Pub Date : 2024-08-22DOI: 10.1109/JERM.2024.3442073
{"title":"IEEE Journal of Electromagnetics, RF, and Microwaves in Medicine and Biology About this Journal","authors":"","doi":"10.1109/JERM.2024.3442073","DOIUrl":"https://doi.org/10.1109/JERM.2024.3442073","url":null,"abstract":"","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643730","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-22DOI: 10.1109/JERM.2024.3442071
{"title":"IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology Publication Information","authors":"","doi":"10.1109/JERM.2024.3442071","DOIUrl":"https://doi.org/10.1109/JERM.2024.3442071","url":null,"abstract":"","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643733","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Malignant melanoma, the aggressive form of skin cancer, progresses via radial and vertical growth. The aim of this study is to assess the feasibility of microwave-based diagnosis of melanoma at different stages of tumor progression. To this end, we used the physiological data for melanoma progression to develop a theoretical model of melanoma growth, followed by the oil-in-gelatin based tissue phantoms, which aim to mimic the dielectric behavior of the tissues under consideration. The phantoms are then dielectrically characterized using a slim-form open-ended coaxial probe by systematically sampling dielectric values across the mimicked skin surfaces at a range of points over the 0.5 – 26.5 GHz frequency range. The resulting observations revealed that the microwave spectroscopy exhibits the capability not only to distinguish between healthy and malignant skin, but also differentiate between tumors at different stages of vertical growth, which may not be visually discernible from the skin surface. The measured results are compared with the estimated dielectric values of malignant melanoma using Lichteneker's mixing equation obtained from the literature and it was observed that the measured results closely agree with the literature values.
{"title":"Models of Melanoma Growth for Assessment of Microwave-Based Diagnostic Tools","authors":"Jasmine Boparai;Rachel Tchinov;Oliver Miller;Yanis Jallouli;Milica Popović","doi":"10.1109/JERM.2024.3430315","DOIUrl":"https://doi.org/10.1109/JERM.2024.3430315","url":null,"abstract":"Malignant melanoma, the aggressive form of skin cancer, progresses via radial and vertical growth. The aim of this study is to assess the feasibility of microwave-based diagnosis of melanoma at different stages of tumor progression. To this end, we used the physiological data for melanoma progression to develop a theoretical model of melanoma growth, followed by the oil-in-gelatin based tissue phantoms, which aim to mimic the dielectric behavior of the tissues under consideration. The phantoms are then dielectrically characterized using a slim-form open-ended coaxial probe by systematically sampling dielectric values across the mimicked skin surfaces at a range of points over the 0.5 – 26.5 GHz frequency range. The resulting observations revealed that the microwave spectroscopy exhibits the capability not only to distinguish between healthy and malignant skin, but also differentiate between tumors at different stages of vertical growth, which may not be visually discernible from the skin surface. The measured results are compared with the estimated dielectric values of malignant melanoma using Lichteneker's mixing equation obtained from the literature and it was observed that the measured results closely agree with the literature values.","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-02DOI: 10.1109/JERM.2024.3418716
Giuseppe Carluccio;Sukhoon Oh;Sangwoo Kim;Donghyuk Kim;Karthik Lakshmanan;Christopher M. Collins
Objectives: Estimation of Specific energy Absorption Rate (SAR) is critical to assess RF safety for devices that rely on the transmission of electromagnetic energy, such as cellphones or MRI coils. SAR generates local heat which can damage human tissues and it is usually estimated through numerical simulations. We describe a method to estimate the SAR distribution in phantoms that is fast and not computationally demanding, based on the evaluation of temperature increase maps. Technology or Method: The presented method relies on the inversion of a previously published method to quickly estimate the temperature increase with the knowledge of the SAR distribution and thermal properties. By reversing the process, we can estimate the SAR from temperature increase maps and material thermal properties. To demonstrate the method, we utilize temperature maps measured with MRI-based thermography and compare the estimated SAR maps with those obtained through electromagnetic simulations. We have performed these comparisons with two datasets, one 2D and one 3D, and we have considered the impact of potential sources of errors such as the acquisition time and discontinuities at the interface air/sample. Results: The method can estimate SAR distribution from experimental temperature increase maps within few seconds, and produces SAR distributions similar to those from simulation of the experimental situation. Clinical or Biological Impact : The method we present can quickly estimate SAR distribution to assess RF safety of radiofrequency devices.
