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Can increased spatial resolution solve the crossing fiber problem for diffusion MRI?
Open accessAbstract
It is now widely recognized that voxels with crossing fibers or complex geometrical configurations present a challenge for diffusion MRI (dMRI) reconstruction and fiber tracking, as well as microstructural modeling of brain tissues. This “crossing fiber” problem has been estimated to affect anywhere from 30% to as many as 90% of white matter voxels, and it is often assumed that increasing spatial resolution will decrease the prevalence of voxels containing multiple fiber populations. The aim of this study is to estimate the extent of the crossing fiber problem as we progressively increase the spatial resolution, with the goal of determining whether it is possible to mitigate this problem with higher resolution spatial sampling. This is accomplished using ex vivo MRI data of the macaque brain, followed by histological analysis of the same specimen to validate these measurements, as well as to extend this analysis to resolutions not yet achievable in practice with MRI. In both dMRI and histology, we find unexpected results: the prevalence of crossing fibers increases as we increase spatial resolution. The problem of crossing fibers appears to be a fundamental limitation of dMRI associated with the complexity of brain tissue, rather than a technical problem that can be overcome with advances such as higher fields and stronger gradients.
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“suggesting that this is a fundamental problem that cannot be overcome by scanning at higher resolution to reduce partial volume effects (Schilling et al., 2017).”
From Potential Pitfalls of Using Fractional Anisotropy, Axial Diffusivity, and Radial Diffusivity as Biomarkers of Cerebral White Matter Microstructure (2022) · cites this paper as Schilling et al., 2017
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“ex vivo MRI and histological studies of the macaque brain have revealed that white matter crossing fibers become even more apparent upon higher resolution and more detailed examination”
From Potential Pitfalls of Using Fractional Anisotropy, Axial Diffusivity, and Radial Diffusivity as Biomarkers of Cerebral White Matter Microstructure (2022) · cites this paper as Schilling et al., 2017
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Citations
Cites
A new look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
Spin Diffusion Measurements: Spin Echoes in the Presence of a Time-Dependent Field Gradient
Stejskal, E. O., & Tanner, J. E. (1965) · The Journal of Chemical Physics · doi:10.1063/1.1695690 · Cited by 8,239
Estimation of the Effective Self-Diffusion Tensor from the NMR Spin Echo
Basser, P. J., Mattiello, J., & Bihan, D. L. (1994) · Journal of Magnetic Resonance Series B · doi:10.1006/jmrb.1994.1037 · Cited by 3,656
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
NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain
Zhang, H., Schneider, T., Wheeler-Kingshott, C. A., & Alexander, D. C. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.072 · Cited by 3,288
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×
“In the neuroimaging community, dMRI research can loosely be divided into two main classes.(2) The first concerns mapping the neural fiber pathways, or connectivity, of the brain.”
Tournier, J., Calamante, F., & Connelly, A. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.02.016 · Cited by 2,191
Fiber tracking: principles and strategies – a technical review
Mori, S., & van Zijl, P. C. M. (2002) · NMR in Biomedicine · doi:10.1002/nbm.781 · Cited by 2,019
Tournier, J.-D., Calamante, F., Gadian, D. G., & Connelly, A. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.037 · Cited by 1,635
High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity
Tuch, D. S., Reese, T. G., Wiegell, M. R., Makris, N., Belliveau, J. W., & Wedeen, V. J. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10268 · Cited by 1,548
Research works printed but not linked: 3
- Tariq M, Medical Image Computing and Computer‐Assisted Intervention—MICCAI 2014: 17th International Conference, Boston, MA, USA, 241
- Bigun J, 1987, First International Conference on Computer Vision (London, Great Britain), 433
- Evans M, 2000, Statistical Distributions
Cited by
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Histological validation of diffusion MRI fiber orientation distributions and dispersion
