Tractometer: Towards validation of tractography pipelines

Medical image analysis: progress over two decades and the challenges ahead

Duncan, J. S., & Ayache, N. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.824822 · Cited by 4,167

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

White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI

Jones, D. K., Knösche, T. R., & Turner, R. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.081 · Cited by 2,402 · cited 1×

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

Q-ball imaging

Tuch, D. S. (2004) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20279 · Cited by 1,880

Medical Image Computing and Computer-Assisted Intervention — MICCAI’98

Roche, A., Malandain, G., Pennec, X., & Ayache, N. (1998) · Lecture notes in computer science · doi:10.1007/bfb0056181 · Cited by 1,578

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

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

Log-Euclidean metrics for fast and simple calculus on diffusion tensors

Arsigny, V., Fillard, P., Pennec, X., & Ayache, N. (2006) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20965 · Cited by 1,113

Regularized, fast, and robust analytical Q-ball imaging

Descoteaux, M., Angelino, E., Fitzgibbons, S., & Deriche, R. (2007) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21277 · Cited by 850

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

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

Fiber up‐sampling and quality assessment of tractograms – towards quantitative brain connectivity

Sommer, S., Kozerke, S., Seifritz, E., & Stämpfli, P. (2016) · Brain and Behavior · doi:10.1002/brb3.588 · Cited by 16

Reinforcement Tractography: A Hybrid Approach for Robust Segmentation of Complex Fiber Bundles

Cabeen, R. P., & Toga, A. W. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098371 · Cited by 14

Optimal DSI reconstruction parameter recommendations: Better ODFs and better connectivity

Paquette, M., Gilbert, G., & Descoteaux, M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.07.048 · Cited by 13

Cortical Surfaces Integration with Tractography for Structural Connectivity Analysis

St‐Onge, E., Al‐Sharif, N., Girard, G., Theaud, G., & Descoteaux, M. (2021) · Brain Connectivity · doi:10.1089/brain.2020.0930 · Cited by 15

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

Fast Streamline Search: An Exact Technique for Diffusion MRI Tractography

St‐Onge, E., Garyfallidis, E., & Collins, D. L. (2022) · Neuroinformatics · doi:10.1007/s12021-022-09590-7 · Cited by 13

A survey of current trends in diffusion MRI for structural brain connectivity

Ghosh, A., & Rachid Deriche, B. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/13/1/011001 · Cited by 13

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 15

Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI : Part 1: In vivo small‐animal imaging

Jelescu, I., Grussu, F., Ianuș, A., Hansen, B., Barrett, R. L. C., Aggarwal, M., Michielse, S., Nasrallah, F. A., Syeda, W. T., Wang, N., et al. (2025) · Magnetic Resonance in Medicine · doi:10.1002/mrm.30429 · Cited by 21

Age-Related Variations in Regional White Matter Volumetry and Microstructure During the Post-adolescence Period: A Cross-Sectional Study of a Cohort of 1,713 University Students

Tsuchida, A., Laurent, A., Crivello, F., Petit, L., Pepe, A., Beguedou, N., Debette, S., Tzourio, C., & Mazoyer, B. M. (2021) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2021.692152 · Cited by 15

Imaging the structural connectome with hybrid MRI-microscopy tractography

Zhu, S., Huszár, I. N., Cottaar, M., Daubney, G., Eichert, N., Hanayik, T., Khrapitchev, A. A., Mars, R. B., Mollink, J., Sallet, J., et al. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103498 · Cited by 11

D-BRAIN: Anatomically Accurate Simulated Diffusion MRI Brain Data

Perrone, D., Jeurissen, B., Aelterman, J., Roine, T., Sijbers, J., Pižurica, A., Leemans, A., & Philips, W. R. (2016) · PLoS ONE · doi:10.1371/journal.pone.0149778 · Cited by 11

Diffusion Capillary Phantom vs. Human Data: Outcomes for Reconstruction Methods Depend on Evaluation Medium

Lichenstein, S. D., Bishop, J. H., Verstynen, T., & Yeh, F.-C. F. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00407 · Cited by 11

Clustering in Tractography Using Autoencoders (CINTA)

