arXiv subjects

Akimasa Hirata

Publications and source records attributed to Akimasa Hirata.

At least 19 recordsLinked to original sources

arXiv2025

Group-Level and Personalized Optimization for the Insula and Hippocampus Focal Electric Field in Transcranial Temporal Interferential Stimulation: A Computational Study

This study evaluated transcranial temporal interference stimulation (tTIS) for focal targeting of the insula and hippocampus, which are clinically relevant yet anatomically difficult to stimulate. Individualized and group-level electrode optimizations were compared to determine whether generalized montages can provide reliable targeting with reduced modeling demands. Sixty high-resolution head models (30 individuals and their mirrored counterparts) were constructed from T1- and T2-weighted MRI. Electric fields (EFs) were computed using the scalar-potential finite-difference method. Electrode montages and current ratios were optimized to minimize the root-mean-square error between simulated and target EF envelope (EFE) distributions, with a threshold of 0.3 V/m. Subsampling analysis was performed to estimate the number of models required for stable group-level outcomes. For the insula, a montage combining T7-P7 and Fp1-Fp2 achieved the highest focality, comparable to individualized results with reduced variability. For the hippocampus, the F7-T7 and T8-P8 montage gave the best group-level focality, though individualized optimization improved off-target suppression. Stable group-level patterns were obtained using 20 models for the insula and 9 for the hippocampus. Optimal tTIS montages depend on target depth. Group-level optimization suffices for cortical regions like the insula, whereas individualized tuning remains preferable for deeper targets such as the hippocampus.

physics.med-ph↗

arXiv2025

Electromagnetic Field Exposure Assessment and Mitigation Strategies for Wireless Power Transfer Systems: A Review and Future Perspectives

Wireless power transfer (WPT) technologies are increasingly being applied in fields ranging from consumer electronics and electric vehicles to space-based energy systems and medical implants. While WPT offers contactless power delivery, it introduces electromagnetic field (EMF) emissions, necessitating careful assessment to address safety and public health concerns. Exposure guidelines developed by ICNIRP and IEEE define frequency-dependent limits based on internal quantities, such as electric field strength and specific absorption rate, intended to prevent tissue nerve stimulation < 100 kHz and heating > 100 kHz, respectively. Complementing these guidelines, assessment standards including the International Electrotechnical Commission (IEC)/IEEE 63184 and IEC Technical Report 63377, provide practical procedures for evaluating the EMF exposure in WPT systems. This review offers a comparative overview of major WPT modalities, with a focus on recent developments in computational dosimetry and standardized assessment techniques for the complex, non-uniform fields typical of WPT environments. It also discusses electromagnetic interference with medical devices and exposure scenarios involving partial body proximity and various postures. A notable observation across modalities is the considerable variability, often spanning an order of magnitude, in the allowable transfer power, depending on the field distribution and assessment approach. Remaining challenges include the lack of harmonized guidance for intermediate frequencies and localized exposure, underscoring the importance of further coordination in international standardization efforts. Addressing these issues is essential for the safe and widespread deployment of WPT technologies.

physics.app-ph↗

arXiv2025

Roadmap towards Personalized Approaches and Safety Considerations in Non-Ionizing Radiation: From Dosimetry to Therapeutic and Diagnostic Applications

This roadmap provides a comprehensive and forward-looking perspective on the individualized application and safety of non-ionizing radiation (NIR) dosimetry in diagnostic and therapeutic medicine. Covering a wide range of frequencies, i.e., from low-frequency to terahertz, this document provides an overview of the current state of the art and anticipates future research needs in selected key topics of NIR-based medical applications. It also emphasizes the importance of personalized dosimetry, rigorous safety evaluation, and interdisciplinary collaboration to ensure safe and effective integration of NIR technologies in modern therapy and diagnosis.

physics.med-ph↗

arXiv2025

A Noninvasive and Dispersive Framework for Estimating Nonuniform Conductivity of Brain Tumor in Patient-Specific Head Models

