Comparison of Five Conductivity Tensor Models and Image Reconstruction Methods Using MRI.

Publisher: MDPI Country of Publication: Switzerland NLM ID: 100964009 Publication Model: Electronic Cited Medium: Internet ISSN: 1420-3049 (Electronic) Linking ISSN: 14203049 NLM ISO Abbreviation: Molecules

Original Publication: Basel, Switzerland : MDPI, c1995-

Electric Conductivity* ; Brain*/diagnostic imaging ; Phantoms, Imaging* ; Image Processing, Computer-Assisted*/methods ; Magnetic Resonance Imaging*/methods ; Diffusion Tensor Imaging*/methods; Humans

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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2019R1A2C2088573 National Research Foundation of Korea; 2020R1A2C200790611 National Research Foundation of Korea; 2021R1A2C2004299 National Research Foundation of Korea; HI18C2435 Ministry of Health and Welfare of Korea; 50461-2021 Korea Institute of Radiological and Medical Sciences

Keywords: anisotropy; conductivity tensor imaging (CTI); diffusion tensor imaging (DTI); electrical conductivity; magnetic resonance imaging (MRI)

Date Created: 20210928 Date Completed: 20240724 Latest Revision: 20240724

20240726

PMC8467711

10.3390/molecules26185499

34576970