Toward global tractography

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

Regularization 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

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 1,376

Functional connectomics from resting-state fMRI

Smith, S. M., Vidaurre, D., Beckmann, C. F., Glasser, M. F., Jenkinson, M., Miller, K. L., Nichols, T. E., Robinson, E. C., Salimi-Khorshidi, G., Woolrich, M. W., et al. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.016 · Cited by 952

Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited

Thomas, C., Ye, F. Q., Irfanoglu, M. O., Modi, P., Saleem, K. S., Leopold, D. A., & Pierpaoli, C. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1405672111 · Cited by 725

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

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

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

Evaluation and statistical inference for human connectomes

Pestilli, F., Yeatman, J. D., Rokem, A., Kay, K. N., & Wandell, B. A. (2014) · Nature Methods · doi:10.1038/nmeth.3098 · Cited by 259

COMMIT: Convex Optimization Modeling for Microstructure Informed Tractography

Daducci, A., Dal Palu, A., Lemkaddem, A., & Thiran, J.-P. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2352414 · Cited by 255

White matter tractography for neurosurgical planning: A topography-based review of the current state of the art

Essayed, W. I., Zhang, F., Unadkat, P., Cosgrove, G. R., Golby, A. J., & O’Donnell, L. J. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.06.011 · Cited by 249

Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography?

Sarwar, T., Ramamohanarao, K., & Zalesky, A. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27471 · Cited by 190

The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery

Pujol, S., Wells, W., Pierpaoli, C., Brun, C., Gee, J., Cheng, G., Vemuri, B., Commowick, O., Prima, S., Stamm, A., et al. (2015) · Journal of Neuroimaging · doi:10.1111/jon.12283 · Cited by 171

Global tractography of multi-shell diffusion-weighted imaging data using a multi-tissue model

Christiaens, D., Reisert, M., Dhollander, T., Sunaert, S., Suetens, P., & Maes, F. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.08.008 · Cited by 152

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

Connectome imaging for mapping human brain pathways

Shi, Y., & Toga, A. W. (2017) · Molecular Psychiatry · doi:10.1038/mp.2017.92 · Cited by 135

Clarifying Human White Matter

Wandell, B. A. (2016) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-070815-013815 · Cited by 117

Ensemble Tractography

Takemura, H., Caiafa, C. F., Wandell, B. A., & Pestilli, F. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004692 · Cited by 114

Does diffusion MRI tell us anything about the white matter? An overview of methods and pitfalls

O’Donnell, L. J., & Pasternak, O. (2014) · Schizophrenia Research · doi:10.1016/j.schres.2014.09.007 · Cited by 94

Tractostorm: The what, why, and how of tractography dissection reproducibility

Rheault, F., BENEDICTIS, A. D., Daducci, A., Maffei, C., Tax, C. M. W., Romascano, D., Caverzasi, E., Morency, F. C., Corrivetti, F., Pestilli, F., et al. (2020) · Human Brain Mapping · doi:10.1002/hbm.24917 · Cited by 95

Quantitative streamlines tractography: methods and inter-subject normalisation

Smith, R. E., Raffelt, D. A., Tournier, J., & Connelly, A. (2022) · Aperture Neuro · doi:10.52294/apertureneuro.2022.2.neod9565 · Cited by 91

Bundle-specific tractography with incorporated anatomical and orientational priors

Rheault, F., St‐Onge, E., Sidhu, J., Maier-Hein, K. H., Tzourio‐Mazoyer, N., Petit, L., & Descoteaux, M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.11.018 · Cited by 92

Tractography and machine learning: current state and open challenges

Poulin, P., Jörgens, D., Jodoin, P.-M., & Descoteaux, M. (2019) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2019.04.013 · Cited by 84

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 92

Dynamic Multi-Coil Technique (DYNAMITE) Shimming for Echo-Planar Imaging of the Human Brain at 7 Tesla

Juchem, C., Umesh Rudrapatna, S., Nixon, T. W., & de Graaf, R. A. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.11.011 · Cited by 74

When tractography meets tracer injections: a systematic study of trends and variation sources of diffusion-based connectivity

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

Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics

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 69

Fiber tractography using machine learning

Neher, P. F., Côté, M.-A., Houde, J.-C., Descoteaux, M., & Maier-Hein, K. H. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.028 · Cited by 68

Longitudinal decline in structural networks predicts dementia in cerebral small vessel disease

Lawrence, A. J., Zeestraten, E., Benjamin, P., Lambert, C., Morris, R. G., Barrick, T. R., & Markus, H. S. (2018) · Neurology · doi:10.1212/wnl.0000000000005551 · Cited by 60

Merits and Limits of Tractography Techniques for the Uninitiated

Nimsky, C., Bopp, M. H. A., & Carl, B. (2015) · Advances and technical standards in neurosurgery · doi:10.1007/978-3-319-21359-0_2 · Cited by 56

The tract terminations in the temporal lobe: Their location and associated functions

Bajada, C. J., Haroon, H. A., Azadbakht, H., Parker, G. J. M., Ralph, M. A. L., & Cloutman, L. L. (2016) · Cortex · doi:10.1016/j.cortex.2016.03.013 · Cited by 58

Human connectomics — What will the future demand?

