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":"8 3","pages":"298-304"},"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 : 2024-06-26DOI: 10.1109/JERM.2024.3416758
Md. Abdul Awal;Azin S. Janani;Sasan Ahdi Rezaeieh;Graeme A. Macdonald;Amin Abbosh
Metabolic dysfunction-associated steatotic liver disease ranks among the most prevalent chronic liver conditions worldwide. To reduce its burden, early diagnosis is vital to enable timely medication and rehabilitation. The non-invasive diagnosis of liver health is challenging due to the limitations of existing methods. For this purpose, the design of portable non-invasive electromagnetic sensors requires knowledge of how human liver tissue and other abdominal tissues interact with electromagnetic waves. This necessitates the accurate characterisation of dielectric properties of the liver and adjacent abdominal tissues. Since postmortem changes or prolonged storage significantly change those properties and lead to incorrect interpretation, fresh human abdominal tissues, including skin, fat, muscle, and liver, were obtained at surgery, and their dielectric properties were measured immediately in the microwave frequency range of 0.5 GHz to 15 GHz. An adaptive weighted vector mean optimization algorithm was used to derive the parameters of a second-order Cole-Cole model using the experimental data. Statistical and cluster analyses were performed on the curated database following the derived model. The results showed that hepatic steatosis significantly changed the dielectric properties of the liver $(p < 0.001)$. Moreover, the liver had distinct dielectric properties from the skin, fat, and muscle tissues $(p < 0.05)$. These findings suggest that electromagnetic sensors could be used to assess liver health in a non-invasive way, which could improve liver health outcomes and reduce costs.
{"title":"Towards Non-Invasive Liver Health Monitoring: Comprehensive Microwave Dielectric Spectroscopy of Freshly Excised Human Abdominal Tissues","authors":"Md. Abdul Awal;Azin S. Janani;Sasan Ahdi Rezaeieh;Graeme A. Macdonald;Amin Abbosh","doi":"10.1109/JERM.2024.3416758","DOIUrl":"https://doi.org/10.1109/JERM.2024.3416758","url":null,"abstract":"Metabolic dysfunction-associated steatotic liver disease ranks among the most prevalent chronic liver conditions worldwide. To reduce its burden, early diagnosis is vital to enable timely medication and rehabilitation. The non-invasive diagnosis of liver health is challenging due to the limitations of existing methods. For this purpose, the design of portable non-invasive electromagnetic sensors requires knowledge of how human liver tissue and other abdominal tissues interact with electromagnetic waves. This necessitates the accurate characterisation of dielectric properties of the liver and adjacent abdominal tissues. Since postmortem changes or prolonged storage significantly change those properties and lead to incorrect interpretation, fresh human abdominal tissues, including skin, fat, muscle, and liver, were obtained at surgery, and their dielectric properties were measured immediately in the microwave frequency range of 0.5 GHz to 15 GHz. An adaptive weighted vector mean optimization algorithm was used to derive the parameters of a second-order Cole-Cole model using the experimental data. Statistical and cluster analyses were performed on the curated database following the derived model. The results showed that hepatic steatosis significantly changed the dielectric properties of the liver <inline-formula><tex-math>$(p < 0.001)$</tex-math></inline-formula>. Moreover, the liver had distinct dielectric properties from the skin, fat, and muscle tissues <inline-formula><tex-math>$(p < 0.05)$</tex-math></inline-formula>. These findings suggest that electromagnetic sensors could be used to assess liver health in a non-invasive way, which could improve liver health outcomes and reduce costs.","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":"9 1","pages":"2-14"},"PeriodicalIF":3.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455302","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":"8 3","pages":"290-297"},"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":"8 3","pages":"259-264"},"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":"8 3","pages":"282-289"},"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 : 2024-06-13DOI: 10.1109/JERM.2024.3409678
A. Paffi;F. Apollonio;M. Cadossi;V. D'Alessio;R. Fusco;A. Giannini;M. Liberti
Purpose of this work is to develop a tool for electrochemotherapy treatment planning, which automatically estimates the optimal electrode configuration on the basis of the calculation of the induced electric field in a 3D tissue volume, including the tumor lesion, obtained from patient's MRI. The tool conciliates accuracy in the estimate of the tumor coverage with speed of calculation. The optimal electrodes configuration, that guarantees the tumor electroporation with the minimum number of electrodes, is obtained by adapting algorithms for the creation of unstructured simplex meshes. To go fast, the elementary electric field distributions are pre-calculated and stored in a database and the optimization procedure is split in two consequential steps: transversal and longitudinal optimizations. The whole code is implemented in C++ environment. The tool, tested in a set of real cases, showed the complete electroporation of the lesions, while preserving noble structures from the electrodes crossing. Calculation times were compatible with real-time requirements. The proposed tool represents a valid support for the electroporation treatment planning. With respect to the literature, it automatically estimates the best electrode configuration in a realistic 3D domain, while maintaining reduced calculation times. This is crucial for improving effectiveness and reliability of electroporation-based treatments.
