortically constrained shape recognition: Automated white matter tract segmentation validated in the pediatric brain
Jordan, K., Lauricella, M., Licata, A. E., Sacco, S., Asteggiano, C., Wang, C., Sudarsan, S. P., Watson, C., Scheffler, A. W., Battistella, G., et al. (2021) · Journal of Neuroimaging · doi:10.1111/jon.12854 · Cited by 2 · see citing works
White matter differences in Parkinson’s disease mapped using tractometryPreprint
Corbin, C. K., Gupta, V., Villalon-Reina, J. E., Nir, T. M., Rashid, F. M., Thomopoulos, S. I., Jahanshad, N., & Thompson, P. M. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/209858 · Cited by 2 · see citing works
Inference of a U-fiber bundle atlas informed by the variability of the cortical folding pattern
Labra, N. (2020) · theses.fr (ABES) · doi:10.70675/42b36629z423cz4f6az9807zf916bfc2242a · Cited by 2 · see citing works
Methods and statistics for diffusion MRI tractometry
Chamberland, M., St‐Jean, S., Jones, D. K., Descoteaux, M., & Leemans, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00023-9 · Cited by 1 · see citing works
Self-citation
Mapping the impact of age and APOE risk factors for late onset Alzheimer’s disease on long range brain connections through multiscale bundle analysis
Stout, J. A., Anderson, R. J., Mahzarnia, A., Han, Z. Y., Beck, K., Browndyke, J. N., Johnson, K. G., O’Brien, R., & Badea, A. (2025) · Brain Structure and Function · doi:10.1007/s00429-025-02905-9 · Cited by 1 · see citing works
FODSeg: a deep learning framework for tract-specific white matter segmentation from full angular distributions
Joshi, A., Li, H., Parikh, N. A., & He, L. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1734498 · Cited by 1 · see citing works
Superficial white matter shape characterization using hierarchical clustering and a multi-subject bundle atlas
Mendoza, C., Román, C., Molina, J., Poupon, C., Mangin, J., Hernández, C., & Guevara, P. (2023) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1117/12.2669738 · Cited by 1 · see citing works
Machine learning in tractography
Neher, P., Poulin, P., Jörgens, D., Reisert, M., Benou, I., & Maier-Hein, K. H. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00032-x · Cited by 1 · see citing works
White Matter Microstructural Alterations in Autism Localized with Bundle Analytics
Kim, G. S., Chandio, B. Q., Benavidez, S. M., Thompson, P. M., & Lawrence, K. E. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10980852 · Cited by 1 · see citing works
HFPRM: Hierarchical Functional Principal Regression Model for Diffusion Tensor Image Bundle Statistics
Zhang, J., Huang, C., Ibrahim, J. G., Jha, S. C., Knickmeyer, R. C., Gilmore, J. H., Styner, M. A., & Hongtu Zhu, B. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59050-9_38 · Cited by 1 · see citing works
Fast Non-rigid Whole Brain Streamline Matching based on Fast Fiber k-NN and Group-wise MDSPreprint
Wang, J. (2022) · Research Square · doi:10.21203/rs.3.rs-1505524/v1 · Cited by 1 · see citing works
Atlasing white matter pathways using diffusion magnetic resonance imaging (dMRI) : With a focus on human association tracts in the external and extreme capsules
Hau, J. (2015) · theses.fr (ABES) · doi:10.70675/bd61b9baz3da8z4bb7za2b5z67fae5e84f0c · Cited by 1 · see citing works
A Measure of Whole-Brain White Matter TopographyPreprint
Wang, J., & Shi, Y. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/783274
Is DL-based Reconstruction for Fiber Orientation Distribution (FOD) Comparable to MSMT-Based FOD for Fiber Bundle Analysis?
Vidal, N., da Silva, M. O., do Carmo, D. S., Navarrete, S., Guevara, P., & Rittner, L. (2024) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1109/sipaim62974.2024.10783621
Dissecting 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
Self-citation
Significance of the corpus callosum and inferior fronto-occipital fasciculus in recovery after traumatic brain injury
Pogosbekian, E. L., Sharova, E. V., Fadeeva, L. M., Alexandrova, E. V., Chekhonin, I. V., Zakharova, N. E., & Pronin, I. (2025) · Neuroradiology · doi:10.1007/s00234-025-03577-2
Detecting neurodegenerative changes in glaucoma using deep mean kurtosis-curve–corrected tractometryPreprint
Kasa, L. W., Schierding, W. S., Kwon, E. E., Holdsworth, S. J., & Danesh-Meyer, H. V. (2025) · medRxiv · doi:10.1101/2025.06.05.25329075
A Novel Streamline-Based Diffusion MRI Tractography Registration Method with Probabilistic Keypoint Detection
Wang, J., Du, M., Wu, Y., Li, Y., Wells, W. M., O’Donnell, L. J., & Zhang, F. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05162-2_3
Along-Tract Mapping of White Matter Microstructural Abnormalities in Bipolar Disorder using Diffusion MRI
Nabulsi, L., Chandio, B. Q., Kang, M. J. Y., Im, Y., McPhilemy, G., Martyn, F. M., Roberts, G. M. P., Hallahan, B. P., Dannlowski, U., Borgers, T., et al. (2025) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1109/sipaim67325.2025.11283419
Registration-Based Analysis of the Three-Dimensional Shape of the Retinal Nerve Fiber Layer for Detection of Glaucomatous Defects
Park, J. S., King, B. J., Garyfallidis, E., & Swanson, W. H. (2026) · Translational Vision Science & Technology · doi:10.1167/tvst.15.3.8
Self-citation
Approche multimodale de connectivité fonctionnelle et structurelle pour l’analyse du déclin cognitif au cours du vieillissement : étude au sein de la cohorte des 3Cités
Edde, M. (2019) · theses.fr (ABES) · doi:10.70675/bd1d4234zafc8z4245z8b87zaa16ee723166
Methodological Considerations and Clinical Relevance of Diffusion Tensor Imaging in Acute Stroke Prognosis
Moulton, E. J. (2019) · theses.fr (ABES) · doi:10.70675/18d80f29zd496z442dz89c6z78820fe340bf
Connectome-based spatial statistics enabling large-scale population analyses of human connectome across cohortsPreprint
Li, T., Wang, X., Cole, M., Sun, Z., Jiang, Z., Qian, X., Gao, S., Luo, T., Descoteaux, M., Stein, J. L., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.09.717492
Self-citation
Assessing the Influence of Tractography Methods on Detected White Matter Microstructure in Alzheimers Disease
Shuai, Y., Feng, Y., Villalón‐Reina, J. E., Nir, T. M., Thomopoulos, S. I., Thompson, P. M., & Chandio, B. Q. (2026) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi61048.2026.11515867
Along-tract statistical mapping of white matter abnormalities in children with SeLECTS
Xu, G., Zhang, Y., 黄可欣, Liu, J., Song, G., Chen, X., Zeng, L., Zhang, Y., Liu, H., & Zhang, T. (2026) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2026.112175
A computational framework for robust white matter tract segmentation via multi-directional fiber orientation representations
Hussain, S. S., Sachdeva, J., & Ahuja, C. K. (2026) · Journal of Computational Science · doi:10.1016/j.jocs.2026.103065

