Stochastic dosimetry of a three compartment head model

Šušnjara, Anna; Dodig, Hrvoje; Cvetković, Mario; Poljak, Dragan

doi:10.1016/j.enganabound.2020.04.010

Cited by 14 publications

(7 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…is integral is evaluated numerically. In this paper, the numerical integration is done according to the Clenshaw-Curtis integration rule [16].…”

Section: Stochastic Collocationmentioning

confidence: 99%

“…Since purely stochastic modelling is either time consuming or impossible to obtain, the hybrid approach in terms of stochastic-deterministic modelling in HF dosimetry has been readily used for a decade [13]. Many examples of stochastic-deterministic modelling in internal EM and thermal dosimetry can be found in, e.g., [11,[14][15][16][17]. However, extensive stochastic dosimetry of incident fields, i.e., the radiated fields, is still scarce in the literature.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stochastic-Deterministic Assessment of Electric Field Radiated by Base Station Antenna above a Two-Layered Ground

Galić¹,

Šušnjara

Poljak

2022

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

The paper deals with stochastic-deterministic modelling of radiated electric field by base station antennas (BSAs) operating in GSM frequency range. Within the framework of deterministic analysis, the total electric field above a two-layered lossy ground is obtained by considering the incident and reflected ray in the far field zone. The influence of nonhomogeneous lower medium is taken into account via two approaches: Fresnel plane wave reflection coefficient (FRM) and simplified reflection coefficient stemming from Modified Image Theory (MIT). Antenna height, relative conductivity, and permittivity of each ground layer, as well as the thickness of the upper ground layer, are considered as input parameters with inherent uncertainty. To quantify the uncertainty of output electric field, deterministic models are treated as black boxes by two stochastic methods, Monte Carlo (MC) and Stochastic Collocation (SC), respectively. Stochastic mean and standard deviation of the output are computed, and sensitivity analysis is carried out in order to analyze the impact of input parameters’ variations on the resulting electric field variance. The presented results expose weakness and strength of the two stochastic methods, particularly identifying the cases when the preferred SC method fails to converge. Furthermore, sensitivity analysis reveals that despite the smallest variation around its respective average value, the antenna height has the highest impact on the output variance at observation points in the vicinity of the antenna. However, as the distance from the antenna increases, the 1st layer depth and its relative electric permittivity become the most significant parameters.

show abstract

“…is integral is evaluated numerically. In this paper, the numerical integration is done according to the Clenshaw-Curtis integration rule [16].…”

Section: Stochastic Collocationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stochastic-Deterministic Assessment of Electric Field Radiated by Base Station Antenna above a Two-Layered Ground

Galić¹,

Šušnjara

Poljak

2022

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…is model represents the increase in the level of detail, compared to the homogeneous one. e 3-compartment (3-shell) model is the realistic head representation routinely used in experimental magnetoencephalography (MEG) [20] and recently in deterministic-stochastic EM dosimetry [21].…”

Section: Human Eye and Head Modelsmentioning

confidence: 99%

“…In case of a multitissue models, the additional tissues surrounding the brain act as the additional insulating layer. e analysis of parameter variation in the three-compartment head model carried out by the stochastic collocation method [21] found that the brain parameters has the most significant influence on fields induced inside the brain. e homogeneous model, on the other hand, uses the concept of the effective surface heat transfer coefficient to take this into account [30].…”

Section: Temperature Increase In Different Human Brain Modelsmentioning

confidence: 99%

On the Use of Compound and Extracted Models in Thermal Dosimetry Assessment

Cvetković

Dodig

Poljak

2020

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

This paper deals with thermal analysis of realistic models of the human eye and brain using the finite element method. The research presented in this paper is the sequel to the electromagnetic dosimetry model presented in the previous work by the authors. The paper presents the numerical results for the specific absorption rate (SAR) and the related temperature increase in various models of the human eye and the brain/head exposed to high-frequency (HF) electromagnetic (EM) radiation. Based on the numerical results for the induced electric field, the distribution of SAR in the human brain and human eye is determined, subsequently used as input to the thermal model. The thermal dosimetry model of both the brain and eye are based on the form of Pennes’ bioheat transfer equation, numerically solved using the finite element method (FEM). The comparison between the extracted models and the compound models of both the eye and brain, placed inside the realistic head model is presented. In case of the human eye, generally, comparable results were obtained for both SAR and temperature increase, while the compound eye model is found to be more suitable when the polarization of incident wave is considered. Moreover, the extracted eye model underestimated the temperature rise, attributed to better heat exchange than the compound model. The results for the compound eye indicate that in some situations, the eye lens could be omitted from simulation, facilitating the model preparation. The numerical results for all three brain models showed similar distributions of SAR and temperature rise. Also, the obtained results show that the peak SAR does not exceed the basic restriction limit for localized SAR, for occupational exposure. The thermal dosimetry assessment of the human brain exposed in four considered scenarios indicates the temperature should not exceed 0.1°C. Finally, the use of a geometrically simplified model may also be found useful in the initial dosimetry assessment prior to dealing with models with more anatomical features.

show abstract

“…In this regime, relative permittivity is also needed for full-wave analysis. In addition, the standardization of human protection from electromagnetic fields considers the inter-subject variability, which in general considered in the reduction factor of the limit, and thus, the dosimetric evaluation of different human models has become essential (Liu et al 2019, Susnjara et al 2018, Li et al 2018.…”

Section: Introductionmentioning

confidence: 99%

Learning-based estimation of dielectric properties and tissue density in head models for personalized radio-frequency dosimetry

2020

View full text Add to dashboard Cite

Radio-frequency dosimetry is an important process in assessments for human exposure safety and for compliance of related products. Recently, computational human models generated from medical images have often been used for such assessment, especially to consider the inter-subject variability. However, a common procedure to develop personalized models is time consuming because it involves excessive segmentation of several components that represent different biological tissues, which is a major obstacle in the inter-subject variability assessment of radiation safety. Deep learning methods have been shown to be a powerful approach for pattern recognition and signal analysis. Convolutional neural networks with deep architecture are proven robust for feature extraction and image mapping in several biomedical applications. In this study, we develop a learning-based approach for fast and accurate estimation of the dielectric properties and density of tissues directly from magnetic resonance images in a single shot. The smooth distribution of the dielectric properties in head models, which is realized using a process without tissue segmentation, improves the smoothness of the specific absorption rate (SAR) distribution compared with that in the commonly used procedure. The estimated SAR distributions, as well as that averaged over 10-g of tissue in a cubic shape, are found to be highly consistent with those computed using the conventional methods that employ segmentation.

show abstract

Stochastic dosimetry of a three compartment head model

Cited by 14 publications

References 31 publications

Stochastic-Deterministic Assessment of Electric Field Radiated by Base Station Antenna above a Two-Layered Ground

Stochastic-Deterministic Assessment of Electric Field Radiated by Base Station Antenna above a Two-Layered Ground

On the Use of Compound and Extracted Models in Thermal Dosimetry Assessment

Learning-based estimation of dielectric properties and tissue density in head models for personalized radio-frequency dosimetry

Contact Info

Product

Resources

About