这项工作的目的是开发一种用于电化学疗法治疗规划的工具，该工具可根据从患者核磁共振成像中获得的包括肿瘤病灶在内的三维组织体积中感应电场的计算结果，自动估算最佳电极配置。该工具兼具估计肿瘤覆盖范围的准确性和计算速度。通过调整创建非结构化单纯网格的算法，可获得最佳电极配置，确保以最少的电极数量电穿孔肿瘤。为了加快速度，基本电场分布已预先计算并存储在数据库中，优化过程分为两个相应步骤：横向优化和纵向优化。整个代码在 C++ 环境中实现。该工具在一组真实病例中进行了测试，结果表明能对病变部位进行完全电穿孔，同时保留了电极交叉处的惰性结构。计算时间符合实时要求。所提出的工具为电穿孔治疗规划提供了有效支持。与文献相比，它能在现实三维域中自动估算最佳电极配置，同时缩短计算时间。这对于提高电穿孔治疗的有效性和可靠性至关重要。
{"title":"A Fast 3-D Approach for Electroporation Treatment Planning: Optimal Electrodes Configuration","authors":"A. Paffi;F. Apollonio;M. Cadossi;V. D'Alessio;R. Fusco;A. Giannini;M. Liberti","doi":"10.1109/JERM.2024.3409678","DOIUrl":"https://doi.org/10.1109/JERM.2024.3409678","url":null,"abstract":"Purpose of this work is to develop a tool for electrochemotherapy treatment planning, which automatically estimates the optimal electrode configuration on the basis of the calculation of the induced electric field in a 3D tissue volume, including the tumor lesion, obtained from patient's MRI. The tool conciliates accuracy in the estimate of the tumor coverage with speed of calculation. The optimal electrodes configuration, that guarantees the tumor electroporation with the minimum number of electrodes, is obtained by adapting algorithms for the creation of unstructured simplex meshes. To go fast, the elementary electric field distributions are pre-calculated and stored in a database and the optimization procedure is split in two consequential steps: transversal and longitudinal optimizations. The whole code is implemented in C++ environment. The tool, tested in a set of real cases, showed the complete electroporation of the lesions, while preserving noble structures from the electrodes crossing. Calculation times were compatible with real-time requirements. The proposed tool represents a valid support for the electroporation treatment planning. With respect to the literature, it automatically estimates the best electrode configuration in a realistic 3D domain, while maintaining reduced calculation times. This is crucial for improving effectiveness and reliability of electroporation-based treatments.","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":"8 4","pages":"393-400"},"PeriodicalIF":3.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10557476","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691782","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}
Microwave head imaging is challenging due to the dominance of clutter signals caused by the strong reflections at the boundary of the head and skull in addition to the heterogeneous nature of the head tissues. These clutter signals complicate the detection of anomalies like strokes and make both traditional and deep-learning-based imaging algorithms less effective. For example, to adapt to different environments, extensive tuning is required for traditional algorithms, while a huge amount of data is needed to train deep-learning models. To this end, a novel deep-learning-based clutter removal approach in microwave head imaging is proposed. The proposed deep learning model is self-supervised and unpaired, and can thus utilize much larger amounts of data, which would otherwise be prohibitively difficult to collect. The model includes two generators to learn the mapping function from mixed signals and the target signal alone to remove clutter and ensure producing target signals that match the original mixed signals. To achieve self-supervised learning, two discriminators are used for judging the predictions from both generators by comparing the predictions with the real signals. Using the peak signal-to-noise ratio and the structural similarity index measure, the experimental results using a 16-antenna head imaging system operating across the band 0.5–2 GHz confirm that the presented solution outperforms existing methods in removing clutter and enabling accurate target localization. The proposed solution is adaptable and scalable and can thus be generalized to other domains.