Legarreta, J. H., Petit, L., Jodoin, P., & Descoteaux, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21206-2_11 · Cited by 11

A sensorimotor-association axis of thalamocortical connection developmentPreprint

Sydnor, V., Bagautdinova, J., Larsen, B., Arcaro, M., Barch, D., Bassett, D. S., Alexander-Bloch, A., Cook, P. A., Covitz, S., Franco, A., et al. (2024) · bioRxiv · doi:10.1101/2024.06.13.598749 · Cited by 9

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

Learning a Single Step of Streamline Tractography Based on Neural Networks

Jörgens, D., Smedby, Ö., & Rodrigo Moreno, A. (2018) · Mathematics and visualization · doi:10.1007/978-3-319-73839-0_8 · Cited by 10

In vivo structural connectivity of the reward system along the hippocampal long axis

Elliott, B. L., Mohyee, R. A., Ballard, I. C., Olson, I. R., Ellman, L. M., & Murty, V. P. (2024) · Hippocampus · doi:10.1002/hipo.23608 · Cited by 9

Assessment of the Depiction of Superficial White Matter Using Ultra‐High‐Resolution Diffusion MRI

Zhang, F., Chen, Y., Ning, L., Rushmore, J., Liu, Q., Du, M., Hassanzadeh‐Behbahani, S., Legarreta, J. H., Yeterian, E. H., Makris, N., et al. (2024) · Human Brain Mapping · doi:10.1002/hbm.70041 · Cited by 9

Using 3D-SHORE and MAP-MRI to obtain both tractography and microstructural constrast from a clinical DMRI acquisition

Fick, R., Zucchelli, M., Girard, G., Descoteaux, M., Menegaz, G., & Rachid Deriche, B. (2015) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7163905 · 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

CHIASM, the human brain albinism and achiasma MRI dataset

Puźniak, R. J., McPherson, B., Ahmadi, K., Herbik, A., Kaufmann, J., Liebe, T., Gouws, A. D., Morland, A. B., Gottlob, I., Hoffmann, M. B., et al. (2021) · Scientific Data · doi:10.1038/s41597-021-01080-w · Cited by 8

Assessment of White Matter Transgression During Neuroendoscopic Procedures Using Diffusion Tensor Image Fiber Tracking

García-García, S., Rincón-Torroella, J., Benet, A., Oleaga, L., & Sánchez, J. J. G. (2016) · World Neurosurgery · doi:10.1016/j.wneu.2016.11.112 · Cited by 8

Fibre orientation atlas guided rapid segmentation of white matter tracts

Young, F., Aquilina, K., Seunarine, K. K., Mancini, L., Clark, C. A., & Clayden, J. D. (2024) · Human Brain Mapping · doi:10.1002/hbm.26578 · Cited by 7

Diffantom: Whole-Brain Diffusion MRI Phantoms Derived from Real Datasets of the Human Connectome Project

Estéban, O., Caruyer, E., Daducci, A., Cuadra, M. B., Ledesma‐Carbayo, M. J., & Santos, A. (2016) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2016.00004 · Cited by 8

Diffusion Kurtosis Imaging Fiber Tractography of Major White Matter Tracts in Neurosurgery

Bopp, M. H. A., Emde, J., Carl, B., Nimsky, C., & Saß, B. (2021) · Brain Sciences · doi:10.3390/brainsci11030381 · Cited by 8

TractEM: Fast Protocols for Whole Brain Deterministic Tractography-Based White Matter AtlasPreprint

Bayrak, R. G., Wang, X., Schilling, K. G., Greer, J. M., Hansen, C. B., Blaber, J. A., Williams, O. A., Beason‐Held, L. L., Resnick, S. M., Rogers, B. P., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/651935 · Cited by 8

Supervised tractogram filtering using Geometric Deep Learning

Astolfi, P., Verhagen, R., Petit, L., Olivetti, E., Sarubbo, S., Masci, J., Boscaini, D., & Avesani, P. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102893 · Cited by 9

Clinical connectomics: Mapping structural brain networks in disease

Zalesky, A., & Biase, M. A. D. (2026) · Neuron · doi:10.1016/j.neuron.2026.02.007 · Cited by 7

