Comparison of Five Conductivity Tensor Models and Image Reconstruction Methods Using MRI Article Swipe
Nitish Katoch
,
Bup-Kyung Choi
,
Ji Ae Park
,
In-Ok Ko
,
Hyung Joong Kim
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/molecules26185499
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/molecules26185499
Imaging of the electrical conductivity distribution inside the human body has been investigated for numerous clinical applications. The conductivity tensors of biological tissue have been obtained from water diffusion tensors by applying several models, which may not cover the entire phenomenon. Recently, a new conductivity tensor imaging (CTI) method was developed through a combination of B1 mapping, and multi-b diffusion weighted imaging. In this study, we compared the most recent CTI method with the four existing models of conductivity tensors reconstruction. Two conductivity phantoms were designed to evaluate the accuracy of the models. Applied to five human brains, the conductivity tensors using the four existing models and CTI were imaged and compared with the values from the literature. The conductivity image of the phantoms by the CTI method showed relative errors between 1.10% and 5.26%. The images by the four models using DTI could not measure the effects of different ion concentrations subsequently due to prior information of the mean conductivity values. The conductivity tensor images obtained from five human brains through the CTI method were comparable to previously reported literature values. The images by the four methods using DTI were highly correlated with the diffusion tensor images, showing a coefficient of determination (R2) value of 0.65 to 1.00. However, the images by the CTI method were less correlated with the diffusion tensor images and exhibited an averaged R2 value of 0.51. The CTI method could handle the effects of different ion concentrations as well as mobilities and extracellular volume fractions by collecting and processing additional B1 map data. It is necessary to select an application-specific model taking into account the pros and cons of each model. Future studies are essential to confirm the usefulness of these conductivity tensor imaging methods in clinical applications, such as tumor characterization, EEG source imaging, and treatment planning for electrical stimulation.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/molecules26185499
- https://www.mdpi.com/1420-3049/26/18/5499/pdf?version=1631269596
- OA Status
- gold
- Cited By
- 5
- References
- 56
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3200735613
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3200735613Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/molecules26185499Digital Object Identifier
- Title
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Comparison of Five Conductivity Tensor Models and Image Reconstruction Methods Using MRIWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-09-10Full publication date if available
- Authors
-
Nitish Katoch, Bup-Kyung Choi, Ji Ae Park, In-Ok Ko, Hyung Joong KimList of authors in order
- Landing page
-
https://doi.org/10.3390/molecules26185499Publisher landing page
- PDF URL
-
https://www.mdpi.com/1420-3049/26/18/5499/pdf?version=1631269596Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1420-3049/26/18/5499/pdf?version=1631269596Direct OA link when available
- Concepts
-
Diffusion MRI, Conductivity, Diffusion, Tensor (intrinsic definition), Materials science, Biomedical engineering, Mathematics, Chemistry, Physics, Magnetic resonance imaging, Radiology, Geometry, Medicine, Thermodynamics, Physical chemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
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2023: 2, 2022: 3Per-year citation counts (last 5 years)
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56Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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