We propose a noninvasive and dispersive framework for estimating the spatially nonuniform conductivity of brain tumors using MR images. The method consists of two components: (i) voxel-wise assignment of tumor conductivity based on reference values fitted to the Cole-Cole model using empirical data from the literature and (ii) fine-tuning of a deep learning model pretrained on healthy participants. A total of 67 cases, comprising both healthy participants and tumor patients and including 9,806 paired T1- and T2-weighted MR images, were used for training and evaluation. The proposed method successfully estimated patient-specific conductivity maps, exhibiting smooth spatial variations that reflected tissue characteristics, such as edema, necrosis, and rim-associated intensity gradients observed in T1- and T2-weighted MR images. At 10 kHz, case-wise mean conductivity values varied across patients, ranging from 0.132 to 0.512 S/m in the rim (defined as the region within 2 mm of the tumor boundary), from 0.132 to 0.608 S/m in the core (the area inside the rim), and from 0.141 to 0.542 S/m in the entire tumor. Electromagnetic simulations for transcranial magnetic stimulation in individualized head models showed substantial differences in intratumoral field distributions between uniform assignments and the proposed nonuniform maps. Furthermore, this framework demonstrated voxel-wise dispersive mapping at 10 kHz, 1 MHz, and 100 MHz. This framework supports accurate whole-brain conductivity estimation by incorporating both individual anatomical structures and tumor-specific characteristics. Collectively, these results advance patient-specific EM modeling for tumor-bearing brains and lay the groundwork for subsequent microwave-band validation.

physics.med-ph↗

arXiv2023

Multiagent Copilot Approach for Shared Autonomy between Human EEG and TD3 Deep Reinforcement Learning

Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer interface (BCI) systems decipher human implicit brain signals regardless of the explicit environment. In this study, we integrated deep RL and BCI to improve beneficial human interventions in autonomous systems and the performance in decoding brain activities by considering environmental factors. Shared autonomy was allowed between the action command decoded from the electroencephalography (EEG) of the human agent and the action generated from the twin delayed DDPG (TD3) agent for a given environment. Our proposed copilot control scheme with a full blocker (Co-FB) significantly outperformed the individual EEG (EEG-NB) or TD3 control. The Co-FB model achieved a higher target approaching score, lower failure rate, and lower human workload than the EEG-NB model. The Co-FB control scheme had a higher invisible target score and level of allowed human intervention than the TD3 model. We also proposed a disparity d-index to evaluate the effect of contradicting agent decisions on the control accuracy and authority of the copilot model. We found a significant correlation between the control authority of the TD3 agent and the performance improvement of human EEG classification with respect to the d-index. We also observed that shifting control authority to the TD3 agent improved performance when BCI decoding was not optimal. These findings indicate that the copilot system can effectively handle complex environments and that BCI performance can be improved by considering environmental factors. Future work should employ continuous action space and different multi-agent approaches to evaluate copilot performance.

cs.HC↗

arXiv2023

SHARM: Segmented Head Anatomical Reference Models

Reliable segmentation of anatomical tissues of human head is a major step in several clinical applications such as brain mapping, surgery planning and associated computational simulation studies. Segmentation is based on identifying different anatomical structures through labeling different tissues through medical imaging modalities. The segmentation of brain structures is commonly feasible with several remarkable contributions mainly for medical perspective; however, non-brain tissues are of less interest due to anatomical complexity and difficulties to be observed using standard medical imaging protocols. The lack of whole head segmentation methods and unavailability of large human head segmented datasets limiting the variability studies, especially in the computational evaluation of electrical brain stimulation (neuromodulation), human protection from electromagnetic field, and electroencephalography where non-brain tissues are of great importance. To fill this gap, this study provides an open-access Segmented Head Anatomical Reference Models (SHARM) that consists of 196 subjects. These models are segmented into 15 different tissues; skin, fat, muscle, skull cancellous bone, skull cortical bone, brain white matter, brain gray matter, cerebellum white matter, cerebellum gray matter, cerebrospinal fluid, dura, vitreous humor, lens, mucous tissue and blood vessels. The segmented head models are generated using open-access IXI MRI dataset through convolutional neural network structure named ForkNet+. Results indicate a high consistency in statistical characteristics of different tissue distribution in age scale with real measurements. SHARM is expected to be a useful benchmark not only for electromagnetic dosimetry studies but also for different human head segmentation applications.

cs.CV↗

arXiv2022

COVID-19 forecasting using new viral variants and vaccination effectiveness models

Background: Recently, a high number of daily positive COVID-19 cases have been reported in regions with relatively high vaccination rates; hence, booster vaccination has become necessary. In addition, infections caused by the different variants and correlated factors have not been discussed in depth. With large variabilities and different co-factors, it is difficult to use conventional mathematical models to forecast the incidence of COVID-19. Methods: Machine learning based on long short-term memory was applied to forecasting the time series of new daily positive cases (DPC), serious cases, hospitalized cases, and deaths. Data acquired from regions with high rates of vaccination, such as Israel, were blended with the current data of other regions in Japan to factor in the potential effects of vaccination. The protection provided by symptomatic infection was also considered in terms of the population effectiveness of vaccination as well as the waning protection and ratio and infectivity of viral variants. To represent changes in public behavior, public mobility and interactions through social media were also included in the analysis. Findings: Comparing the observed and estimated new DPC in Tel Aviv, Israel, the parameters characterizing vaccination effectiveness and the waning protection from infection were well estimated; the vaccination effectiveness of the second dose after 5 months and the third dose after two weeks from infection by the delta variant were 0.24 and 0.95, respectively. Using the extracted parameters regarding vaccination effectiveness, new cases in three prefectures of Japan were replicated.