Disruption of white matter connectivity in chronic obstructive pulmonary disease

Spilling, C. A., Jones, P., Dodd, J. W., & Barrick, T. R. (2019) · PLoS ONE · doi:10.1371/journal.pone.0223297 · Cited by 7

Entrack: A Data-Driven Maximum-Entropy Approach to Fiber Tractography

Wegmayr, V., Giuliari, G., & Buhmann, J. M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33676-9_16 · Cited by 6

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

Optimal shrinkage denoising breaks the noise floor in high-resolution diffusion MRI

Huynh, K. M., Chang, W., Wu, Y., & Yap, P. (2024) · Patterns · doi:10.1016/j.patter.2024.100954 · Cited by 7

Combining Improved Euler and Runge-Kutta 4th order for Tractography in Diffusion-Weighted MRI

Cherifi, D., Boudjada, M., Morsli, A., Girard, G., & Rachid Deriche, B. (2017) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2017.11.008 · 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

Deep learning-based diffusion MRI tractography: Integrating spatial and anatomical information

Yang, Y., Yuan, Y., Ren, B., Wu, Y., Feng, Y., & Zhang, X. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121314 · Cited by 6

TractOracle: Towards an Anatomically-Informed Reward Function for RL-Based Tractography

Théberge, A., Descoteaux, M., & Jodoin, P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72069-7_45 · Cited by 5

Super-resolution mapping of anisotropic tissue structure with diffusion MRI and deep learning

Ordinola, A., Abramian, D., Herberthson, M., Eklúnd, A., & Özarslan, E. (2025) · Scientific Reports · doi:10.1038/s41598-025-90972-7 · Cited by 5

Structural Connectivity Differences Reflect Microstructural Heterogeneity of the Human Insular Cortex

Quabs, J., Bittner, N., & Caspers, S. (2025) · Human Brain Mapping · doi:10.1002/hbm.70231 · Cited by 6

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

Diffusion coefficient orientation distribution function for diffusion magnetic resonance imaging

Shi, D., Pan, Z., Li, X., Guo, H., & Zheng, Q. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108986 · Cited by 5

ReTrace: Topological Evaluation of White Matter Tractography Algorithms Using Reeb Graphs

Shailja, S., Chen, J. W., Grafton, S. T., & Manjunath, B. S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47292-3_16 · Cited by 5

Global tractography

Daducci, A., Schiavi, S., Christiaens, D., Smith, R. E., & Alexander, D. C. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00014-8 · Cited by 7

Semi-local tractography strategies using neighborhood information

Schomburg, H., & Hohage, T. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.03.003 · Cited by 5

Construction of brain structural connectivity network using a novel integrated algorithm based on ensemble average propagator

Wu, Z., Peng, Y., Xu, D. R., Hong, M., & Zhang, Y. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103384 · Cited by 5

Riemann-DTI Geodesic Tractography Revisited

Florack, L. M. J., Sengers, R., Meesters, S., Smolders, L., & Fuster, A. (2021) · Mathematics and visualization · doi:10.1007/978-3-030-56215-1_11 · Cited by 5

A Multimodal Multilevel Neuroimaging Model for Investigating Brain Connectome Development

Hu, Y., Zeydabadinezhad, M., Li, L., & Guo, Y. (2022) · Journal of the American Statistical Association · doi:10.1080/01621459.2022.2055559 · Cited by 5

A validation framework for neuroimaging software: the case of population receptive fieldsPreprint

Lerma‐Usabiaga, G., Benson, N. C., Winawer, J. A., & Wandell, B. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.07.897991 · Cited by 5

MPI-LiFE: Designing High-Performance Linear Fascicle Evaluation of Brain Connectome with MPI

Gugnani, S., Lu, X., Pestilli, F., Caiafa, C. F., & Panda, D. K. D. (2017) · IEEE International Conference on High Performance Computing, Data, and Analytics · doi:10.1109/hipc.2017.00033 · Cited by 4

General orientation transform for the estimation of fiber orientations in white matter tissues

