Journal of Radiological Protection最新文献

{"title":"Catchment-based radiological impact assessment of a postulated severe accident at Akkuyu NPP: a FEP-driven dose assessment for humans and biota in the Göksu river catchment.","authors":"Abdullah Dirican, Aydın Parmaksız, Mehmet Cem Toker","doi":"10.1088/1361-6498/ae6518","DOIUrl":"10.1088/1361-6498/ae6518","url":null,"abstract":"<p><p>Radiological environmental impact assessment study was carried out for the terrestrial agricultural systems (AGS) and non-human biota (NHB) within the Göksu river catchment, proximate to the Akkuyu NPP in Türkiye, which is currently under construction. A severe accident scenario, as envisaged by the construction company, involving an atmospheric release of 3.5 TBq of¹³⁷Cs and 1 TBq of⁹⁰Sr, was considered. interaction matrices and exposure scenarios were meticulously prepared for AGS and NHB by analysing relevant features, events, and processes (FEP)s. Total annual effective doses to adult human receptors in two villages, Bükdeğirmeni and Mucuk, situated approximately 50-60 km from the NPP, were calculated using the RESRAD-Onsite model. Dose rates to terrestrial and freshwater biota within a forest area of the Göksu river catchment were assessed using the ERICA Tool. The calculated total annual doses for adults were approximately 1.0 mSv and 1.1 mSv for Bükdeğirmeni and Mucuk villages, respectively, with external irradiation from ground-deposited¹³⁷Cs being the dominant pathway. For NHB, total absorbed dose rates ranged from 0.67 to 5.36<i>µ</i>Sv·h^-1for terrestrial organisms and from 3.0 to 8.7 × 10⁴<i>µ</i>Sv·h^-1for freshwater organisms. While all terrestrial biota dose rates remained below the IAEA screening benchmark (40<i>µ</i>Sv·h^-1), most freshwater organisms exceeded the corresponding benchmark (400<i>µ</i>Sv·h^-1), indicating potential ecological risks in aquatic environments. The FEP-driven catchment-based approach facilitated a systematic evaluation of potential radiological consequences, underscoring the importance of such assessments for emergency preparedness and environmental management.</p>","PeriodicalId":50068,"journal":{"name":"Journal of Radiological Protection","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147787581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Updated computational performance evaluation of ICRP mesh-type reference computational phantoms using PHITS, Geant4, MCNP6.3, and EGSnrc.","authors":"Bangho Shin, Suhyeon Kim, Chansoo Choi, Hyeonil Kim, Yeon Soo Yeom, Junyoung Lee, Reid W Townson, Chan Hyeong Kim","doi":"10.1088/1361-6498/ae670c","DOIUrl":"10.1088/1361-6498/ae670c","url":null,"abstract":"<p><p>The International Commission on Radiological Protection (ICRP) has adopted mesh-type reference computational phantoms (MRCPs) for the calculation of new reference dose coefficients following the forthcoming general recommendations. In the present study, the computational performance of adult and pediatric MRCPs was systematically evaluated using recently updated Monte Carlo radiation transport codes, including PHITS (version 3.31), Geant4 (version 11.01.p02), MCNP6.3, and EGSnrc (version 2025a). Performance evaluation included initialisation time, memory usage, computation time, and multi-threading efficiency for photon, electron, and neutron transport under selected external exposure scenarios. Among the evaluated codes, EGSnrc exhibited the longest initialisation time, approximately 8-9 times longer than PHITS. Nevertheless, the absolute time remained within a few minutes. In terms of memory usage, PHITS and EGSnrc required the least memory (<2 GB), whereas Geant4 required the largest memory (approximately 13-14 GB). For computation time, EGSnrc showed the fastest performance for photons and electrons with energies ⩽10 MeV, while PHITS demonstrated the shortest computation time for neutrons. MCNP6.3 consistently exhibited longer computation times compared with the other codes. When compared with simulations using the existing ICRP voxel-type reference computational phantoms (VRCPs), PHITS and Geant4 showed comparable or shorter computation times for mesh phantoms. EGSnrc exhibited comparable or shorter computation times for pediatric mesh phantoms, whereas the opposite trend was observed for adult phantoms. In contrast, MCNP6.3 consistently showed longer computation times for the MRCPs than for the VRCPs. Despite its relatively longer absolute computation time, MCNP6.3 demonstrated the best multi-threading performance among the evaluated codes, showing near-linear scaling of computation speed with increasing number of threads. The results of this study provide practical guidance for users in selecting an optimal Monte Carlo code for dose calculations using ICRP MRCPs.