cs.LG↗

arXiv2021

Mobility-Dependent and Mobility-Compensated Effective Reproduction Number of COVID-19 Viral Variants: New Metric for Infectivity Evaluation

During epidemics, estimation of the effective reproduction number (ERN) associated with infectious disease is a challenging topic for policy development and medical resource management. There is still an open question about the dominant factors to characterize in corona virus disease 2019 (COVID-19), although recent studies based on nonlinear regression with machine learning suggested mobility. The emergence of new viral variants is common in widespread pandemics. However, understanding the potential ERN of new variants is required for policy revision, including lockdown constraints. In this study, we proposed time-averaged mobility at transit stations as a surrogate to correlate with ERN using data from three prefectures in Japan. The latency and duration to average over the mobility were 6-8 days and 6-7, respectively (R2 was 0.109-0.512 in Tokyo, 0.365-0.607 in Osaka, and 0.317-0.631 in Aichi). The same linear correlation was confirmed in Singapore and London. The mobility-adjusted ERN of the alpha variant was 15%-30%, and was 20%-40% higher than the standard type in Osaka, Aichi, and London. Similarly, the ERN of the delta variant was 20%-40% higher than that of the standard type in Osaka and Aichi. The proposed metric would be useful for proper evaluation of the infectivity of different variants in terms of ERN.

q-bio.PE↗

arXiv2021

Knowledge discovery from emergency ambulance dispatch during COVID-19: A case study of Nagoya City, Japan

Accurate forecasting of medical service requirements is an important big data problem that is crucial for resource management in critical times such as natural disasters and pandemics. With the global spread of coronavirus disease 2019 (COVID-19), several concerns have been raised regarding the ability of medical systems to handle sudden changes in the daily routines of healthcare providers. One significant problem is the management of ambulance dispatch and control during a pandemic. To help address this problem, we first analyze ambulance dispatch data records from April 2014 to August 2020 for Nagoya City, Japan. Significant changes were observed in the data during the pandemic, including the state of emergency (SoE) declared across Japan. In this study, we propose a deep learning framework based on recurrent neural networks to estimate the number of emergency ambulance dispatches (EADs) during a SoE. The fusion of data includes environmental factors, the localization data of mobile phone users, and the past history of EADs, thereby providing a general framework for knowledge discovery and better resource management. The results indicate that the proposed blend of training data can be used efficiently in a real-world estimation of EAD requirements during periods of high uncertainties such as pandemics.

cs.AI↗

arXiv2021

Influence of segmentation accuracy in structural MR head scans on electric field computation for TMS and tES

In several diagnosis and therapy procedures based on electrostimulation effect, the internal physical quantity related to the stimulation is the induced electric field. To estimate the induced electric field in an individual human model, the segmentation of anatomical imaging, such as (magnetic resonance image (MRI) scans, of the corresponding body parts into tissues is required. Then, electrical properties associated with different annotated tissues are assigned to the digital model to generate a volume conductor. An open question is how segmentation accuracy of different tissues would influence the distribution of the induced electric field. In this study, we applied parametric segmentation of different tissues to exploit the segmentation of available MRI to generate different quality of head models using deep learning neural network architecture, named ForkNet. Then, the induced electric field are compared to assess the effect of model segmentation variations. Computational results indicate that the influence of segmentation error is tissue-dependent. In brain, sensitivity to segmentation accuracy is relatively high in cerebrospinal fluid (CSF), moderate in gray matter (GM) and low in white matter for transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES). A CSF segmentation accuracy reduction of 10% in terms of Dice coefficient (DC) lead to decrease up to 4% in normalized induced electric field in both applications. However, a GM segmentation accuracy reduction of 5.6% DC leads to increase of normalized induced electric field up to 6%. Opposite trend of electric field variation was found between CSF and GM for both TMS and tES. The finding obtained here would be useful to quantify potential uncertainty of computational results.

physics.med-ph↗

arXiv2020

Human Exposure to Radiofrequency Energy above 6 GHz: Review of Computational Dosimetry Studies