Johansen‐Berg, H. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.082 · Cited by 53

TractoInferno - A large-scale, open-source, multi-site database for machine learning dMRI tractography

Poulin, P., Theaud, G., Rheault, F., St‐Onge, E., Boré, A., Renauld, E., Beaumont, L. D., Guay, S., Jodoin, P., & Descoteaux, M. (2022) · Scientific Data · doi:10.1038/s41597-022-01833-1 · Cited by 49

Track-to-Learn: A general framework for tractography with deep reinforcement learning

Théberge, A., Desrosiers, C., Descoteaux, M., & Jodoin, P. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102093 · Cited by 44

Multidimensional encoding of brain connectomes

Caiafa, C. F., & Pestilli, F. (2017) · Scientific Reports · doi:10.1038/s41598-017-09250-w · Cited by 46

The Emerging Role of Tractography in Deep Brain Stimulation: Basic Principles and Current Applications

Rodrigues, N. B., Mithani, K., Meng, Y., Lipsman, N., & Hamani, C. (2018) · Brain Sciences · doi:10.3390/brainsci8020023 · Cited by 40

A new compression format for fiber tracking datasets

Presseau, C., Jodoin, P., Houde, J., & Descoteaux, M. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.058 · Cited by 36

Preserved white matter microstructure in young patients with anorexia nervosa?

Pfuhl, G., King, J. A., Geisler, D., Roschinski, B., Ritschel, F., Seidel, M., Bernardoni, F., Müller, D. K., White, T. J. H., Roessner, V., et al. (2016) · Human Brain Mapping · doi:10.1002/hbm.23296 · Cited by 33

Diffusion MRI: Assessment of the Impact of Acquisition and Preprocessing Methods Using the BrainVISA-Diffuse Toolbox

Brun, L., Pron, A., Sein, J., Deruelle, C., & Coulon, O. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00536 · Cited by 31

Connectivity of the Superficial Muscles of the Human Perineum: A Diffusion Tensor Imaging-Based Global Tractography Study

Zifan, A., Reisert, M., Sinha, S., Ledgerwood-Lee, M. M., Cory, E., Sah, R. L., & Mittal, R. K. (2018) · Scientific Reports · doi:10.1038/s41598-018-36099-4 · Cited by 27

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

Probing the reproducibility of quantitative estimates of structural connectivity derived from global tractography

Schumacher, L. V., Reisert, M., Nitschke, K., Egger, K. E., Urbach, H., Hennig, J., Weiller, C., & Kaller, C. P. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.086 · Cited by 25

Shape analysis of the cingulum, uncinate and arcuate fasciculi in patients with bipolar disorder

Sun, Z. Y., Houenou, J., Duclap, D., Sarrazin, S., Linke, J. O., Daban, C., Hamdani, N., d’Albis, M.-A., Corvoisier, P. L., Guevara, P., et al. (2016) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.150291 · Cited by 24

Diffusion MRI with machine learning

Karimi, D., & Warfield, S. K. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00353 · Cited by 23

Towards deep learning for connectome mapping: A block decomposition framework

Sarwar, T., Seguin, C., Ramamohanarao, K., & Zalesky, A. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116654 · Cited by 22

Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference

Gutierrez, C. E., Skibbe, H., Nakae, K., Tsukada, H., Liénard, J., Watakabe, A., Hata, J., Reisert, M., Woodward, A., Yamaguchi, Y., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-78284-4 · Cited by 21

Repeatability Analysis of Global and Local Metrics of Brain Structural Networks

Andreotti, J., Jann, K., Melie‐García, L., Giezendanner, S., Dierks, T., & Federspiel, A. (2014) · Brain Connectivity · doi:10.1089/brain.2013.0202 · Cited by 21

Predicting Planning Performance from Structural Connectivity Between Left and Right Mid-Dorsolateral Prefrontal Cortex: Moderating Effects of Age During Postadolescence and Midadulthood

Kaller, C. P., Reisert, M., Katzev, M., Umarova, R. M., Mader, I., Hennig, J., Weiller, C., & Köstering, L. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht276 · Cited by 20

Modeling topographic regularity in structural brain connectivity with application to tractogram filtering

Wang, J., Aydogan, D. B., Varma, R., Toga, A. W., & Shi, Y. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.07.068 · Cited by 20