Shi, D., Li, S., Chen, L., Li, X., Guo, H., & Zheng, Q. (2022) · Magnetic Resonance in Medicine · doi:10.1002/mrm.29256 · Cited by 4

Presurgical simulation for neuroendoscopic procedures: Virtual study of the integrity of neurological pathways using diffusion tensor imaging tractography

García-García, S., Kakaizada, S., Oleaga, L., Benet, A., Rincon-Toroella, J., & Sánchez, J. J. G. (2019) · Neurology India · doi:10.4103/0028-3886.263199 · Cited by 4

White Matter Fiber Tractography Using Nonuniform Rational B-Splines Curve Fitting

Wu, Z., Wu, D., & Xu, D. R. (2018) · Journal of Healthcare Engineering · doi:10.1155/2018/8643871 · Cited by 4

MaPPeRTrac: A Massively Parallel, Portable, and Reproducible Tractography PipelinePreprint

Moon, J. Y., Bremer, P., Mukherjee, P., Markowitz, A. J., Palacios, E. M., Cai, L. T., Rodriguez, A., Xiao, Y., Manley, G. A., & Madduri, R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.23.424191 · 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

Reliability of Structural Connectivity Examined with Four Different Diffusion Reconstruction Methods at Two Different Spatial and Angular Resolutions

Villalón‐Reina, J. E., Nir, T. M., Zhan, L., McMahon, K. L., de Zubicaray, G. I., Wright, M. J., Jahanshad, N., & Thompson, P. M. (2016) · Mathematics and visualization · doi:10.1007/978-3-319-28588-7_19 · Cited by 3

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

Validation of MRI-Based Fiber-Tracking Results

Il'yasov, K. A., Konopleva, L. V., & Nedopekin, O. V. (2017) · Applied Magnetic Resonance · doi:10.1007/s00723-017-0859-z · Cited by 3

A PSO-Powell Hybrid Method to Extract Fiber Orientations from ODF

Wu, Z., Yu, X., Liu, Y., & Hong, M. (2018) · Computational and Mathematical Methods in Medicine · doi:10.1155/2018/7680164 · Cited by 3

Probabilistic Tractography for Complex Fiber Orientations with Automatic Model Selection

Versteeg, E., Vos, F. M., Kwakkel, G., van der Helm, F. C. T., Arkesteijn, J. A. M., & Filatova, O. G. (2018) · Mathematics and visualization · doi:10.1007/978-3-319-73839-0_9 · Cited by 2

False positive bundles in tractography

Descoteaux, M., Sidhu, J., Garyfallidis, E., Houde, J., Neher, P., Stieltjes, B., & Maier-Hein, K. H. (2016) · Cited by 3

White Matter Fiber Tracking Method with Adaptive Correction of Tracking Direction

Zheng, Q., Guo, K., Meng, Y., Nan, J., & Xu, L. (2024) · International Journal of Biomedical Imaging · doi:10.1155/2024/4102461 · Cited by 3

MaPPeRTrac: A Massively Parallel, Portable, and Reproducible Tractography Pipeline

Cai, L. T., Moon, J. Y., Camacho, P., Anderson, A. T., Chwa, W. J., Sutton, B. P., Markowitz, A. J., Palacios, E. M., Rodriguez, A., Manley, G. T., et al. (2024) · Neuroinformatics · doi:10.1007/s12021-024-09650-0 · Cited by 5

Optimized Uniform Sampling and Validation of Fiber Tracts From Magnetic Resonance Tractography for In Vivo Architectural Measurement of Human Forearm Muscles

Li, Y., Ma, S., Zhao, J., Li, Q., & Sheng, X. (2024) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2024.3423320 · Cited by 3

Repeated Tractography of a Single Subject: How High Is the Variance?