International guidelines/standards for human protection from electromagnetic fields have been revised recently, especially for frequencies above 6 GHz where new wireless communication systems have been deployed. Above this frequency a new physical quantity "absorbed/epithelia power density" has been adopted as a dose metric. Then, the permissible level of external field strength/power density is derived for practical assessment. In addition, a new physical quantity, fluence or absorbed energy density, is introduced for protection from brief pulses (especially for shorter than 10 sec). These limits were explicitly designed to avoid excessive increases in tissue temperature, based on electromagnetic and thermal modeling studies but supported by experimental data where available. This paper reviews the studies on the computational modeling/dosimetry which are related to the revision of the guidelines/standards. The comparisons with experimental data as well as an analytic solution are also been presented. Future research needs and additional comments on the revision will also be mentioned.

physics.med-ph↗

arXiv2020

Model-based approach for analyzing prevalence of nuclear cataracts in elderly residents

Recent epidemiological studies have hypothesized that the prevalence of cortical cataracts is closely related to ultraviolet radiation. However, the prevalence of nuclear cataracts is higher in elderly people in tropical areas than in temperate areas. The dominant factors inducing nuclear cataracts have been widely debated. In this study, the temperature increase in the lens due to exposure to ambient conditions was computationally quantified in subjects of 50-60 years of age in tropical and temperate areas, accounting for differences in thermoregulation. A thermoregulatory response model was extended to consider elderly people in tropical areas. The time course of lens temperature for different weather conditions in five cities in Asia was computed. The temperature was higher around the mid and posterior part of the lens, which coincides with the position of the nuclear cataract. The duration of higher temperatures in the lens varied, although the daily maximum temperatures were comparable. A strong correlation (adjusted R2 > 0.85) was observed between the prevalence of nuclear cataract and the computed cumulative thermal dose in the lens. We propose the use of a cumulative thermal dose to assess the prevalence of nuclear cataracts. Cumulative wet-bulb globe temperature, a new metric computed from weather data, would be useful for practical assessment in different cities.

physics.med-ph↗

arXiv2020

Correlation between COVID-19 morbidity and mortality rates in Japan and local population density, temperature and absolute humidity

This study analyzed the morbidity and mortality rates of the COVID-19 pandemic in different prefectures of Japan. Under the constraint that daily maximum confirmed deaths and daily maximum cases should exceed 4 and 10, respectively, 14 prefectures were included, and cofactors affecting the morbidity and mortality rates were evaluated. In particular, the number of confirmed deaths was assessed excluding the cases of nosocomial infections and nursing home patients. A mild correlation was observed between morbidity rate and population density (R2=0.394). In addition, the percentage of the elderly per population was also found to be non-negligible. Among weather parameters, the maximum temperature and absolute humidity averaged over the duration were found to be in modest correlation with the morbidity and mortality rates, excluding the cases of nosocomial infections. The lower morbidity and mortality are observed for higher temperature and absolute humidity. Multivariate analysis considering these factors showed that determination coefficients for the spread, decay, and combined stages were 0.708, 0.785, and 0.615, respectively. These findings could be useful for intervention planning during future pandemics, including a potential second COVID-19 outbreak.

q-bio.PE↗

arXiv2020

Influence of Absolute Humidity, Temperature and Population Density on COVID-19 Spread and Decay Durations: Multi-prefecture Study in Japan

This study analyzed the spread and decay durations of the COVID-19 pandemic in different prefectures of Japan. During the pandemic, affordable healthcare was widely available in Japan and the medical system did not suffer a collapse, making accurate comparisons between prefectures possible. For the 16 prefectures included in this study that had daily maximum confirmed cases exceeding ten, the number of daily confirmed cases follow bell-shape or log-normal distribution in most prefectures. A good correlation was observed between the spread and decay durations. However, some exceptions were observed in areas where travelers returned from foreign countries, which were defined as the origins of infection clusters. Excluding these prefectures, the population density was shown to be a major factor affecting the spread and decay patterns, with R2=0.39 (p<0.05) and 0.42 (p<0.05), respectively, approximately corresponding to social distancing. The maximum absolute humidity was found to affect the decay duration normalized by the population density (R2>0.36, p <0.05). Our findings indicate that the estimated pandemic spread duration, based on the multivariate analysis of maximum absolute humidity, ambient temperature, and population density (adjusted R2=0.53, p-value<0.05), could prove useful for intervention planning during potential future pandemics, including a second COVID-19 outbreak.

q-bio.PE↗

arXiv2020

Assessment of absorbed power density and temperature rise for nonplanar body model under electromagnetic exposure above 6 GHz

