Complex Permittivity of Ex-Vivo Human, Bovine and Porcine Brain Tissues in the Microwave Frequency Range

Matković, Anđela; Kordić, Anton; Jakovčević, Antonia; Šarolić, Antonio

doi:10.3390/diagnostics12112580

Cited by 5 publications

(11 citation statements)

References 45 publications

(70 reference statements)

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“…The measured data can be fit by using a linear model as: both fitting models had a R 2 of 0.99. For the mixture with only water, a concentration of 5% sodium has a conductivity of 8 S/m; however, in the case of human tissues, the higher value of conductivity that has been reported is in the CSF and is approximately 2.2 S/m 12 , 13 , which corresponds to approximately 1.8% of sodium for the case of the water-sodium mixture. The plots of the EP values from the single mixture phantoms help us understand the relationship and correlation.…”

Section: Resultsmentioning

confidence: 82%

Use of machine learning to improve the estimation of conductivity and permittivity based on longitudinal relaxation time T1 in magnetic resonance at 7 T

Hernández

Kim

2023

Sci Rep

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Electrical property tomography (EPT) is a noninvasive method that uses magnetic resonance imaging (MRI) to estimate the conductivity and permittivity of tissues, and hence, can be used as a biomarker. One branch of EPT is based on the correlation of water and relaxation time T1 with the conductivity and permittivity of tissues. This correlation was applied to a curve-fitting function to estimate electrical properties, it was found to have a high correlation between permittivity and T1 however the computation of conductivity based on T1 requires to estimate the water content. In this study, we developed multiple phantoms with several ingredients that modify the conductivity and permittivity and explored the use of machine learning algorithms to have a direct estimation of conductivity and permittivity based on MR images and the relaxation time T1. To train the algorithms, each phantom was measured using a dielectric measurement device to acquire the true conductivity and permittivity. MR images were taken for each phantom, and the T1 values were measured. Then, the acquired data were tested using curve fitting, regression learning, and neural fit models to estimate the conductivity and permittivity values based on the T1 values. In particular, the regression learning algorithm based on Gaussian process regression showed high accuracy with a coefficient of determination R2 of 0.96 and 0.99 for permittivity and conductivity, respectively. The estimation of permittivity using regression learning demonstrated a lower mean error of 0.66% compared to the curve fitting method, which resulted in a mean error of 3.6%. The estimation of conductivity also showed that the regression learning approach had a lower mean error of 0.49%, whereas the curve fitting method resulted in a mean error of 6%. The findings suggest that utilizing regression learning models, specifically Gaussian process regression, can result in more accurate estimations for both permittivity and conductivity compared to other methods.

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Section: Resultsmentioning

confidence: 82%

Use of machine learning to improve the estimation of conductivity and permittivity based on longitudinal relaxation time T1 in magnetic resonance at 7 T

Hernández

Kim

2023

Sci Rep

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“…However, cell morphology and density vary between mammalian species proportionally to differences in brain size (Herculano-Houzel, Manger et al 2014). While bovine, ovine, and porcine dielectric values are similar to human dielectric values (Matković, Kordić et al 2022), rodent values are likely to differ. Detailed recordings of rodent tissue dielectric values at various stimulation frequencies are not currently available and would improve accuracy and better guide translational modelling efforts.…”

Section: Discussionmentioning

confidence: 97%

Translational modelling of low and medium intensity transcranial magnetic stimulation from rodents to humans