{"title":"Clutter Removal for Microwave Head Imaging via Self-Supervised Deep Learning Techniques","authors":"Wei-chung Lai;Lei Guo;Konstanty Bialkowski;Amin Abbosh;Alina Bialkowski","doi":"10.1109/JERM.2024.3409846","DOIUrl":"https://doi.org/10.1109/JERM.2024.3409846","url":null,"abstract":"Microwave head imaging is challenging due to the dominance of clutter signals caused by the strong reflections at the boundary of the head and skull in addition to the heterogeneous nature of the head tissues. These clutter signals complicate the detection of anomalies like strokes and make both traditional and deep-learning-based imaging algorithms less effective. For example, to adapt to different environments, extensive tuning is required for traditional algorithms, while a huge amount of data is needed to train deep-learning models. To this end, a novel deep-learning-based clutter removal approach in microwave head imaging is proposed. The proposed deep learning model is self-supervised and unpaired, and can thus utilize much larger amounts of data, which would otherwise be prohibitively difficult to collect. The model includes two generators to learn the mapping function from mixed signals and the target signal alone to remove clutter and ensure producing target signals that match the original mixed signals. To achieve self-supervised learning, two discriminators are used for judging the predictions from both generators by comparing the predictions with the real signals. Using the peak signal-to-noise ratio and the structural similarity index measure, the experimental results using a 16-antenna head imaging system operating across the band 0.5–2 GHz confirm that the presented solution outperforms existing methods in removing clutter and enabling accurate target localization. The proposed solution is adaptable and scalable and can thus be generalized to other domains.","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":"8 4","pages":"384-392"},"PeriodicalIF":3.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691746","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 : 2024-06-12DOI: 10.1109/JERM.2024.3409423
Vivek Kumar Srivastava;Ashwani Sharma
This paper proposes an optimized switching integrated transmitter to generate highly non-uniform magnetic field (H-field) components for near-field localization applications. The localization accuracy of a magnetic-based localization system depends on the degree of non-uniformity present in the H-field distribution. Targeting this, several state-of-the-art designs presented eight spatially distributed transmitter structures. However, the absence of required H-field components at several receiver positions resulted in poor localization performance. To overcome this problem, an overlapping coil transmitter structure has been proposed in this work that spreads the H-field components at the receiver region. Further optimization of the transmitter coil design parameters is performed analytically to accomplish a highly non-uniform H-field at the receiver location and miniaturize the transmitter size. A time-divisional approach has been exploited and realized using a switching technique to acquire the required voltage samples at the receiver. The proposed transmitter is realized using a high-frequency Litz wire, and the switching is performed by adopting DPDT switches. The fabricated prototype is experimentally verified, and the measured results show a good agreement with the analytical result. This demonstrates the potential of the proposed transmitter for near-field localization applications such as the localization of biomedical implants, wireless endoscopy capsules, etc.