目的：估算比能量吸收率（SAR）对于评估依赖电磁能量传输的设备（如手机或核磁共振成像线圈）的射频安全至关重要。SAR 会产生局部热量，对人体组织造成损害，通常通过数值模拟来估算。我们介绍了一种基于温度升高图评估的方法来估算模型中的 SAR 分布，这种方法速度快，计算要求低。技术或方法：所介绍的方法依赖于对之前公布的一种方法进行反演，从而利用 SAR 分布和热特性知识快速估算温升。通过反向过程，我们可以根据温升图和材料热特性估算出 SAR。为了演示这种方法，我们利用基于核磁共振成像的热成像技术测得的温度图，并将估算的 SAR 图与电磁模拟获得的 SAR 图进行比较。我们用两个数据集（一个是二维数据集，一个是三维数据集）进行了比较，并考虑了潜在误差源（如采集时间和空气/样品界面的不连续性）的影响。结果该方法可在几秒钟内根据实验温度升高图估算出 SAR 分布，并得出与模拟实验情况相似的 SAR 分布。临床或生物学影响：我们提出的方法可以快速估算 SAR 分布，以评估射频设备的射频安全性。
{"title":"A Fast Method to Estimate the SAR Distribution From Temperature Increased Maps","authors":"Giuseppe Carluccio;Sukhoon Oh;Sangwoo Kim;Donghyuk Kim;Karthik Lakshmanan;Christopher M. Collins","doi":"10.1109/JERM.2024.3418716","DOIUrl":"https://doi.org/10.1109/JERM.2024.3418716","url":null,"abstract":"<bold>Objectives:</b>\u0000 Estimation of Specific energy Absorption Rate (SAR) is critical to assess RF safety for devices that rely on the transmission of electromagnetic energy, such as cellphones or MRI coils. SAR generates local heat which can damage human tissues and it is usually estimated through numerical simulations. We describe a method to estimate the SAR distribution in phantoms that is fast and not computationally demanding, based on the evaluation of temperature increase maps. \u0000<bold>Technology or Method:</b>\u0000 The presented method relies on the inversion of a previously published method to quickly estimate the temperature increase with the knowledge of the SAR distribution and thermal properties. By reversing the process, we can estimate the SAR from temperature increase maps and material thermal properties. To demonstrate the method, we utilize temperature maps measured with MRI-based thermography and compare the estimated SAR maps with those obtained through electromagnetic simulations. We have performed these comparisons with two datasets, one 2D and one 3D, and we have considered the impact of potential sources of errors such as the acquisition time and discontinuities at the interface air/sample. \u0000<bold>Results:</b>\u0000 The method can estimate SAR distribution from experimental temperature increase maps within few seconds, and produces SAR distributions similar to those from simulation of the experimental situation. \u0000<bold>Clinical or Biological Impact</b>\u0000: The method we present can quickly estimate SAR distribution to assess RF safety of radiofrequency devices.","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-21DOI: 10.1109/JERM.2024.3414196
Valeria Mariano;Jorge A. Tobon Vasquez;David O. Rodriguez-Duarte;Francesca Vipiana
The contrast source inversion method is an iterative non-linear algorithm, and, in this paper, it works in combination with a finite element method solver for the reconstruction of the dielectric properties' distribution in the head with the aim to diagnose brain stroke. Here, the involved contrast source variables are discretized through a novel field-based discretization that allows a linear variation of the variables, leading to their more accurate description, and therefore to a final dielectric properties' reconstruction closer to the expected scenario. Moreover, we propose a new approach to compute the imaging algorithm initial guess, based on the truncated singular value decomposition technique, that appears more effective in the case of noisy measured data. Finally, the developed algorithm is applied to sets of data, measured with a microwave imaging system to reconstruct brain stroke scenarios.