</p>","PeriodicalId":50068,"journal":{"name":"Journal of Radiological Protection","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147823042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Specific absorbed fractions for detailed foetal organs from maternal electron sources using gestation-specific pregnant phantoms.","authors":"Tae-Eun Kwon, Cari M Kitahara, Choonsik Lee","doi":"10.1088/1361-6498/ae6869","DOIUrl":"10.1088/1361-6498/ae6869","url":null,"abstract":"<p><p>Accurate estimation of foetal dose from maternal intake of beta-emitting radionuclides (e.g.¹³¹I,¹⁷⁷Lu,¹³⁷Cs) is essential for protecting the developing foetus and assessing its radiological risks. This study developed a comprehensive dataset of electron specific absorbed fractions (SAFs) from maternal source regions to detailed foetal target organs using the series of anatomically realistic hybrid pregnant female phantoms representing eight gestational stages (8-38 weeks). Monte Carlo radiation transport simulations were performed to compute electron SAFs for 70 maternal source regions and 55 foetal target organs across 20 energies (10 keV-10 MeV). The results revealed a strong dependence of foetal SAFs on maternal-foetal geometry, with adjacent maternal organs (e.g. amniotic fluid, urinary bladder wall) producing markedly higher SAFs than distant sources. Organ-specific analyses showed large variability among foetal targets, particularly for electrons, underscoring the need for organ-level rather than total-body dose estimation. Comparisons with previous datasets based on simplified phantoms demonstrated that anatomical realism and gestation-specific modelling substantially influence dose transfer predictions. The electron SAF dataset presented here, developed with high anatomical and energy resolution, provides an essential foundation for improving foetal internal dosimetry and supporting radiological protection decisions in medical, environmental, and emergency exposure scenarios.</p>","PeriodicalId":50068,"journal":{"name":"Journal of Radiological Protection","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147845301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges and potential solutions for radiation protection research infrastructures.","authors":"Alan H Tkaczyk, Roald H Ivask, Florence Miller, Erica Maremonti, Giacomo Cuttone, Benjamin Zorko, Veronika Groma, Simon Bouffler, Elizabeth A Ainsbury, Yannick Saintigny","doi":"10.1088/1361-6498/ae582c","DOIUrl":"10.1088/1361-6498/ae582c","url":null,"abstract":"<p><p>Radiation protection research (RPR), particularly its infrastructural aspects, is grappling with numerous challenges. Key among these are financial constraints for hiring permanent technical staff, accessibility issues, and administrative support requirements. The European Partnership for RPR (PIANOFORTE) conducted a workshop to discuss these challenges. The workshop identified a lack of sustained funding for maintaining and upgrading equipment and facilities, retaining skilled staff, and managing databases as the primary hurdles. This paper delves into these challenges in greater detail and proposes policy solutions to strengthen the infrastructure of RPR. It emphasises the need for improved financial models, better accessibility to databases, and more effective training of staff. It also suggests fostering connections with other related databases to share costs and maintain a prevalent digital presence. The goal is to ensure the sustainability and effectiveness of infrastructures within RPR in the European Research Area, thereby leading to improved protection of the public, patients, workers and the environment against exposure to ionising radiation.