本文提出了一种优化的开关式集成发射器，可为近场定位应用产生高度不均匀的磁场（H-场）分量。基于磁场的定位系统的定位精度取决于 H 场分布的不均匀程度。针对这一点，一些最先进的设计提出了八种空间分布式发射器结构。然而，由于多个接收器位置缺乏所需的 H 场成分，导致定位性能不佳。为克服这一问题，本研究提出了一种重叠线圈发射器结构，可在接收器区域扩散 H 场分量。通过分析进一步优化发射器线圈设计参数，在接收器位置实现高度不均匀的 H 场，并缩小发射器尺寸。利用分时方法，并通过开关技术在接收器获取所需的电压样本。拟议的发射器使用高频利兹线实现，开关采用 DPDT 开关。制作的原型经过了实验验证，测量结果与分析结果非常吻合。这证明了所提出的发射器在近场定位应用中的潜力，如生物医学植入物的定位、无线内窥镜胶囊等。
{"title":"An Optimized Switching Integrated Transmitter Pad for Generating Orthogonal H-Field Components to Localize Implanted Devices","authors":"Vivek Kumar Srivastava;Ashwani Sharma","doi":"10.1109/JERM.2024.3409423","DOIUrl":"https://doi.org/10.1109/JERM.2024.3409423","url":null,"abstract":"This paper proposes an optimized switching integrated transmitter to generate highly non-uniform magnetic field (H-field) components for near-field localization applications. The localization accuracy of a magnetic-based localization system depends on the degree of non-uniformity present in the H-field distribution. Targeting this, several state-of-the-art designs presented eight spatially distributed transmitter structures. However, the absence of required H-field components at several receiver positions resulted in poor localization performance. To overcome this problem, an overlapping coil transmitter structure has been proposed in this work that spreads the H-field components at the receiver region. Further optimization of the transmitter coil design parameters is performed analytically to accomplish a highly non-uniform H-field at the receiver location and miniaturize the transmitter size. A time-divisional approach has been exploited and realized using a switching technique to acquire the required voltage samples at the receiver. The proposed transmitter is realized using a high-frequency Litz wire, and the switching is performed by adopting DPDT switches. The fabricated prototype is experimentally verified, and the measured results show a good agreement with the analytical result. This demonstrates the potential of the proposed transmitter for near-field localization applications such as the localization of biomedical implants, wireless endoscopy capsules, etc.","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":"8 4","pages":"363-371"},"PeriodicalIF":3.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691810","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 : 2024-06-12DOI: 10.1109/JERM.2024.3406331
François Frassati;Mélanie Descharles;Martin Gauroy;Agathe Yvinou;Eric Stindel;Guillaume Dardenne;Guillaume Nonglaton;Pierre Gasnier
Our research aims to enhance smart orthopedic knee implants used in Total Knee Arthroplasty (TKA). With the projected quadrupling of TKA demand by 2030 due to factors like aging populations, rising obesity rates, and broader indications for younger patients, our focus is on instrumented medical implants to measure knee parameters. In this paper, we report the optimization of a wireless power transmission system for powering smart knee implants, employing an established HF Near-field Resonant Inductive Coupling (NRIC) technique at $13.56 ,mathrm{M}mathrm{Hz}$ inside the stem of a tibial knee implant. We propose a pragmatic optimization approach in this study, guided by the integration constraints of a knee implant and validated by orthopedic surgeons through cadaveric specimen testing. Finite Element simulations guided the selection of a frontal 3-turn solenoid (called “paperclip” coil) at the Rx side, located at the tip of the stem, which demonstrated balanced performance metrics and reasonable volume occupancy (1.6 cm 3 ). Power transfer measurements were conducted through conductive solutions mimicking skin, muscle, and bones. At $13.56 ,mathrm{M}mathrm{Hz}$ , a power transfer efficiency $eta$ of 30% and 7.5% ( $300 ,mathrm{m}mathrm{W}$ and $75 ,mathrm{m}mathrm{W}$ at $1 ,mathrm{W}$ input power) was achieved at Tx-Rx distances of $25 ,mathrm{m}mathrm{m}$ and $40 ,mathrm{m}mathrm{m}$ respectively. The proposed solution was implanted in a cadaveric specimen : $250 ,mathrm{m}mathrm{W}$ was obtained at an estimated $30 ,mathrm{m}mathrm{m}$ distance for an input power of $1 ,mathrm{W}$ at the Tx side. For the same distance, we also performed a successful DC power provision up to $64 ,mathrm{m}mathrm{W}$ at $3 ,mathrm{V}$ DC and data transfer functions at $26, mathrm{kbit,s}^{-1}$ in the cadaver. The proposed system, with its integration strategy, holds promise in powering advanced sensor functions, contributing to the identification and monitoring of postoperative complications and potentially reducing the need for long-term revisions.