Bolland,

Goryachev,

Opitz

et al. 2024

Preprint

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Background: Rodent models using subthreshold intensities of transcranial magnetic stimulation (TMS) have provided insight into the biological mechanisms of TMS but often differ from human studies in the intensity of the electric field (E-field) induced in the brain. Objective: To develop a finite element method model as a guide for translation between low and medium intensity TMS rodent studies and high intensity TMS studies in humans. Methods: FEM models using three head models (mouse, rat, and human), and eight TMS coils were developed to simulate the magnetic flux density (B-field) and E-field values induced by three intensities. Results: In the mouse brain, maximum B-fields ranged from 0.00675 T to 0.936 T and maximum E-field of 0.231 V/m to 60.40 V/m E-field. In the rat brains maximum B-fields ranged from of 0.00696 T to 0.567 T and maximum E-fields of 0.144 V/m to 97.2 V/m. In the human brain, the S90 Standard coil could be used to induce a maximum B-field of 0.643 T and E-field of 241 V/m, while the MC-B70 coil induced 0.564 T B-field and 220 V/m E-field. Conclusions: We have developed a novel FEM modelling tool that can help guide the replication of rodent studies using low intensity E-fields to human studies using commercial TMS coils. Modelling limitations include lack of data on dielectric values and CSF volumes for rodents and simplification of tissue geometry impacting E-field distribution, methods for mitigating these issues are discussed. A range of additional cross-species factors affecting the translation of E-fields were identified that will aid TMS E-field modelling in both humans and rodents. We present data that describes to what extent translation of brain region-specific E-field values from rodents to humans is possible and detail requirements for future improvement. A graphical abstract of the translational modelling pipeline from this study is provided below (Figure A.1).

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“…The dielectric spectroscopy, i.e., the complex permittivity measurement, was performed using the standardized open-ended coaxial probe method, which is described in detail in our previous studies [ 44 , 46 , 47 ]. It is a non-invasive method (at the sample level), where an open-ended coaxial probe is put in contact with the sample acting as the material under test (MUT) ( Figure 1 ).…”

Section: Methodsmentioning

confidence: 99%

“…The name Slim Form Probe indicates that its diameter of only 2.2 mm is smaller than most other commercial probes. Accordingly, it is a common choice for biological tissue measurements [ 43 , 44 ] as it allows to differentiate between the tissues in a heterogeneous sample, e.g., to separately measure the white matter or the gray matter on a brain slice.…”

Section: Methodsmentioning

confidence: 99%

“…On the other hand, the microwave frequency range is of primary interest because it is the working range of slim form probes, whose narrow diameter allows probing the tissue with a space resolution required to discriminate between tissues [ 43 ]. Actually, in the microwave frequency range, the data on the brain tissue permittivity is dated and scarce, especially when it comes to human tissues [ 44 ], while the data on the permittivity of tumors in human CNS is practically non-existent.…”

Section: Introductionmentioning

confidence: 99%

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Dielectric Spectroscopy Shows a Permittivity Contrast between Meningioma Tissue and Brain White and Gray Matter—A Potential Physical Biomarker for Meningioma Discrimination

Kordić,

Šarolić

2023

Cancers

Self Cite

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The effectiveness of surgical resection of meningioma, the most common primary CNS tumor, depends on the capability to intraoperatively discriminate between the meningioma tissue and the surrounding brain white and gray matter tissues. Aiming to find a potential biomarker based on tissue permittivity, dielectric spectroscopy of meningioma, white matter, and gray matter ex vivo tissues was performed using the open-ended coaxial probe method in the microwave frequency range from 0.5 to 18 GHz. The averages and the 95% confidence intervals of the measured permittivity for each tissue were compared. The results showed the absence of overlap between the 95% confidence intervals for meningioma tissue and for brain white and gray matter, indicating a significant difference in average permittivity (p ≤ 0.05) throughout almost the entire measured frequency range, with the most pronounced contrast found between 2 GHz and 5 GHz. The discovered contrast is relevant as a potential physical biomarker to discriminate meningioma tissue from the surrounding brain tissues by means of permittivity measurement, e.g., for intraoperative meningioma margin assessment. The permittivity models for each tissue, developed in this study as its byproducts, will allow more accurate electromagnetic modeling of brain tumor and healthy tissues, facilitating the development of new microwave-based medical devices and tools.

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Complex Permittivity of Ex-Vivo Human, Bovine and Porcine Brain Tissues in the Microwave Frequency Range

Cited by 5 publications

References 45 publications

Use of machine learning to improve the estimation of conductivity and permittivity based on longitudinal relaxation time T1 in magnetic resonance at 7 T

Use of machine learning to improve the estimation of conductivity and permittivity based on longitudinal relaxation time T1 in magnetic resonance at 7 T

Translational modelling of low and medium intensity transcranial magnetic stimulation from rodents to humans

Dielectric Spectroscopy Shows a Permittivity Contrast between Meningioma Tissue and Brain White and Gray Matter—A Potential Physical Biomarker for Meningioma Discrimination

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