</p>","PeriodicalId":50068,"journal":{"name":"Journal of Radiological Protection","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147534211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generic neural network model for estimating exposure levels in industrial environments.","authors":"David Plets, Christos Apostolidis, Shanshan Wang, Blaž Valič, Luc Martens, Theodoros Samaras, Peter Gajšek","doi":"10.1088/1361-6498/ae6c32","DOIUrl":"10.1088/1361-6498/ae6c32","url":null,"abstract":"<p><p>This study describes a neural network-based method for estimating exposure levels in industrial environments, without requiring detailed technical inputs, allowing usage of the model by layman people or by workers active in these areas. A pipeline based on Blender environments and MATLAB ray-tracing simulations is created and after defining a set of 11 candidate input parameters for the model, more than 20 000 different wireless configurations are simulated, varying the different environmental and wireless input parameters. A correlation analysis shows that main inputs influencing the exposure levels in the industrial area are the transmit power of the antennas, the density of clutter in the area, the density of transmitters in the area, and the height and location of the transmitters. A multi-layer fully connected neural network regression model is developed to predict median (<i>E</i>₅₀) and 95th percentile (<i>E</i>₉₅) exposure levels in industrial areas. Testing the obtained model on an unseen dataset of environments with<i>E</i>₅₀values between 0 and 3.25 V m^-1and<i>E</i>₉₅values between 0 and 7 V m^-1, demonstrates the good prediction performance of the model: root-mean-square error values below 0.173 V m^-1and<i>R</i>²values above 95% are obtained. Subsequently, the model is validated with measurement data collected in three distinct realistic industrial environments. The average absolute deviation of the model predictions with respect to the measurements is limited to 20.4%. This novel and broadly accessible approach demonstrates that it is possible to reliably estimate exposure levels in realistic environments without having to rely on external experts or on dedicated complex software.</p>","PeriodicalId":50068,"journal":{"name":"Journal of Radiological Protection","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147935106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The integration of artificial intelligence in radiation medical physics: insights from an international survey with regional variability.","authors":"Amal Yousif Al-Yasiri","doi":"10.1088/1361-6498/ae6d09","DOIUrl":"10.1088/1361-6498/ae6d09","url":null,"abstract":"<p><p>Artificial intelligence (AI) is considered to be a leading technology in radiation medical physics, which has the potential for improving efficiency and precision in imaging, radiotherapy, and nuclear medicine. Nonetheless, its application in the clinical setting is hindered by education, regulation, and ethical issues. To evaluate the views of medical physicists on AI adoption, potential issues, perceived advantages, ethical issues, and training needs. A cross-sectional survey in the form of an online questionnaire was distributed internationally to practicing medical physicists, representing specialties in radiation therapy, diagnostic imaging, and nuclear medicine. The instrument was used to capture demographic variables and AI familiarity, perceived benefits and barriers, ethical issues, training preferences, and future expectations. Descriptive statistics, chi-square tests, and binary logistic regression were performed. Open-ended responses were analyzed using thematic analysis. Most respondents were moderately familiar with AI, but nearly half had not incorporated AI into their practice despite being interested. Adoption patterns differed significantly across geographic regions, with higher usage reported in developed countries. AI familiarity was strongly associated with adoption. The most prominent benefits noted were dose calculation accuracy, patient safety, and workflow efficiency. In contrast, the main obstacles were inadequate training, high implementation costs, absence of standardized protocols, and low availability of advanced equipment. Ethical apprehensions focused on accountability and reduced human oversight. Preferred educational strategies included hands-on workshops and on-the-job training. Regional analysis and modeling revealed variability in AI adoption and identified key predictors, including AI familiarity and specialization. Results indicate that AI is considered an important technology in radiation medical physics. However, implementation was influenced by regional and professional differences. These findings highlight the necessity to expand AI education, create standard training programs, improve infrastructure, and provide clearer governance frameworks to support safe and responsible AI integration.