{"title":"Field-Based Discretization of the 3-D Contrast Source Inversion Method Applied to Brain Stroke Microwave Imaging","authors":"Valeria Mariano;Jorge A. Tobon Vasquez;David O. Rodriguez-Duarte;Francesca Vipiana","doi":"10.1109/JERM.2024.3414196","DOIUrl":"https://doi.org/10.1109/JERM.2024.3414196","url":null,"abstract":"The contrast source inversion method is an iterative non-linear algorithm, and, in this paper, it works in combination with a finite element method solver for the reconstruction of the dielectric properties' distribution in the head with the aim to diagnose brain stroke. Here, the involved contrast source variables are discretized through a novel field-based discretization that allows a linear variation of the variables, leading to their more accurate description, and therefore to a final dielectric properties' reconstruction closer to the expected scenario. Moreover, we propose a new approach to compute the imaging algorithm initial guess, based on the truncated singular value decomposition technique, that appears more effective in the case of noisy measured data. Finally, the developed algorithm is applied to sets of data, measured with a microwave imaging system to reconstruct brain stroke scenarios.","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-20DOI: 10.1109/JERM.2024.3414830
Tiangang Long;Changqing Jiang;Luming Li
Radiofrequency-induced heating represents a significant and intricate challenge during the combined use of magnetic resonance imaging and active implantable medical devices. The coupling of the transfer function (TF) determination process and radiofrequency (RF) exposure experiment is a perennial problem in the field. In this study, the tip electrode was separated from the lead and numerically modeled for analysis. The current induced at the electrode in the TF measurement scenario was estimated by analyzing the electromagnetic (EM) fields near the electrode. The magnitude of TF was calibrated according to the estimated current source. The tip response under RF exposure is independently predicted with an error of less than 10% using the obtained scaled TF in simulation studies. Near-electrode EM fields analysis introduces a novel perspective in RF-induced heating evaluation study.
{"title":"Electromagnetic Modeling of the Implantable Electrode for Transfer Function Calibration in MRI RF-Induced Heating Assessment","authors":"Tiangang Long;Changqing Jiang;Luming Li","doi":"10.1109/JERM.2024.3414830","DOIUrl":"https://doi.org/10.1109/JERM.2024.3414830","url":null,"abstract":"Radiofrequency-induced heating represents a significant and intricate challenge during the combined use of magnetic resonance imaging and active implantable medical devices. The coupling of the transfer function (TF) determination process and radiofrequency (RF) exposure experiment is a perennial problem in the field. In this study, the tip electrode was separated from the lead and numerically modeled for analysis. The current induced at the electrode in the TF measurement scenario was estimated by analyzing the electromagnetic (EM) fields near the electrode. The magnitude of TF was calibrated according to the estimated current source. The tip response under RF exposure is independently predicted with an error of less than 10% using the obtained scaled TF in simulation studies. Near-electrode EM fields analysis introduces a novel perspective in RF-induced heating evaluation study.","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rotator cuff tear (RCT) is one of the most common shoulder injuries, which can be irreparable if it develops to a severe condition. A portable imaging system for the on-site detection of RCT is necessary to identify its extent for early diagnosis. We introduce a microwave tomography system, using state-of-the-art numerical modeling and parallel computing for detection of RCT. The results show that the proposed method is capable of accurately detecting and localizing this injury in different size. In the next step, an efficient design in terms of computing time and complexity is proposed to detect the variations in the injured model with respect to the healthy model. The method is based on finite element discretization and uses parallel preconditioners from the domain decomposition method to accelerate computations. It is implemented using the open source FreeFEM software.
