Angular versus spatial resolution trade-offs for diffusion imaging under time constraints
Open accessAbstract
Diffusion weighted magnetic resonance imaging (DW-MRI) are now widely used to assess brain integrity in clinical populations. The growing interest in mapping brain connectivity has made it vital to consider what scanning parameters affect the accuracy, stability, and signal-to-noise of diffusion measures. Trade-offs between scan parameters can only be optimized if their effects on various commonly-derived measures are better understood. To explore angular versus spatial resolution trade-offs in standard tensor-derived measures, and in measures that use the full angular information in diffusion signal, we scanned eight subjects twice, 2 weeks apart, using three protocols that took the same amount of time (7 min). Scans with 3.0, 2.7, 2.5 mm isotropic voxels were collected using 48, 41, and 37 diffusion-sensitized gradients to equalize scan times. A specially designed DTI phantom was also scanned with the same protocols, and different b-values. We assessed how several diffusion measures including fractional anisotropy (FA), mean diffusivity (MD), and the full 3D orientation distribution function (ODF) depended on the spatial/angular resolution and the SNR. We also created maps of stability over time in the FA, MD, ODF, skeleton FA of 14 TBSS-derived ROIs, and an information uncertainty index derived from the tensor distribution function, which models the signal using a continuous mixture of tensors. In scans of the same duration, higher angular resolution and larger voxels boosted SNR and improved stability over time. The increased partial voluming in large voxels also led to bias in estimating FA, but this was partially addressed by using "beyond-tensor" models of diffusion.
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DTI measures vary with voxel size due to partial voluming.
“This is consistent with the notion that DTI measures vary with voxel size due to partial voluming (Zhan et al., 2013).”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Zhan et al., 2013
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Self-citationZhan, L., Liu, Y., Zhou, J., Ye, J., & Thompson, P. M. (2015) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7163833 · Cited by 5 · see citing works
Self-citationReynolds, G., Nir, T. M., Jahanshad, N., Prasad, G., & Thompson, P. M. (2014) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2014.6868048 · Cited by 5 · see citing works
Self-citationHe, Y., Wang, L., Yang, F., Clarysse, P., Robini, M. C., & Zhu, Y. (2022) · International Conference on Signal Processing · doi:10.1109/icsp56322.2022.9965350 · Cited by 3 · see citing works
Yang, J., Carl, B., Nimsky, C., & Bopp, M. H. A. (2020) · PLoS ONE · doi:10.1371/journal.pone.0233474 · Cited by 1 · see citing works
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Coveney, S., Shelley, D., Foster, R. J., Afzali, M., Poenar, A., Sharrack, N., Plein, S., Dall’Armellina, E., Schneider, J. E., Nguyen, C. T., et al. (2025) · medRxiv · doi:10.1101/2025.02.08.25321885 · Cited by 1 · see citing works
WITHDRAWN: Mapping Connectivity in the Developing Brain
Dennis, E. L., & Thompson, P. M. (2013) · International Journal of Developmental Neuroscience · doi:10.1016/j.ijdevneu.2013.05.004
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Self-citationSpatial Image Gradient Estimation From the Diffusion MRI Profile
Aganj, I., Feiweier, T., KIRSCH, J. E., Fischl, B. R., & van der Kouwe, A. J. W. (2026) · Magnetic Resonance in Medicine · doi:10.1002/mrm.70258
- Authors
- Zhan, L., Jahanshad, N., Ennis, D. B., Jin, Y., Bernstein, M. A., Borowski, B. J., Jack, C. R., Toga, A. W., Leow, A. D., & Thompson, P. M.
- Affiliations
- University of California, Los Angeles (US)
- Department of Radiology; Mayo Clinic; Rochester; Minnesota
- Mayo Clinic (US)
- University of Illinois Urbana-Champaign (US)
- ORCID
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2012-04-21
- Volume
- 34
- Issue
- 10
- Pages
- 2688-2706
- ISSN
- 1065-9471 (ISSN-L)
- PMC
- PMC3468661
- OpenAlex
- W1568946913
- Cited by
- 52
- References
- 40
- Citations by year
- 2026: 2 · 2025: 2 · 2022: 2 · 2021: 3 · 2020: 2 · 2019: 1 · 2018: 3 · 2017: 2 · 2016: 4 · 2015: 6 · 2014: 11 · 2013: 11 · 2012: 3
- FWCI
- 7.49
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Human Brain Mapping (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute on Aging (RC2AG036535)
- National Institute of Biomedical Imaging and Bioengineering (R01EB008432, R01EB008281, R01EB007813, EB008281, EB 007813, EB008432, P41EB015922)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (HD 050735, R01HD050735)
- 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
- diffusion-weighted imaging, angular resolution, spatial resolution, resolution trade-off, fractional anisotropy, mean diffusivity, orientation distribution function, signal-to-noise ratio, test-retest stability, b-value, brain connectivity mapping
- MeSH terms
- Adult, Algorithms, Brain Mapping, Computer Simulation, Female, Humans, Image Processing, Computer-Assisted, Male, Models, Neurological, Nerve Fibers, Reference Values, Research Design, Time Factors, Reproducibility of Results, Artifacts, Echo-Planar Imaging, Phantoms, Imaging, Head Movements, Diffusion Magnetic Resonance Imaging, Signal-To-Noise Ratio
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