Tractography of Association Fibers Associated with Language Processing

Egger, K. E., Yang, S., Reisert, M., Kaller, C. P., Mader, I., Beume, L.-A., Weiller, C., & Urbach, H. (2015) · Clinical Neuroradiology · doi:10.1007/s00062-015-0447-2 · Cited by 18

CTtrack: A CNN+Transformer-based framework for fiber orientation estimation & tractography

Hosseini, S. M., Hassanpour, M., Masoudnia, S., Iraji, S., Raminfard, S., & Nazem-Zadeh, M.-R. (2022) · Neuroscience Informatics · doi:10.1016/j.neuri.2022.100099 · Cited by 19

A Novel Anatomically-Constrained Global Tractography Approach to Monitor Sharp Turns in Gyri

Teillac, A., Beaujoin, J., Poupon, F., Mangin, J., & Poupon, C. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66182-7_61 · Cited by 17

Awakening Neuropsychiatric Research Into the Stria Medullaris: Development of a Diffusion-Weighted Imaging Tractography Protocol of This Key Limbic Structure

Roddy, D. W., Roman, E., Rooney, S., Andrews, S., Farrell, C., Doolin, K., Levins, K. J., Tozzi, L., Tierney, P., Barry, D., et al. (2018) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2018.00039 · Cited by 16

A critical review of connectome validation studies

Sarwar, T., Ramamohanarao, K., & Zalesky, A. (2021) · NMR in Biomedicine · doi:10.1002/nbm.4605 · Cited by 20

Mapping Brain Anatomical Connectivity Using Diffusion Magnetic Resonance Imaging: Structural connectivity of the human brain

Li, J., Shi, Y., & Toga, A. W. (2016) · IEEE Signal Processing Magazine · doi:10.1109/msp.2015.2510024 · Cited by 16

Probabilistic Tractography for Topographically Organized Connectomes

Aydogan, D. B., & Shi, Y. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46720-7_24 · Cited by 16

The human brain project: neuroscience perspectives and German contributions

Amunts, K., Lindner, A., & Zilles, K. (2014) · e-Neuroforum · doi:10.1007/s13295-014-0058-4 · Cited by 15

A novel male Japanese quail structural connectivity atlas using ultra-high field diffusion MRI at 11.7 T

Hot, R. Y., Siwiaszczyk, M., Love, S., Andersson, F., Calandreau, L., Poupon, F., Beaujoin, J., Herlin, B., Boumezbeur, F., Mulot, B., et al. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02457-2 · Cited by 14

Modular subgraphs in large-scale connectomes underpin spontaneous co-fluctuation events in mouse and human brains

Ragone, E., Tanner, J., Jo, Y., Esfahlani, F. Z., Faskowitz, J., Pope, M., Coletta, L., Gozzi, A., & Betzel, R. F. (2024) · Communications Biology · doi:10.1038/s42003-024-05766-w · Cited by 16

Modelfree global tractography

Konopleva, L. V., Il'yasov, K. A., Skibbe, H., Kiselev, V. G., Kellner, E., Dhital, B., & Reisert, M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.03.058 · Cited by 15

An integrated TMS-EEG and MRI approach to explore the interregional connectivity of the default mode network

Esposito, R., Bortoletto, M., Zacà, D., Avesani, P., & Miniussi, C. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02453-6 · Cited by 13

Track Filtering via Iterative Correction of TDI Topology

Aydogan, D. B., & Shi, Y. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24553-9_3 · Cited by 12

The frequent complete subgraphs in the human connectome

Fellner, M., Varga, B., & Grolmusz, V. I. (2020) · PLoS ONE · doi:10.1371/journal.pone.0236883 · Cited by 10

DTI Analysis Methods: Fibre Tracking and Connectivity

Caan, M. W. A. (2015) · Springer eBooks · doi:10.1007/978-1-4939-3118-7_11 · Cited by 13

Deep learning based tractography with TractSeg in patients with hemispherotomy: Evaluation and refinement

Gruen, J., Bauer, T., Rüber, T., & Schultz, T. (2025) · NeuroImage Clinical · doi:10.1016/j.nicl.2025.103738 · Cited by 9

Bundle-o-graphy: improving structural connectivity estimation with adaptive microstructure-informed tractography

Battocchio, M., Schiavi, S., Descoteaux, M., & Daducci, A. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119600 · Cited by 9

A unified global tractography framework for automatic visual pathway reconstruction

He, J., Yao, S., Zeng, Q., Chen, J., Sang, T., Xie, L., Pan, Y., & Feng, Y. (2023) · NMR in Biomedicine · doi:10.1002/nbm.4904 · Cited by 7

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

Atlas-Guided Global Tractography: Imposing a Prior on the Local Track Orientation