{"title":"Powering Smart Orthopedic Implants Through Near-Field Resonant Inductive Coupling","authors":"François Frassati;Mélanie Descharles;Martin Gauroy;Agathe Yvinou;Eric Stindel;Guillaume Dardenne;Guillaume Nonglaton;Pierre Gasnier","doi":"10.1109/JERM.2024.3406331","DOIUrl":"https://doi.org/10.1109/JERM.2024.3406331","url":null,"abstract":"Our research aims to enhance smart orthopedic knee implants used in Total Knee Arthroplasty (TKA). With the projected quadrupling of TKA demand by 2030 due to factors like aging populations, rising obesity rates, and broader indications for younger patients, our focus is on instrumented medical implants to measure knee parameters. In this paper, we report the optimization of a wireless power transmission system for powering smart knee implants, employing an established HF Near-field Resonant Inductive Coupling (NRIC) technique at \u0000<inline-formula><tex-math>$13.56 ,mathrm{M}mathrm{Hz}$</tex-math></inline-formula>\u0000 inside the stem of a tibial knee implant. We propose a pragmatic optimization approach in this study, guided by the integration constraints of a knee implant and validated by orthopedic surgeons through cadaveric specimen testing. Finite Element simulations guided the selection of a frontal 3-turn solenoid (called “paperclip” coil) at the Rx side, located at the tip of the stem, which demonstrated balanced performance metrics and reasonable volume occupancy (1.6 cm\u0000<sup>3</sup>\u0000). Power transfer measurements were conducted through conductive solutions mimicking skin, muscle, and bones. At \u0000<inline-formula><tex-math>$13.56 ,mathrm{M}mathrm{Hz}$</tex-math></inline-formula>\u0000, a power transfer efficiency \u0000<inline-formula><tex-math>$eta$</tex-math></inline-formula>\u0000 of 30% and 7.5% (\u0000<inline-formula><tex-math>$300 ,mathrm{m}mathrm{W}$</tex-math></inline-formula>\u0000 and \u0000<inline-formula><tex-math>$75 ,mathrm{m}mathrm{W}$</tex-math></inline-formula>\u0000 at \u0000<inline-formula><tex-math>$1 ,mathrm{W}$</tex-math></inline-formula>\u0000 input power) was achieved at Tx-Rx distances of \u0000<inline-formula><tex-math>$25 ,mathrm{m}mathrm{m}$</tex-math></inline-formula>\u0000 and \u0000<inline-formula><tex-math>$40 ,mathrm{m}mathrm{m}$</tex-math></inline-formula>\u0000 respectively. The proposed solution was implanted in a cadaveric specimen : \u0000<inline-formula><tex-math>$250 ,mathrm{m}mathrm{W}$</tex-math></inline-formula>\u0000 was obtained at an estimated \u0000<inline-formula><tex-math>$30 ,mathrm{m}mathrm{m}$</tex-math></inline-formula>\u0000 distance for an input power of \u0000<inline-formula><tex-math>$1 ,mathrm{W}$</tex-math></inline-formula>\u0000 at the Tx side. For the same distance, we also performed a successful DC power provision up to \u0000<inline-formula><tex-math>$64 ,mathrm{m}mathrm{W}$</tex-math></inline-formula>\u0000 at \u0000<inline-formula><tex-math>$3 ,mathrm{V}$</tex-math></inline-formula>\u0000 DC and data transfer functions at \u0000<inline-formula><tex-math>$26, mathrm{kbit,s}^{-1}$</tex-math></inline-formula>\u0000 in the cadaver. The proposed system, with its integration strategy, holds promise in powering advanced sensor functions, contributing to the identification and monitoring of postoperative complications and potentially reducing the need for long-term revisions.","PeriodicalId":29955,"journal":{"name":"IEEE Journal of Electromagnetics RF and Microwaves in Medicine and Biology","volume":"8 4","pages":"372-383"},"PeriodicalIF":3.0,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691676","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 : 2024-06-03DOI: 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":"8 3","pages":"273-281"},"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}
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