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Self-citationValidation strategies for the interpretation of microstructure imaging using diffusion MRI
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Girard, G., Caminiti, R., Battaglia-Mayer, A., St-Onge, E., Ambrosen, K. S., Eskildsen, S. F., Krug, K., Dyrby, T. B., Descoteaux, M., Thiran, J.-P., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117201 · Cited by 83
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Self-citationThe glial framework reveals white matter fiber architecture in human and primate brains
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Gatto, R. G., Amin, M., Finkielsztein, A., Weissmann, C., Barrett, T., Lamoutte, C., Uchitel, O., Sumagin, R., Mareci, T. H., & Magin, R. L. (2019) · Amyotrophic Lateral Sclerosis and Frontotemporal Degeneration · doi:10.1080/21678421.2019.1620285 · Cited by 31
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Luca, A. D., Guo, F., Froeling, M., & Leemans, A. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117206 · Cited by 29
Schilling, K. G., Li, M., Rheault, F., Ding, Z., Anderson, A. W., Kang, H., Landman, B. A., & Gore, J. C. (2022) · Cerebral Cortex Communications · doi:10.1093/texcom/tgac035 · Cited by 24
Self-citationGatto, R. G. (2020) · Journal of Integrative Neuroscience · doi:10.31083/j.jin.2020.03.165 · Cited by 27
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Lozada, P. S. M., Pozo-Neira, J., & León-Rojas, J. E. (2025) · Cancers · doi:10.3390/cancers17132174 · Cited by 19
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
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Andrews, L., Keller, S. S., Osman‐Farah, J., & Macerollo, A. (2023) · Brain Communications · doi:10.1093/braincomms/fcad171 · Cited by 17
Jones, R. J., Maffei, C., Augustinack, J. C., Fischl, B. R., Wang, H., Bilgiç, B., & Yendiki, A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118621 · Cited by 17
Jossinger, S., Kronfeld-Duenias, V., Zislis, A., Amir, O., & Ben‐Shachar, M. (2021) · Brain Structure and Function · doi:10.1007/s00429-020-02210-7 · Cited by 16
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Leppert, I. R., Bontempi, P., Rowley, C. D., Campbell, J. S. W., Nelson, M. C., Schiavi, S., Pike, G. B., Daducci, A., & Tardif, C. L. (2023) · Imaging Neuroscience · doi:10.1162/imag_a_00019 · Cited by 11
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Charvet, C. J., Palani, A., Kabaria, P., & Takahashi, E. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz054 · Cited by 9
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Self-citationZeng, Z., Jia, X., Han, S., Zeng, C., Bi, J., Liu, L., Wu, Y., Gao, T., Liang, L., & Cheng, F. (2026) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2026.1733820 · Cited by 3
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Unraveling multi-fixel microstructure with tractography and angular weighting
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Gao, J., Jiang, M. C., Erricolo, D., Magin, R. L., Morfini, G. A., Royston, T. J., Larson, A. C., & Li, W. (2023) · NMR in Biomedicine · doi:10.1002/nbm.5037 · Cited by 3
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Zheng, J., Li, Z., Zhong, W., Wang, Z., Li, Z., Yang, H., Liu, M., Cao, X., Liao, C., Salat, D. H., et al. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.1089 · Cited by 1
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Cavallo, M., Ricchi, M., Axford, A., Yeung, K., McGing, J., Brizi, L., Tyler, D. J., Testa, C. M., & Grist, J. T. (2026) · Scientific Reports · doi:10.1038/s41598-026-38964-z · Cited by 1
Martinez, J. D., Mooshagian, E., Wang, H., Karlsgodt, K. H., Uddin, L. Q., & Nomi, J. S. (2026) · Brain Structure and Function · doi:10.1007/s00429-026-03091-y · Cited by 1
Arciniega, H., Wickham, A., Szekely, B., Kim, N., Cho, K. I., Carrington, H., Knyazhanskaya, E. E., John, O., Jung, L., Breedlove, K. M., et al. (2026) · Brain Communications · doi:10.1093/braincomms/fcag195 · Cited by 2
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Nath, V., Schilling, K. G., Remedios, S. W., Bayrak, R. G., Gao, Y., Blaber, J. A., Huo, Y., Landman, B. A., & Anderson, A. W. (2019) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2019.8759388 · Cited by 1
Self-citationReishofer, G., Studencnik, F., Koschutnig, K., Deutschmann, H., Gunzer, F., Hassler, E. M., Leber, S. L., & Wood, G. M. D. O. (2021) · Fractals · doi:10.1142/s0218348x21502327 · Cited by 1