Christiaens, D., Reisert, M., Dhollander, T., Maes, F., Sunaert, S. G., & Suetens, P. (2014) · Mathematics and visualization · doi:10.1007/978-3-319-11182-7_11 · Cited by 7

Anisotropy Across Fields and Scales

Özarslan, E., Schultz, T., Zhang, E., & Fuster, A. (2021) · Mathematics and visualization · doi:10.1007/978-3-030-56215-1 · Cited by 6

Diffusion MRI Fiber Tractography

Smith, R. E., Connelly, A., & Calamante, F. (2020) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-12-817057-1.00023-8 · Cited by 10

Challenges for Tractogram Filtering

Jörgens, D., Descoteaux, M., & Moreno, R. (2021) · Mathematics and visualization · doi:10.1007/978-3-030-56215-1_7 · Cited by 9

Cellular Automata Tractography: Fast Geodesic Diffusion MR Tractography and Connectivity Based Segmentation on the GPU

Hamamcı, A. (2019) · Neuroinformatics · doi:10.1007/s12021-019-09425-y · Cited by 5

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

The Frequent Complete Subgraphs in the Human Connectome

Fellner, M., Varga, B., & Grolmusz, V. I. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20518-8_75 · Cited by 4

Multifiber pathway reconstruction using bundle constrained streamline

Chu, C., Huang, J., Sun, C.-Y., Zhang, Y.-L., Liu, W., & Zhu, Y. (2015) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2015.07.010 · Cited by 4

Embarrassingly Parallel Acceleration of Global Tractography via Dynamic Domain Partitioning

Wu, H., Chen, G., Jin, Y., Shen, D. G., & Yap, P. (2016) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2016.00025 · Cited by 4

Exploring the robustness of TractOracle methods in RL-based tractography

Levesque, J., Théberge, A., Descoteaux, M., & Jodoin, P. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103743 · Cited by 3

Streamline tractography of the fetal brain in utero with machine learning

Liu, W., Calixto, C. A., Warfield, S. K., & Karimi, D. (2025) · Imaging Neuroscience · doi:10.1162/imag_a_00537 · Cited by 3

Imaging White Matter Anatomy for Brain Tumor Surgery

Pujol, S. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-800870-6.00004-2 · Cited by 3

Diffusion MRI for Brain Connectivity Mapping and Analysis

Booth, B. G., & Hamarneh, B. G. G. (2018) · doi:10.1201/b19353-8 · Cited by 3

TractoInferno : A large-scale, open-source, multi-site database for machine learning dMRI tractographyPreprint

Poulin, P., Theaud, G., Rheault, F., St‐Onge, E., Boré, A., Renauld, E., Beaumont, L. D., Guay, S., Jodoin, P., & Descoteaux, M. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.29.470422 · Cited by 3

Anatomy-to-tract mapping infers white matter pathways without diffusion streamline propagation

Tan, Y. F., Huynh, K. M., Liu, S., Phan, R. C. ‐., Ting, C., & Yap, P. (2025) · Nature Communications · doi:10.1038/s41467-025-66615-w · Cited by 2

Bundle-specific tractogram distribution estimation using higher-order streamline differential equation

Feng, Y., Xie, L., Wang, J., Tian, Q., He, J., Zeng, Q., & Gao, F. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120766 · Cited by 2

Deterministic fiber tractography

Leemans, A., Dell’Acqua, F., & Descoteaux, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00029-x · Cited by 2

Formulation and Efficient Computation of ${\ell}_{\textsf{1}}$ - and Smoothness Penalized Estimates for Microstructure-Informed Tractography

Schomburg, H., & Hohage, T. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2902787 · Cited by 2

Robust intra-individual estimation of structural connectivity by Principal Component Analysis

Konopleva, L. V., Il'yasov, K. A., Teo, S. J., Coenen, V. A., Kaller, C. P., & Reisert, M. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117483 · Cited by 2

Connectivity calculation on diffusion phantom using shortest paths method

Hamamcı, A. (2015) · Medical Technologies National Conference · doi:10.1109/tiptekno.2015.7374611 · Cited by 1

White matter abnormalities in bipolar II and unipolar depression – Evidence from fixel-based analysis

Chou, I. W. Y., Manelis, A., Swartz, H. A., Leung, O., Phillips, M. L., So, S. F., Chu, C. W., Lu, H., Lam, L. C. W., & Mak, A. D. P. (2026) · Journal of Affective Disorders · doi:10.1016/j.jad.2026.121957 · Cited by 1

Global diffusion tractography by simulated annealing

Robini, M. C., Ozon, M., Frindel, C., Yang, F., & Zhu, Y. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2570900 · Cited by 1

Comparison of T1‐weighted landmark placement and ROI transfer onto diffusion‐weighted EPI sequences for targeted tractography tasks in the optic nerve

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