Limitations and requirements of diffusion tensor fiber tracking: an assessment using simulations
Open accessAbstract
Diffusion tensor fiber tracking potentially can give information about in vivo brain connectivity. However, this technique is difficult to validate due to the lack of a gold standard. Fiber tracking reliability will depend on the quality of the data and on the robustness of the algorithms used. Information about the effects of various anatomical and image acquisition parameters on fiber tracking reliability may be used in the design of imaging sequences and of tracking algorithms. In this study, tracking was performed on two different simulated models to study the effects on tracking quality of SNR, anisotropy, curvature, fiber cross‐section, background anisotropy, step size, and interpolation. Tracking was also performed on volunteer data to assess the relevance of the simulations to real data. Our results show that, in general, tracking with high SNR and high anisotropy using interpolation and a low step size gives the most reliable results. Partial volume effects are shown to have a detrimental effect when the background is anisotropic and when tracking narrow fibers. The results derived from real data show similar trends and thus support the findings of the simulations. These simulations may therefore help to determine which structures can be tracked for a given image quality. Magn Reson Med 47:701–708, 2002. © 2002 Wiley‐Liss, Inc.
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First-order streamline tractography in curved bundles tends to underestimate curvature, producing erroneous trajectories.
“Possibly the earliest bias established for dMRI tractography was the fact that if the streamline algorithm simply takes a step of finite size in the direction of the local fiber orientation at the current vertex (a so-called “first-order” method), streamlines in curved bundles will tend to under-estimate the curvature, resulting in erroneous trajectories (Tournier et al., 2002).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Tournier et al., 2002
The Tracking Algorithm section describes finite steps taken in the local major-eigenvector direction. Model A reports that larger steps increase endpoint offset and that tracks can depart systematically from an ideal curved path, especially without interpolation. The copy does not establish the claimed direction of the bias—that these tracks tend to underestimate curvature. (LLM, conf=0.85)
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“…have been made to validate the accuracy of DTT maps using physical phantoms (Lin et al., 2003; Perrin et al., 2005), software phantoms (Lori et al., 2002; Tournier et al., 2002) or biological samples of uniform construction (Van Doorn et al., 1996; Van Donkelaar et al., 1999; Lin et al., 2001).”
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“Available online on ScienceDirect (www.sciencedirect.com). understood to have some significant limitations (Basser et al., 2000; Lazar and Alexander, 2003; Tournier et al., 2002), chief among them is its inability to image fiber crossings (Mori and van Zijl, 2002; Wiegell et al., 2000).”
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“Several approaches have been taken to validate the metrics obtained from a variety of diffusion MRI methods ranging from fiber tractography to compartment size mapping, including the use of software phantoms (Balls and Frank, 2009; Close et al., 2009; Hall and Alexander, 2009; Leemans et al., 2005; Tournier et al., 20…”
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“who showed that the accuracy of FT sensitively depends on the step size (27).”
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“Synthetic datasets are used to test tensor fiber tracking [67, 112].”
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“Compared to the related work of Tournier et al. (2002) in which the deviation of single fibers from the ideal pathway is analyzed, the safety radius is a global measure of the tracked fiber bundle which gives us an indication of how close the tracked fiber bundle approaches the true border of the modeled pathway.”
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“Tournier et al, by using simulations on synthetic data, showed that to identify small fibers, the signal-to-noise ratio needs to be high (40).”
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“To make the tensor fields more realistic, noise is added [24].”
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“[55] introduced phantoms based on apparent diffusion coefficient (ADC) and fractional anisotropy (FA), while Leemans et al.”
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“In particular, this technique can be used to infer the directionality of white matter fiber architecture within a voxel.(1,2) With the further development of MRI technology, diffusion tensor imaging (DTI) can be used for the organs in the body.”