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Campbell, J. S. W., Baete, S. H., Cohen‐Adad, J., Tournier, J., Szczepankiewicz, F., Beaulieu, C., Baron, C. A., Mani, M., Setsompop, K., Liao, C., et al. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00019-7 · Cited by 1
Anpilogova, K. S., Chegina, D. S., Ignatova, T. S., Efimtsev, A. Y., & Труфанов, Г. Е. (2021) · Translational Medicine · doi:10.18705/2311-4495-2021-8-4-27-34 · Cited by 1
Kjer, H. M., Andersson, M., He, Y., Pacureanu, A., Daducci, A., Pizzolato, M., Salditt, T., Robisch, A.-L., Eckermann, M., Töpperwien, M., et al. (2024) · eLife · doi:10.7554/elife.94917.2 · Cited by 1
HARP: Hierarchical Anatomical Refinement of Pathways for Whole-Brain TractographyPreprint
Leserri, S., Rockland, K. S., & Aydogan, D. B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.27.728127 · Cited by 1
Lan, Z., & Reich, B. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.06459
Yarmohamadi, Z., Yaghoobi, A., Rashid, K., & Noghabi, R. K. (2022) · Shenakht Journal of Psychology and Psychiatry · doi:10.32598/shenakht.8.6.83
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Self-citationArdesch, D. J. (2023) · doi:10.5463/thesis.22
Seddighi, A. S., Seddighi, A., Ghadirian, M., Zali, A., Ommi, D., Far, S. M. T., Farsani, H. A., & Masoudian, N. (2022) · Iranian Journal of Neurosurgery · doi:10.32598/irjns.specialissue.5
Sughrue, M. E., Yeung, J. T., & Dadario, N. B. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-19089-6.00007-0
Luca, A. D., & Leemans, A. (2024) · Springer eBooks · doi:10.1007/978-3-031-70337-9_5 · Cited by 1
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Schilling, K. G., Rheault, F., & Dyrby, T. B. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-818894-1.00004-5
Self-citationCamacho, P., Wilcox, R. R., Barbey, A. K., & Sutton, B. P. (2025) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197677131.013.11
Kaur, S., Vanier, D., Dimond, D., Graff, K., Tansey, R. C., Ip, A., Yin, S., Merrikh, D., Rohr, C. S., Lebel, C. A., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5363527
End-to-end evaluation of white matter microstructure of the visual pathway in asymmetric glaucomaPreprint
Coutiño, D., Guerrero, J., Domínguez-Frausto, C. A., García-Guillén, M., Coronado-Leija, R., Ramírez-Manzanares, A., Hernandez-Gutierrez, E., Descoteaux, M., Ayala, M., Badillo, M., et al. (2025) · bioRxiv · doi:10.1101/2025.10.20.683506
Ghassemi, L., Mehdizadeh, A., Ketabi, A., Ostad, S. P., & Tavakoli, M. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.109520
Pyman, P., Kelly, C. E., Dhollander, T., Delagneau, G., Elliott, E., Penington, A. J., Hearps, S., Collins, S. E., Lewis, S., Liang, X., et al. (2026) · Brain Imaging and Behavior · doi:10.1007/s11682-026-01110-4
Modeling a shared reality of tractography through varied structural imaging
Schwartz, T., McMaster, E. M., Rudravaram, G., Cho, C., Krishnan, A. R., Kim, M. E., Samir, J., Bilgel, M., Resnick, S. M., Beason‐Held, L. L., et al. (2026) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.3086475
Self-citationConnectional neuroanatomy of U-fibers in the rhesus monkey brainPreprint
Capen, T., O’Donnell, L. J., Zhang, F., Yeterian, E. H., Yogesh Rathi, A., Makris, N., Rosene, D. L., & Rushmore, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.06.730597
Metadata
- Authors
- Schilling, K., Gao, Y., Janve, V., Stepniewska, I., Landman, B. A., & Anderson, A. W.
- Affiliations
- Vanderbilt University (US)
- Corresponding author
- Schilling, Kurt
- Venue
- NMR in Biomedicine
- Publisher
- Wiley
- Published
- 2017-09-15
- Volume
- 30
- Issue
- 12
- ISSN
- 0952-3480 (ISSN-L)
- 1099-1492
- PMC
- PMC5685916
- OpenAlex
- W2756010063
- Cited by
- 102
- References
- 50
- Citations by year
- 2026: 11 · 2025: 19 · 2024: 13 · 2023: 13 · 2022: 11 · 2021: 13 · 2020: 12 · 2019: 6 · 2018: 3 · 2017: 1
- FWCI
- 4.62
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- NMR in Biomedicine (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (R01 NS058639, S10 RR17799)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- crossing fibers, diffusion MRI, spatial resolution, tractography, white matter microstructure, macaque brain, histopathological confirmation, diffusion MRI reconstruction
- MeSH terms
- Diffusion, Diffusion Magnetic Resonance Imaging, Signal-To-Noise Ratio
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