Evidence map›Paper›PMID 35464316›Full record

ArticleFrontiers in neuroscience2022

Enabling Complex Fibre Geometries Using 3D Printed Axon-Mimetic Phantoms.

Tristan K Kuehn, Farah N Mushtaha, Ali R Khan, Corey A Baron

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Tristan K KuehnCentre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, ON, Canada.
Farah N MushtahaSchool of Biomedical Engineering, Western University, London, ON, Canada.
Ali R KhanCentre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, ON, Canada.
Corey A BaronCentre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To introduce a method to create 3D-printed axon-mimetic phantoms with complex fibre orientations to characterise the performance of diffusion magnetic resonance imaging (MRI) models and representations in the presence of orientation dispersion. Methods: An extension to an open-source 3D printing package was created to produce a set of five 3D-printed axon-mimetic (3AM) phantoms with various combinations of bending and crossing fibre orientations. A two-shell diffusion MRI scan of the five phantoms in water was performed at 9.4T. Diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), the ball and stick model, neurite orientation density and dispersion imaging (NODDI), and Bingham-NODDI were all fit to the resulting diffusion MRI data. A ground truth map of that phantom's crossing angles and/or arc radius was registered to the diffusion-weighted images. Metrics from each model and representation were compared to the ground-truth maps, and a quadratic regression model was fit to each combination of output metric and ground-truth metric. Results: The mean diffusivity (MD) metric defined by DTI was insensitive to crossing angle but increased with fibre curvature. Axial diffusivity (AD) decreased with increasing crossing angle. DKI's diffusivity metrics replicated the trends seen in DTI, and its mean kurtosis (MK) metric decreased with fibre curvature, except in regions with high crossing angles. The estimated stick volume fraction in the ball and stick model decreased with increasing fibre curvature and crossing angle. NODDI's intra-neurite volume fraction was insensitive to crossing angle, and its orientation dispersion index (ODI) was correlated to crossing angle. Bingham-NODDI's intra-neurite volume fraction was also insensitive to crossing angle, while its primary ODI (ODI Conclusion: Inexpensive 3D-printed axon-mimetic phantoms can be used to investigate the effect of fibre curvature and crossings on diffusion MRI representations and models of diffusion signal. The dependence of several representations and models on fibre dispersion/crossing was investigated. As expected, Bingham-NODDI was best able to characterise planar fibre dispersion in the phantoms.

Indexed as

3D printingaxonsdiffusion MRImodellingphantomsrepresentationswhite matter

Identifiers

PMID35464316
PMCPMC9023071

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.