MethodLi, M., Ratnanather, J. T., Miller, M. I., & Mori, S. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.10.011 · Cited by 13 · see citing works
“…repeated under different conditions, leading to multiple potential paths from one seed voxel (Behrens et al., 2003; Jeurissen et al., 2011; Jones, 2003; Jones and Pierpaoli, 2005; Lazar and Alexander, 2003, 2005; Lori et al., 2002; Parker et al., 2002; Richter et al., 2013; Tournier et al., 2002).”
BackgroundAn image-processing toolset for diffusion tensor tractography
Mishra, A., Lu, Y., Choe, A. S., Aldroubi, A., Gore, J. C., Anderson, A. W., & Ding, Z. (2006) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2006.10.006 · Cited by 9 · see citing works
“However, only a few conventional interpolation techniques have been chosen for DTI data, such as linear [32], low-order polynomial [33,34], cubic B-spline [11] and nearest-neighbor interpolations [35].”
MethodDiffusion MRI Fiber Tractography
Smith, R., Connelly, A., & Calamante, F. (2020) · Advances in magnetic resonance technology and applications · doi:10.1016/b978-0-12-817057-1.00023-8 · Cited by 12 · see citing works
Self-citationTensor deflection (TEND) tractography with adaptive subvoxel stepping
Chou, M., Wu, M., Chen, C., Wang, C., Huang, T., Liu, Y., Juan, C., & Chung, H. (2006) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.20652 · Cited by 11 · see citing works
Segmentation of diffusion weighted MRI using the level set framework
Jonasson, L. (2005) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5075/epfl-thesis-3315 · Cited by 9 · see citing works
“1 Synthetic Tensor Fields The method was tested on the synthetic images with different levels of SNR, using an SNR of 8, 16 and 32 on the MR amplitude images as in [96].”
InfluentialWang, C.-C., Holly, L., Oughourlian, T., Yao, J., Raymond, C., Salamon, N., & Ellingson, B. (2021) · Journal of clinical neuroscience · doi:10.1016/j.jocn.2021.01.011 · Cited by 10
Kim, J., Phi, J., Lee, J. Y., Kim, K. H., Park, S.-H., Choi, Y.-H., Cho, B., & Kim, S.-K. (2018) · Brain Tumor Research and Treatment · doi:10.14791/btrt.2018.6.e14 · Cited by 10
“First, DTI is dependent on operators which leads to difficult in interpretations and standardization [23].”
BackgroundRatnanather, J. T., Lal, R. M., An, M., Poynton, C. B., Li, M., Jiang, H., Oishi, K., Selemon, L. D., Mori, S., & Miller, M. I. (2013) · Brain Connectivity · doi:10.1089/brain.2013.0143 · Cited by 12 · see citing works
“…limitations such as inability to discriminate crossing fi-bers, low signal-to-noise ratio (SNR), and poor tracking in regions of low anisotropy (Jbabdi and Johansen-Berg, 2011; Lazar and Alexander, 2003; Le Bihan et al., 2006; Moldrich et al., 2010; Mukherjee et al., 2008; Tournier et al., 2002).”
BackgroundSearching for Novel Biomarkers Using High Resolution Diffusion Tensor Imaging
Yassa, M. A. (2011) · Journal of Alzheimer s Disease · doi:10.3233/jad-2011-0005 · Cited by 10 · see citing works
“Various sophisticated fiber-tracking techniques have now become commonplace tools in neuroimaging laboratories for mapping white matter connectivity [8–12].”
Diffusion Tensor Imaging of Cerebral White Matter*
Stoeter, P., Dellani, P. R., & Vučurević, G. (2008) · Clinical Neuroradiology · doi:10.1007/s00062-008-8019-3 · Cited by 3 · see citing works
“Only after refinement of the original data by oversampling techniques, approximation and interpolation [12], reliable and smoothly running trajectories could be achieved.”