Gu, X., Eklund, A., & Knutsson, H. E. (2017) · Mathematics and visualization · doi:10.1007/978-3-319-61358-1_14 · Cited by 3

Towards Learned Optimal q-Space Sampling in Diffusion MRI

Weiss, T., Vedula, S., Senouf, O. Y., Michailovich, O., & Bronstein, A. (2021) · Mathematics and visualization · doi:10.1007/978-3-030-73018-5_2 · Cited by 3

The Case for Optimized Edge-Centric Tractography at Scale

Moon, J. Y., Mukherjee, P., Madduri, R., Markowitz, A. J., Cai, L. T., Palacios, E. M., Manley, G. T., & Bremer, P. (2022) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2022.752471 · Cited by 3

Mitigating Gyral Bias in Cortical Tractography via Asymmetric Fiber Orientation DistributionsPreprint

Wu, Y., Hong, Y., Feng, Y., Shen, D. G., & Yap, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.03328 · 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

Prediction of the Topography of the Corticospinal Tract on T1-Weighted MR Images Using Deep-Learning-Based Segmentation

Bárány, L., Hore, N., Stadlbauer, A., Buchfelder, M., & Brandner, S. (2023) · Diagnostics · doi:10.3390/diagnostics13050911 · Cited by 2

BundleCleaner: Unsupervised Denoising and Subsampling of Diffusion MRI-Derived Tractography Data

Feng, Y., Chandio, B. Q., Villalón‐Reina, J. E., Thomopoulos, S. I., Joshi, H., Nair, G., Joshi, A. A., Venkatasubramanian, G., John, J. P., & Thompson, P. M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47292-3_14 · Cited by 2

Flocking Method for Identifying of Neural Circuits in Optogenetic Datasets

Zaleshina, M., & Zaleshin, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-53969-5_4 · Cited by 2

Bounding tractogram redundancy

Persson, S., & Moreno, R. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1403804 · 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

Can functional connectivity be used to refine structural connectivity strength by combining neural computational model and generative adversarial network?

Wu, Z., Yu, J., Chen, X., & Shen, J. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10703-3 · Cited by 2

Current challenges and opportunities for tractography

Rheault, F., Poulin, P., Caron, A. V., St‐Onge, E., Schilling, K. G., Petit, L., Dell’Acqua, F., Leemans, A., & Descoteaux, M. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00037-9 · Cited by 2

Tractography validation Part 2: The use of anatomical model systems and measures for validation

Dyrby, T. B., Sarubbo, S., Rheault, F., Fieremans, E., Anderson, A. W., Palombo, M., Neher, P., Rockland, K. S., & Schilling, K. G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00020-3 · Cited by 4

Tractography validation Part 1: Foundations, numerical simulations, and phantom models

Dyrby, T. B., Fieremans, E., Rheault, F., Anderson, A. W., Palombo, M., Sarubbo, S., Neher, P., & Schilling, K. G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00017-3 · Cited by 2

Replace2Self: Self-Supervised Denoising Based on Voxel Replacing and Image Mixing for Diffusion MRI

Wu, L., Wang, L., Deng, Z., Zhu, Y., & Wei, H. (2025) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2025.3552611 · Cited by 2

Longitudinal Harmonization for Improving Tractography in Baby Diffusion MRI

Huynh, K. M., Kim, J., Chen, G., Wu, Y., Shen, D. G., & Yap, P. (2019) · Mathematics and visualization · doi:10.1007/978-3-030-05831-9_15 · Cited by 2

A community-based topological distance for brain-connectome classification

Villareal–Haro, J. L., Ramírez-Manzanares, A., & Pichardo-Corpus, J. A. (2020) · Journal of Complex Networks · doi:10.1093/comnet/cnaa034 · Cited by 2

A Novel Richardson-Lucy Model with Dictionary Basis and Spatial Regularization for Isolating Isotropic Signals

Xu, T., Feng, Y., Wu, Y., Zeng, Q., Zhang, J., He, J., & Zhuge, Q. (2017) · PLoS ONE · doi:10.1371/journal.pone.0168864 · 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

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 2

Quantitative evaluation of fiber tractography with a Delaunay triangulation–based interpolation approach

Alaya, I. B., Jribi, M., Ghorbel, F., Mars, M., & Kraiem, T. (2018) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-018-1932-y · Cited by 2

Fast Tractography Streamline Search

St‐Onge, E., Garyfallidis, E., & Collins, D. L. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87615-9_8 · Cited by 1