The averaged absorbed power density (APD) and temperature rise in body models with nonplanar surfaces were computed for electromagnetic exposure above 6 GHz. Different calculation schemes for the averaged APD were investigated. Additionally, a novel compensation method for correcting the heat convection rate on the air/skin interface in voxel human models was proposed and validated. The compensation method can be easily incorporated into bioheat calculations and does not require information regarding the normal direction of the boundary voxels, in contrast to a previously proposed method. The APD and temperature rise were evaluated using models of a two-dimensional cylinder and a three-dimensional partial forearm. The heating factor, which was defined as the ratio of the temperature rise to the APD, was calculated using different APD averaging schemes. Our computational results revealed different frequency and curvature dependences. For body models with curvature radii of >30 mm and at frequencies of >20 GHz, the differences in the heating factors among the APD schemes were small.

physics.comp-ph↗

arXiv2020

Review on Biophysical Modelling and Simulation Studies for Transcranial Magnetic Stimulation

Transcranial magnetic stimulation (TMS) is a technique for noninvasively stimulating a brain area for therapeutic, rehabilitation treatments and neuroscience research. Despite our understanding of the physical principles and experimental developments pertaining to TMS, it is difficult to identify the exact brain target as the generated dosage exhibits a non-uniform distribution owing to the complicated and subject-dependent brain anatomy and the lack of biomarkers that can quantify the effects of TMS in most cortical areas. Computational dosimetry has progressed significantly and enables TMS assessment by computation of the induced electric field (the primary physical agent known to activate the brain neurons) in a digital representation of the human head. In this review, TMS dosimetry studies are summarised, clarifying the importance of the anatomical and human biophysical parameters and computational methods. This review shows that there is a high consensus on the importance of a detailed cortical folding representation and an accurate modelling of the surrounding cerebrospinal fluid. Recent studies have also enabled the prediction of individually optimised stimulation based on magnetic resonance imaging of the patient/subject and have attempted to understand the temporal effects of TMS at the cellular level by incorporating neural modelling. These efforts, together with the fast deployment of personalised TMS computations, will permit the adoption of TMS dosimetry as a standard procedure in clinical procedures.

q-bio.NC↗

arXiv2020

Development of accurate human head models for personalized electromagnetic dosimetry using deep learning

The development of personalized human head models from medical images has become an important topic in the electromagnetic dosimetry field, including the optimization of electrostimulation, safety assessments, etc. Human head models are commonly generated via the segmentation of magnetic resonance images into different anatomical tissues. This process is time consuming and requires special experience for segmenting a relatively large number of tissues. Thus, it is challenging to accurately compute the electric field in different specific brain regions. Recently, deep learning has been applied for the segmentation of the human brain. However, most studies have focused on the segmentation of brain tissue only and little attention has been paid to other tissues, which are considerably important for electromagnetic dosimetry. In this study, we propose a new architecture for a convolutional neural network, named ForkNet, to perform the segmentation of whole human head structures, which is essential for evaluating the electrical field distribution in the brain. The proposed network can be used to generate personalized head models and applied for the evaluation of the electric field in the brain during transcranial magnetic stimulation. Our computational results indicate that the head models generated using the proposed network exhibit strong matching with those created via manual segmentation in an intra-scanner segmentation task.

eess.IV↗

arXiv2020

Effect of skin-to-skin contact on stimulation threshold and dosimetry

The human dosimetry for electromagnetic field exposure is an essential task to develop exposure guidelines/standards for human safety as well as product safety assessment. At frequencies from a few hundred Hz to 10 MHz, the adverse effect to be protected is the stimulation of the peripheral nervous system. The in situ electric field in the skin is used as a surrogate of nerve activation. In the low-frequency dosimetry, a high but inaccurate in situ electric field has been reported at positions where a skin-to-skin contact exists, whose relation to the stimulation is controversial. One of the reasons for high electric fields may be attributable to the current resolution of anatomical models. In this study, we first evaluate the stimulation threshold at postures of skin-to-skin contact experimentally for different hand/finger positions to represent skin touching/non-touching scenarios. We confirm that the skin-to-skin contact does not lower the threshold current of magnetic stimulation devices needed to induce pain. Second, a new method is proposed for hand modeling to configure different finger positions using static hand models with similar postures to the experiments. We compute the in situ electric field at skin-to-skin contact for the different hand posture scenarios that indicate an excessive raise of the electric field in skin-to-skin regions that is not justified by the experiments. The comparison suggests that a high in situ electric field in the skin would be caused by poor modeling of the skin layers, which is not enough to represent in a resolution of the order of a millimeter. This skin-to-skin contact should not be considered to set the restriction in the international exposure guidelines/standards as well as product safety assessment.

physics.med-ph↗