MethodRatnarajah, N., Simmons, A., Davydov, O., & Hojjatoleslami, A. (2011) · Medical Image Analysis · doi:10.1016/j.media.2011.07.005 · Cited by 10 · see citing works
“Several studies (Lazar and Alexander, 2003b; Tournier et al., 2002) have investigated the effects of image noise, tensor anisotropy, step size, and tract geometry on the accuracy of deterministic tractography algorithms.”
BackgroundBayesian regularization of diffusion tensor images
Frandsen, J. R., Hobolth, A., Østergaard, L., Vestergaard‐Poulsen, P., & Jensen, E. B. V. (2006) · Biostatistics · doi:10.1093/biostatistics/kxm005 · Cited by 8 · see citing works
Complementary Detection of Multiple Electrical Sources in Tissue Using Acoustoelectric Effects.
Wang, Z., Challoo, R., Peng, H., Leung, C. S., & Witte, R. (2016) · Ultrasound in Medicine and Biology · doi:10.1016/j.ultrasmedbio.2016.05.013 · Cited by 7
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 · see citing works
“Early digital phantoms started with simulation of simple geometries (Basser et al., 2000; G¨ossl et al., 2002; Tournier et al., 2002; Leemans et al., 2005) to evaluate the angular resolution as well.”
BackgroundDiffusion Tensor Magnetic Resonance Imaging in Autism
Travers, B. G., & Alexander, A. L. (2013) · Springer eBooks · doi:10.1007/978-1-4614-6843-1_8 · Cited by 3 · see citing works
“Measurement noise also causes errors in the DTI eigenvector directions (Anderson 2001; Lori et al. 2002; Tournier et al. 2002; Lazar and Alexander 2003), though these errors are relatively small in homogeneous WM pathways.”
BackgroundNew insights into the neurobiology of language from functional brain imaging
Tremblay, P., Dick, A. S., & Small, S. L. (2011) · Springer eBooks · doi:10.1007/978-3-7091-0723-2_10 · Cited by 3 · see citing works
“Despite these advances in understanding fiber pathways in the human brain, DTI tractography has some serious shortcomings that are often minimized or ignored in the literature [5, 86].”
BackgroundMethodUğurlu, 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
The role for diffusion tensor imaging in neuropsychiatry
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Global brain connectivity analysis by diffusion MR tractography
Gigandet, X. (2009) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.5075/epfl-thesis-4511 · Cited by 6 · see citing works
“In order to tackle this issue, higher angular resolution diffusion MRI methodologies have been designed, such as Q-ball [215] or spherical deconvolution [210].”
MethodAdvances in longitudinal MRI diagnostic tests
Hernández-García, L., & Buschkuehl, M. (2012) · Expert Opinion on Medical Diagnostics · doi:10.1517/17530059.2012.686995 · Cited by 5 · see citing works
“partial volume effects jeopardize the results of fiber-tracking algorithms, and that regions of low FA can also lead the fiber-tracking process astray [120].”
BackgroundOn the Reliability of Quantitative Volumetric and Structural Neuroimaging
Klein, J., Laun, F. B., Erhard, P., Diehl, V., & Hahn, H. K. (2008) · Imaging Decisions MRI · doi:10.1111/j.1617-0830.2009.00128.x · Cited by 3 · see citing works
“[4, 5] describe fibers by simple 2D rings in tensor fields, whereas other authors [30, 45, 60] define fibers by cylindric tubes in 3D tensor fields.”
BackgroundA method for non-rigid registration of diffusion tensor magnetic resonance images
Duda, J., Rivera, M., Alexander, D., & Gee, J. (2003) · SPIE Medical Imaging · doi:10.1117/12.481155 · Cited by 5
Quantitative Analysis and Comparison of Diffusion Tensor Imaging Tractography Algorithms
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“This is in line with other results reported in the literature [23].”