Evaluating the impact of denoising diffusion MRI data on tractometry metrics of optic tract abnormalities in glaucoma

Taguma, D., Ogawa, S., & Takemura, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-10947-6 · Cited by 1

DDTracking: A diffusion model-based deep generative framework with local-global spatiotemporal modeling for diffusion MRI tractography

Li, Y., Zhang, W., Zhu, X., Wu, Y., Yogesh Rathi, A., O’Donnell, L. J., & Zhang, F. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.103967 · Cited by 1

Diffusion tensor distribution function metrics boost power to detect deficits in Alzheimer's disease

Nir, T. M., Thompson, P. M., Zavaliangos‐Petropulu, A., Jahanshad, N., Villalón‐Reina, J. E., Zhan, L., Leow, A., Bernstein, M. A., Jack, C. R., & Weiner, M. W. (2016) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2016.7493455 · Cited by 1

Interactive Computation and Visualization of Structural Connectomes in Real-Time

Chamberland, M., Gray, W., Descoteaux, M., & Jones, D. K. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67159-8_5 · Cited by 1

dMRI: Diffusion Magnetic Resonance Imaging as a Window onto Structural Brain Networks and White Matter Microstructure

Roebroeck, A. F. (2021) · Springer eBooks · doi:10.1007/978-3-030-59797-9_6 · Cited by 1

A Convolutional Wasserstein Distance for Tractography Evaluation: Complementarity Study to State-of-the-Art Measures

Durantel, T., Coloigner, J., & Commowick, O. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761650 · Cited by 1

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 2

Verification of Diffusion MRI Fiber Tracking Results In Vivo

Konopleva, L. V., Nedopekin, O. V., & Il'yasov, K. A. (2018) · Applied Magnetic Resonance · doi:10.1007/s00723-018-1044-8 · Cited by 1

Quantitative Evaluation of Enhanced Multi-plane Clinical Fetal Diffusion MRI with a Crossing-Fiber Phantom

Kebiri, H., Lajous, H., Alemán‐Gómez, Y., Girard, G., Canales-Rodríguez, E. J., Tourbier, S., Pizzolato, M., Ledoux, J., Fornari, E., András, J., et al. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87615-9_2 · Cited by 1

New Algorithms for Local and Global Fiber Tractography in Diffusion-Weighted Magnetic Resonance Imaging

Schomburg, H. (2017) · eDiss (Georg-August-Universität Göttingen) · doi:10.53846/goediss-6602 · Cited by 1

Tractostorm: Rater reproducibility assessment in tractography dissection of the pyramidal tractPreprint

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. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/623892 · Cited by 1

Transformer-Based Framework for Fiber Orientation Estimation & TractographyPreprint

Hosseini, S.-M. H., Hassanpour, M., Masoudnia, S., Iraji, S., Raminfard, S., & Nazem-Zadeh, M.-R. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4157505 · Cited by 1

Image processing methods for human brain connectivity analysis from in-vivo diffusion MRI

Estéban, O. (2013) · Hispana · Cited by 1

Tractometer: Online Evaluation System for Tractography

Côté, M.-A., Boré, A., Girard, G., Houde, J., & Descoteaux, M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33415-3_86 · Cited by 31

Strengths and weaknesses of state of the art fiber tractography pipelines – A comprehensive in-vivo and phantom evaluation study using Tractometer

Neher, P., Descoteaux, M., Houde, J., Stieltjes, B., & Maier-Hein, K. H. (2015) · Medical Image Analysis · doi:10.1016/j.media.2015.10.011 · Cited by 68

Improving the Tractography Pipeline: on Evaluation, Segmentation, and Visualization

Reichenbach, A. (2022) · Qucosa (Saxon State and University Library Dresden)

Evaluation and validiation of tractography based connectivity estimates

Knösche, T. R., Yo, T., Trong, M. N., Dyrby, T. B., Liptrop, M., & Anwander, A. (2011)

Fiber up-sampling and quality assessment of tractograms - towards quantitative brain connectivity

Sommer, S., Kozerke, S., Seifritz, E., & Stäempfli, P. (2017) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000129836