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Toward a quantitative assessment of diffusion anisotropy
Abstract
Indices of diffusion anisotropy calculated from diffusion coefficients acquired in two or three perpendicular directions are rotationally variant. In living monkey brain, these indices severely underestimate the degree of diffusion anisotropy. New indices calculated from the entire diffusion tensor are rotationally invariant (RI). They show that anisotropy is highly variable in different white matter regions depending on the degree of coherence of fiber tract directions. In structures with a regular, parallel fiber arrangement, water diffusivity in the direction parallel to the fibers (D| ≈ 1400–1800 × 10−6 mm 2/s) is almost 10 times higher than the average diffusivity in directions perpendicular to them ((D + D⊥′)/2 ≈ 150–300 × 10−6 mm 2/s), and is almost three times higher than previously reported. In structures where the fiber pattern is less coherent (e.g., where fiber bundles merge), diffusion anisotropy is significantly reduced. However, RI anisotropy indices are still susceptible to noise contamination. Monte Carlo simulations show that these indices are statistically biased, particularly those requiring sorting of the eigenvalues of the diffusion tensor based on their magnitude. A new intervoxel anisotropy index is proposed that locally averages inner products between diffusion tensors in neighboring voxels. This “lattice” RI index has an acceptably low error variance and is less susceptible to bias than any other RI anisotropy index proposed to date.
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DTI analyses and clinical applications in Alzheimer’s disease
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Self-citationFrontal connections and cognitive changes in normal aging rhesus monkeys: A DTI study.
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The sensitivity of diffusion MRI to microstructural properties and experimental factors
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Advances in microstructural diffusion neuroimaging for psychiatric disorders
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Quantitative assessment of diffusional kurtosis anisotropy
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Metadata
- Authors
- Pierpaoli, C., & Basser, P. J.
- Venue
- Magnetic Resonance in Medicine
- Publisher
- Wiley
- Published
- 1996-12
- Volume
- 36
- Issue
- 6
- Pages
- 893-906
- ISSN
- 0740-3194 (ISSN-L)
- 1522-2594
- OpenAlex
- W2157035009
- Cited by
- 2,405
- References
- 2
- FWCI
- 27.59
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.