ResultSharma, A., Cabeen, R. P., Harris, N. G., Wright, D. K., Tohka, J., Gröhn, O. H. J., Bennett, A., Alba, C., Bhagavatula, S., Garner, R., et al. (2025) · Imaging Neuroscience · doi:10.1162/imag_a_00471 · Cited by 4 · see citing works
“However, conventional diffusion tensor models are constrained by their single-fiber orientation representation and struggle in areas of complex fiber structures, such as regions with crossing fibers (Homos et al., 2017; Tournier et al., 2002).”
The Efficiency of Diffusion Weighted MRI and MR Spectroscopy On Breast MR Imaging.
Altay, C., & Balcı, P. (2014) · Journal of Breast Health · doi:10.5152/tjbh.2014.2142 · Cited by 3
Neuronal Fiber--tracking via optimal mass transportation
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Uncertainty in the DTI Visualization Pipeline
Siddiqui, F., Höllt, T., & Vilanova, A. (2021) · Mathematics and visualization · doi:10.1007/978-3-030-56215-1_6 · Cited by 3 · see citing works
“The integration step, or step size, further affect the quality of these integration schemes [97].”
BackgroundChapter 20 – Validation of Tractography
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“Mit steileren Gradientenfeldern, wie sie in den neueren MR-Geräten zur Verfügung stehen, sind zwar Messungen bei höheren b-Werten möglich, um so die Anisotropie zu vergrößern, was jedoch wegen der gleichzeitigen Abnahme des S/N mit einem Verlust an Datenhomogenität erkauft wird und nach unseren Erfahrungen die Trackin…”
BackgroundJin, R., Cai, Y., Zhang, S., Yang, T., Feng, H., Jiang, H.-Y., Zhang, X., Hu, Y., & Liu, J. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1191999
“Nevertheless, fiber tracking is time-consuming and its computational process is complicated and easily affected by noises and artifacts (Tournier et al., 2002; Lazar and Alexander, 2003).”
BackgroundShape deformation measures for white matter fibers
Güler, R. A., Ugurlu, D., Fırat, Z., Türe, U., & Unal, G. (2016) · Signal Processing and Communications Applications Conference · doi:10.1109/siu.2016.7495980
Study of the sources of errors in fiber tracking using diffusion tensor MRI
Tawfik, M. M., Youssef, A.-B. M., & Kadah, Y. (2008) · National Radio Science Conference · doi:10.1109/nrsc.2008.4542384
Kleiser, R., Nickel, J., Seitz, R., Trenkler, J., & Wurm, G. (2012) · doi:10.1007/978-94-007-1706-0_19
- Authors
- Tournier, J. ‐., Calamante, F., King, M. D., Gadian, D. G., & Connelly, A.
- Affiliations
- Great Ormond Street Hospital (GB)
- University College London (GB)
- Corresponding author
- Tournier, J.‐D.
- Venue
- Magnetic Resonance in Medicine
- Publisher
- Wiley
- Published
- 2002-03-28
- Volume
- 47
- Issue
- 4
- Pages
- 701-708
- ISSN
- 0740-3194 (ISSN-L)
- 1522-2594
- OpenAlex
- W2047050738
- Cited by
- 114
- References
- 0
- Citations by year
- 2025: 2 · 2024: 1 · 2023: 1 · 2022: 2 · 2021: 4 · 2020: 4 · 2018: 5 · 2017: 5 · 2016: 7 · 2015: 4 · 2014: 7 · 2013: 7 · 2012: 9
- FWCI
- 5.47
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Magnetic Resonance in Medicine (publishedVersion)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Research Portal (King's College London) (submittedVersion)
- Funders
- Wellcome Trust
- Great Ormond Street Hospital for Children
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- signal-to-noise ratio, anisotropy, fiber curvature, partial volume effect, step size, interpolation, tracking algorithm, white matter connectivity, image acquisition parameters
- MeSH terms
- Algorithms, Brain, Computer Simulation, Diffusion, Humans, Magnetic Resonance Imaging, Anisotropy
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