{"title":"Numerical Modeling for Shoulder Injury Detection Using Microwave Imaging","authors":"Sahar Borzooei;Pierre-Henri Tournier;Victorita Dolean;Christian Pichot;Nadine Joachimowicz;Helene Roussel;Claire Migliaccio","doi":"10.1109/JERM.2024.3411799","DOIUrl":"https://doi.org/10.1109/JERM.2024.3411799","url":null,"abstract":"Rotator cuff tear (RCT) is one of the most common shoulder injuries, which can be irreparable if it develops to a severe condition. A portable imaging system for the on-site detection of RCT is necessary to identify its extent for early diagnosis. We introduce a microwave tomography system, using state-of-the-art numerical modeling and parallel computing for detection of RCT. The results show that the proposed method is capable of accurately detecting and localizing this injury in different size. In the next step, an efficient design in terms of computing time and complexity is proposed to detect the variations in the injured model with respect to the healthy model. The method is based on finite element discretization and uses parallel preconditioners from the domain decomposition method to accelerate computations. It is implemented using the open source FreeFEM software.","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10564578","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-03DOI: 10.1109/JERM.2024.3404119
Tomas Pokorny;David Vrba;Ondrej Fiser;Marco Salucci;Jan Vrba
The primary objective of this study is to systematically evaluate the performance of the Support Vector Machine (SVM) algorithm, identifying optimal configurations and appropriate parameters for training and testing data, for microwave brain stroke classification. Using experimentally verified 3D numerical models, a large database of synthetic training and test data has been created with different levels of data variability. These models consist of an antenna array surrounding reconfigurable geometrically and dielectrically realistic human head models Within these models, strokes of varying sizes, types, and dielectric parameters are virtually inserted at different positions in brain within the plane of the antennas. Synthetic data sets have been generated to study the impact of reducing training data, data dimensionality, data format, and algorithm settings. The results of this study confirm that Principal Component Analysis (PCA) dimensionality reduction significantly improved the classification accuracy of the SVM algorithm, and datasets of subjects with smaller strokes appeared to be the most suitable for training. Furthermore, datasets that contain the real and imaginary parts of transmission and reflection coefficients result in the highest classification accuracy. For the current antenna array, the best observed setting and scenarios with high variability in training and test data, close to real clinical scenarios, the ability to accurately classify ischemic strokes and suggest safe initiation of thrombotic therapy is approximately 70%.
{"title":"Systematic Optimization of Training and Setting of SVM-Based Microwave Stroke Classification: Numerical Simulations for 10 Port System","authors":"Tomas Pokorny;David Vrba;Ondrej Fiser;Marco Salucci;Jan Vrba","doi":"10.1109/JERM.2024.3404119","DOIUrl":"https://doi.org/10.1109/JERM.2024.3404119","url":null,"abstract":"The primary objective of this study is to systematically evaluate the performance of the Support Vector Machine (SVM) algorithm, identifying optimal configurations and appropriate parameters for training and testing data, for microwave brain stroke classification. Using experimentally verified 3D numerical models, a large database of synthetic training and test data has been created with different levels of data variability. These models consist of an antenna array surrounding reconfigurable geometrically and dielectrically realistic human head models Within these models, strokes of varying sizes, types, and dielectric parameters are virtually inserted at different positions in brain within the plane of the antennas. Synthetic data sets have been generated to study the impact of reducing training data, data dimensionality, data format, and algorithm settings. The results of this study confirm that Principal Component Analysis (PCA) dimensionality reduction significantly improved the classification accuracy of the SVM algorithm, and datasets of subjects with smaller strokes appeared to be the most suitable for training. Furthermore, datasets that contain the real and imaginary parts of transmission and reflection coefficients result in the highest classification accuracy. For the current antenna array, the best observed setting and scenarios with high variability in training and test data, close to real clinical scenarios, the ability to accurately classify ischemic strokes and suggest safe initiation of thrombotic therapy is approximately 70%.","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10546281","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-22DOI: 10.1109/JERM.2024.3378977
Chao Ma;Quan Shi;Bing Hua;Yongwei Zhang;Zhihuo Xu;Liu Chu;Robin Braun;Jiajia Shi
Software-defined radio (SDR) can be used to detect human respiratory and heartbeat signals with the merits of low costs, high flexibility, and fast implementation. This paper proposes a human respiratory heartbeat detection system based on SDR micro-Doppler radar. The system can adjust radar parameters in real-time according to the detection environment, breaking the hardware limitations of traditional radar. Data pre-processing is performed on the transmit and receive baseband signals to obtain a composite signal containing human respiratory and heartbeat signals. In addressing the difficulty of detecting heartbeat signals compared to respiratory signals, an adaptive heartbeat signal enhancement detection algorithm named the one-time differential weighted step-size normalized least mean square (ODWS-NLMS) is proposed. This algorithm enhances the step size through weighted improvements utilizing the first-order differential characteristics of composite signals. Experiments were conducted in three distinct real-world environments, and the results indicate that the proposed algorithm outperforms discrete wavelet transform (DWT) and ensemble empirical mode decomposition (EEMD) in terms of average accuracy, root mean square error (RMSE), and signal-to-noise ratio (SNR).
