Enabling Complex Fibre Geometries Using 3D Printed Axon-Mimetic Phantoms
PurposeTo 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.MethodsAn extension to an open-source 3D printing...
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| Format: | Article |
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Frontiers Media S.A.
2022-04-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.833209/full |
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| author | Tristan K. Kuehn Tristan K. Kuehn Farah N. Mushtaha Ali R. Khan Ali R. Khan Ali R. Khan Ali R. Khan Ali R. Khan Corey A. Baron Corey A. Baron Corey A. Baron Corey A. Baron |
| author_facet | Tristan K. Kuehn Tristan K. Kuehn Farah N. Mushtaha Ali R. Khan Ali R. Khan Ali R. Khan Ali R. Khan Ali R. Khan Corey A. Baron Corey A. Baron Corey A. Baron Corey A. Baron |
| author_sort | Tristan K. Kuehn |
| collection | DOAJ |
| description | PurposeTo 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.MethodsAn 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.ResultsThe 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 (ODIP) was also correlated to crossing angle and its secondary ODI (ODIS) was insensitive to crossing angle. For both NODDI models, the volume fractions of the extra-neurite and CSF compartments had low reliability with no clear relationship to crossing angle.ConclusionInexpensive 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. |
| first_indexed | 2024-12-13T22:35:45Z |
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| id | doaj.art-9460af0fbd894ab5a2f4fb79d7369535 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-13T22:35:45Z |
| publishDate | 2022-04-01 |
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| series | Frontiers in Neuroscience |
| spelling | doaj.art-9460af0fbd894ab5a2f4fb79d73695352022-12-21T23:28:59ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-04-011610.3389/fnins.2022.833209833209Enabling Complex Fibre Geometries Using 3D Printed Axon-Mimetic PhantomsTristan K. Kuehn0Tristan K. Kuehn1Farah N. Mushtaha2Ali R. Khan3Ali R. Khan4Ali R. Khan5Ali R. Khan6Ali R. Khan7Corey A. Baron8Corey A. Baron9Corey A. Baron10Corey A. Baron11Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, ON, CanadaSchool of Biomedical Engineering, Western University, London, ON, CanadaSchool of Biomedical Engineering, Western University, London, ON, CanadaCentre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, ON, CanadaSchool of Biomedical Engineering, Western University, London, ON, CanadaDepartment of Biology, Western University, London, ON, CanadaRobarts Research Institute, Western University, London, ON, CanadaDepartment of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, CanadaCentre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, ON, CanadaSchool of Biomedical Engineering, Western University, London, ON, CanadaRobarts Research Institute, Western University, London, ON, CanadaDepartment of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, CanadaPurposeTo 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.MethodsAn 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.ResultsThe 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 (ODIP) was also correlated to crossing angle and its secondary ODI (ODIS) was insensitive to crossing angle. For both NODDI models, the volume fractions of the extra-neurite and CSF compartments had low reliability with no clear relationship to crossing angle.ConclusionInexpensive 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.https://www.frontiersin.org/articles/10.3389/fnins.2022.833209/fulldiffusion MRIphantomsmodelling3D printingrepresentationswhite matter |
| spellingShingle | Tristan K. Kuehn Tristan K. Kuehn Farah N. Mushtaha Ali R. Khan Ali R. Khan Ali R. Khan Ali R. Khan Ali R. Khan Corey A. Baron Corey A. Baron Corey A. Baron Corey A. Baron Enabling Complex Fibre Geometries Using 3D Printed Axon-Mimetic Phantoms Frontiers in Neuroscience diffusion MRI phantoms modelling 3D printing representations white matter |
| title | Enabling Complex Fibre Geometries Using 3D Printed Axon-Mimetic Phantoms |
| title_full | Enabling Complex Fibre Geometries Using 3D Printed Axon-Mimetic Phantoms |
| title_fullStr | Enabling Complex Fibre Geometries Using 3D Printed Axon-Mimetic Phantoms |
| title_full_unstemmed | Enabling Complex Fibre Geometries Using 3D Printed Axon-Mimetic Phantoms |
| title_short | Enabling Complex Fibre Geometries Using 3D Printed Axon-Mimetic Phantoms |
| title_sort | enabling complex fibre geometries using 3d printed axon mimetic phantoms |
| topic | diffusion MRI phantoms modelling 3D printing representations white matter |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.833209/full |
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