Bundles of interest based registration of White Matter tractographies
Mayer, A., & Greenspan, H. K. (2008) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2008.4541147 · Cited by 7 · see citing works
Recognition of white matter bundles using local and global streamline-based registration and clustering
Garyfallidis, E., Côté, M.-A., Rheault, F., Sidhu, J., Hau, J., Petit, L., Fortin, D., Cunanne, S., & Descoteaux, M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.015 · Cited by 279 · see citing works
Self-citation
BundleWarp: Enhancing white matter tractometry and morphometry with precise neuronal mapping using streamline-based nonlinear registration
Chandio, B. Q., Olivetti, E., Romero-Bascones, D., Thomopoulos, S. I., Villalón‐Reina, J. E., Nir, T. M., Harezlak, J., Thompson, P. M., & Garyfallidis, E. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104114 · Cited by 7 · see citing works
Self-citation
Fiber-to-bundle registration of white matter tracts
Xu, Q., Anderson, A. W., Gore, J. C., & Ding, Z. (2009) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.811620
How Does White Matter Registration Affect Tractography Alignment?
Amorosino, G., Olivetti, E., Jovicich, J., & Avesani, P. (2023) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi53787.2023.10230615 · Cited by 3 · see citing works

Authors: Garyfallidis, E., Ocegueda, O., Wassermann, D., & Descoteaux, M.
Affiliations: Université de Sherbrooke (CA)
Mathematics Research Center (MX)
Corresponding author: Garyfallidis, Eleftherios
ORCID: Garyfallidis, Eleftherios
Descoteaux, Maxime
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2015-08
Volume: 117
Pages: 124-140
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2015.05.016
OpenAlex: W241994552
Cited by: 84
References: 91
Citations by year: 2026: 6 · 2025: 15 · 2024: 2 · 2023: 11 · 2022: 6 · 2021: 6 · 2020: 6 · 2019: 13 · 2018: 3 · 2017: 10 · 2016: 5 · 2015: 1
FWCI: 5.13
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: NeuroImage (publishedVersion)
PubMed (publishedVersion)
HAL (submittedVersion)
Funders: Max-Planck-Institut für Kognitions- und Neurowissenschaften
Natural Sciences and Engineering Research Council of Canada
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords: linear registration, diffusion MRI tractography, optic radiation, sex differences, laterality, arcuate fasciculus, corticospinal tract
MeSH terms: Algorithms, Brain, Humans, Image Processing, Computer-Assisted, Nerve Fibers, Neural Pathways, Diffusion Magnetic Resonance Imaging, White 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

Robust and efficient linear registration of white-matter fascicles in the space of streamlines

Abstract

Integrity signals

Claims the paper makes

Claims other papers make about this one

Cites

Cited by

Related

Record

About these checks