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Cited by 37Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity StudiesAdvanced MRI Techniques and Applications

Kernel regression estimation of fiber orientation mixtures in diffusion MRI

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Cabeen, Ryan P., Bastin, Mark E., Laidlaw, David H.·2016·NeuroImage
DOI 10.1016/j.neuroimage.2015.11.061PMC PMC4870009
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Abstract

We present and evaluate a method for kernel regression estimation of fiber orientations and associated volume fractions for diffusion MR tractography and population-based atlas construction in clinical imaging studies of brain white matter. This is a model-based image processing technique in which representative fiber models are estimated from collections of component fiber models in model-valued image data. This extends prior work in nonparametric image processing and multi-compartment processing to provide computational tools for image interpolation, smoothing, and fusion with fiber orientation mixtures. In contrast to related work on multi-compartment processing, this approach is based on directional measures of divergence and includes data-adaptive extensions for model selection and bilateral filtering. This is useful for reconstructing complex anatomical features in clinical datasets analyzed with the ball-and-sticks model, and our framework's data-adaptive extensions are potentially useful for general multi-compartment image processing. We experimentally evaluate our approach with both synthetic data from computational phantoms and in vivo clinical data from human subjects. With synthetic data experiments, we evaluate performance based on errors in fiber orientation, volume fraction, compartment count, and tractography-based connectivity. With in vivo data experiments, we first show improved scan-rescan reproducibility and reliability of quantitative fiber bundle metrics, including mean length, volume, streamline count, and mean volume fraction. We then demonstrate the creation of a multi-fiber tractography atlas from a population of 80 human subjects. In comparison to single tensor atlasing, our multi-fiber atlas shows more complete features of known fiber bundles and includes reconstructions of the lateral projections of the corpus callosum and complex fronto-parietal connections of the superior longitudinal fasciculus I, II, and III.

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  • A great many studies clearly distinguish the AF and SLF as distinct entities, both anatomically and functionally.

    “A great many studies clearly distinguish the 2 bundles as distinct entities, both anatomically and functionally (56, 59, 70, 71, 75, 76).”

    From Superior Longitudinal Fasciculus: A Review of the Anatomical Descriptions With Functional Correlates (2022) · cites this paper as [59]

    The Results (§5.5; page unavailable) describe the arcuate fasciculus and three portions of the superior longitudinal fasciculus separately, supporting an anatomical distinction. The paper does not address whether they are functionally distinct or establish that a great many studies distinguish them on both grounds. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Cabeen, R. P., Bastin, M. E., & Laidlaw, D. H.
Affiliations
  • Brown University (US)
  • University of Edinburgh (GB)
Corresponding author
Cabeen, Ryan P.
ORCID
Venue
NeuroImage
Publisher
Elsevier BV
Published
2016-02
Volume
127
Pages
158-172
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/j.neuroimage.2015.11.061
PMC
PMC4870009
OpenAlex
W2191069827
Cited by
37
References
6
Citations by year
2025: 1 · 2024: 1 · 2023: 2 · 2022: 4 · 2021: 5 · 2020: 6 · 2019: 4 · 2018: 5 · 2017: 5 · 2016: 4
FWCI
3.78
Type
journal article
Language
en
Open access
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • NeuroImage (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by-nc-nd)
Funders
  • Brown Institute for Brain Science Graduate Research
  • NIH/NINDS (R01 NS052470)
  • NIH/NIMH (R01 MH085604)
  • NIH (R01 EB004155)
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
kernel regression, fiber orientation estimation, diffusion MRI, tractography, white matter, fiber volume fraction, model selection, bilateral filter, corpus callosum, superior longitudinal fasciculus
MeSH terms
Adult, Aged, Algorithms, Anatomy, Artistic, Atlases as Topic, Brain, Brain Mapping, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Models, Neurological, Neural Pathways, Diffusion Tensor Imaging, White Matter

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