Comparison of modelled diffusion-derived electrical conductivities found using magnetic resonance imaging Article Swipe
Sasha Hakhu
,
Leland Hu
,
Scott C. Beeman
,
Rosalind Sadleir
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fradi.2025.1492479
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fradi.2025.1492479
Introduction Magnetic resonance-based electrical conductivity imaging offers a promising new contrast mechanism to enhance disease diagnosis. Conductivity tensor imaging (CTI) combines data from MR diffusion microstructure imaging to reconstruct electrodeless low-frequency conductivity images. However, different microstructure imaging methods rely on varying diffusion models and parameters, leading to divergent tissue conductivity estimates. This study investigates the variability in conductivity predictions across different microstructure models and evaluates their alignment with experimental observations. Methods We used publicly available diffusion databases from neurotypical adults to extract microstructure parameters for three diffusion-based brain models: Neurite Orientation Dispersion and Density Imaging (NODDI), Soma and Neurite Density Imaging (SANDI), and Spherical Mean technique (SMT) conductivity predictions were calculated for gray matter (GM) and white matter (WM) tissues using each model. Comparative analyses were performed to assess the range of predicted conductivities and the consistency between bilateral tissue conductivities for each method. Results Significant variability in conductivity estimates was observed across the three models. Each method predicted distinct conductivity values for GM and WM tissues, with notable differences in the range of conductivities observed for specific tissue examples. Despite the variability, many WM and GM tissues exhibited symmetric bilateral conductivities within each microstructure model. SMT yielded conductivity estimates closer to values reported in experimental studies, while none of the methods aligned with spectroscopic models of tissue conductivity. Discussion and conclusion Our findings highlight substantial discrepancies in tissue conductivity estimates generated by different diffusion models, underscoring the challenge of selecting an appropriate model for low-frequency electrical conductivity imaging. SMT demonstrated better alignment with experimental results. However other microstructure models may produce better tissue discrimination.
Related Topics
Concepts
Diffusion MRI
Conductivity
Microstructure
Materials science
Nuclear magnetic resonance
Magnetic resonance imaging
Diffusion
White matter
Electrical resistivity and conductivity
Fractional anisotropy
Anisotropy
Biological system
Biomedical engineering
Chemistry
Physics
Medicine
Optics
Thermodynamics
Radiology
Metallurgy
Quantum mechanics
Biology
Physical chemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fradi.2025.1492479
- OA Status
- gold
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406717556
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4406717556Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fradi.2025.1492479Digital Object Identifier
- Title
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Comparison of modelled diffusion-derived electrical conductivities found using magnetic resonance imagingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-22Full publication date if available
- Authors
-
Sasha Hakhu, Leland Hu, Scott C. Beeman, Rosalind SadleirList of authors in order
- Landing page
-
https://doi.org/10.3389/fradi.2025.1492479Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3389/fradi.2025.1492479Direct OA link when available
- Concepts
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Diffusion MRI, Conductivity, Microstructure, Materials science, Nuclear magnetic resonance, Magnetic resonance imaging, Diffusion, White matter, Electrical resistivity and conductivity, Fractional anisotropy, Anisotropy, Biological system, Biomedical engineering, Chemistry, Physics, Medicine, Optics, Thermodynamics, Radiology, Metallurgy, Quantum mechanics, Biology, Physical chemistryTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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