{"title":"Noncontact Heartbeat and Respiratory Signal Separation Using a Sub 6 GHz SDR Micro-Doppler Radar","authors":"Chao Ma;Quan Shi;Bing Hua;Yongwei Zhang;Zhihuo Xu;Liu Chu;Robin Braun;Jiajia Shi","doi":"10.1109/JERM.2024.3378977","DOIUrl":"https://doi.org/10.1109/JERM.2024.3378977","url":null,"abstract":"Software-defined radio (SDR) can be used to detect human respiratory and heartbeat signals with the merits of low costs, high flexibility, and fast implementation. This paper proposes a human respiratory heartbeat detection system based on SDR micro-Doppler radar. The system can adjust radar parameters in real-time according to the detection environment, breaking the hardware limitations of traditional radar. Data pre-processing is performed on the transmit and receive baseband signals to obtain a composite signal containing human respiratory and heartbeat signals. In addressing the difficulty of detecting heartbeat signals compared to respiratory signals, an adaptive heartbeat signal enhancement detection algorithm named the one-time differential weighted step-size normalized least mean square (ODWS-NLMS) is proposed. This algorithm enhances the step size through weighted improvements utilizing the first-order differential characteristics of composite signals. Experiments were conducted in three distinct real-world environments, and the results indicate that the proposed algorithm outperforms discrete wavelet transform (DWT) and ensemble empirical mode decomposition (EEMD) in terms of average accuracy, root mean square error (RMSE), and signal-to-noise ratio (SNR).","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141084923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-12DOI: 10.1109/JERM.2024.3385335
Milad Mokhtari;Milica Popović
The challenges in antenna design for microwave-based breast screening systems identify two distinct needs: 1) to minimize the surface-wave propagation at the interface between the substrate and the tissue, and 2) to address the back-radiation. These surface waves become more noticeable within the substrate, particularly when a confining ground plane is present, and yet the ground plane is pivotal for achieving unidirectionality and shielding against environmental radiation. This paper introduces a simplified human breast model and offers a quantitative analysis of existing surface waves. We then propose a 16-antenna array of cavity-backed patch antennas with parasitic elements, designed for operation in the 3.1–5.1 GHz range. Each antenna element is optimized to function seamlessly alongside the breast tissue. Full-wave simulations illustrate that the proposed antenna array achieves superior unidirectionality and diminished mutual coupling levels when compared to its predecessor. We further outline the cost-effective fabrication method that employs the SYLGARD(TM) 184 silicone elastomer PDMS kit. The measurements from the fabricated antenna elements are consistent with the results of the full-wave simulations.
{"title":"Surface Wave and Back Radiation Suppression in Microwave Breast Screening","authors":"Milad Mokhtari;Milica Popović","doi":"10.1109/JERM.2024.3385335","DOIUrl":"https://doi.org/10.1109/JERM.2024.3385335","url":null,"abstract":"The challenges in antenna design for microwave-based breast screening systems identify two distinct needs: 1) to minimize the surface-wave propagation at the interface between the substrate and the tissue, and 2) to address the back-radiation. These surface waves become more noticeable within the substrate, particularly when a confining ground plane is present, and yet the ground plane is pivotal for achieving unidirectionality and shielding against environmental radiation. This paper introduces a simplified human breast model and offers a quantitative analysis of existing surface waves. We then propose a 16-antenna array of cavity-backed patch antennas with parasitic elements, designed for operation in the 3.1–5.1 GHz range. Each antenna element is optimized to function seamlessly alongside the breast tissue. Full-wave simulations illustrate that the proposed antenna array achieves superior unidirectionality and diminished mutual coupling levels when compared to its predecessor. We further outline the cost-effective fabrication method that employs the SYLGARD(TM) 184 silicone elastomer PDMS kit. The measurements from the fabricated antenna elements are consistent with the results of the full-wave simulations.","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142041422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：