</p>","PeriodicalId":50068,"journal":{"name":"Journal of Radiological Protection","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147935087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exposure and impact: highlights from the second scientific conference and recent activities of the International Society of Radiation Epidemiology and Dosimetry (ISoRED).","authors":"Richard Harbron, Keith T Griffin, Isaf Al-Nabulsi, Pablo Andres, Marie-Odile Bernier, Amy Berrington de González, Catherine Chassin, Christelle Chua En Lin, Cato Milder, Sergio Morató, Elisa Pasqual, André Rose, Suman Shrestha, Isabelle Thierry-Chef, Abigail Ukwuani, Lydia Wilson, Evgenia Ostroumova, Matthew M Mille","doi":"10.1088/1361-6498/ae6516","DOIUrl":"10.1088/1361-6498/ae6516","url":null,"abstract":"<p><p>The International Society of Radiation Epidemiology and Dosimetry (ISoRED) held its second scientific conference on 9-11 September 2025 in Lyon, France, hosted by the International Agency for Research on Cancer (IARC). The meeting brought together a multidisciplinary community of researchers to present recent advances in radiation epidemiology, dosimetry, and risk modelling, while fostering international collaboration and supporting early career investigators. The hybrid format enabled broad global participation, with attendees from 16 countries representing academic, governmental, and clinical institutions. This article summarises key scientific, professional, and social highlights of the conference and provides an update on recent activities of the society. The conference programme and a compilation of abstracts are included in the supplementary material.</p>","PeriodicalId":50068,"journal":{"name":"Journal of Radiological Protection","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13222722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147787579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk perception and behavioural intentions in a nuclear host community: evidence from Wolsong, South Korea.","authors":"Kyung Hee Kim, Sachchi Baral Chitrakar, Kyungsik Kim, Youjin Hong, Sangjun Lee, Soseul Sung, Woojin Lim, Sue K Park, Jae Wook Choi","doi":"10.1088/1361-6498/ae6517","DOIUrl":"10.1088/1361-6498/ae6517","url":null,"abstract":"<p><p>Public risk perception plays a critical role in shaping social responses to nuclear power plants (NPPs), particularly in host regions experiencing persistent conflict over safety, waste management, and institutional governance. The Wolsong NPP in South Korea represents a unique case, marked by long-standing opposition, policy disputes over spent fuel, and heightened health concern following the detection of radionuclides. Understanding how local residents perceive risk perception of radionuclides and how these perceptions translate into behavioural intentions such as migration, is critical for the Wolsong region. This study examines the demographic, psychological, and institutional determinants of risk perception of radionuclides and migration intentions among 1342 residents living within a 5 km radius of the plant, using cross-sectional survey data analysed via two sequential multivariable logistic regression models. Higher risk perception was associated with middle age, high school education, elevated anxiety, and, notably, greater recognition of the social benefits of nuclear power, while institutional trust and perceived policy transparency significantly reduced perceived risk. Migration intentions were reported by 13% of respondents and were more prevalent among younger residents, those with shorter residence duration, elevated stress levels, and higher risk perception. Conversely, institutional trust and perceived transparency substantially reduced migration intentions. These findings demonstrate that, in the context of Wolsong's conflict, risk perception functions as a key mechanism linking psychosocial distress, institutional trust, and population stability. Strengthening transparent governance, institutional credibility, and locally responsive risk communication is essential for improving community resilience in nuclear host regions.</p>","PeriodicalId":50068,"journal":{"name":"Journal of Radiological Protection","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147787638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast occupational upper-limb radiation dose prediction using machine learning and Monte Carlo simulation.","authors":"Hussein Harb, Kamilia Taguelmimt, Didier Benoit, Chi-Hieu Pham, Bahaa Nasr, Julien Bert","doi":"10.1088/1361-6498/ae63da","DOIUrl":"10.1088/1361-6498/ae63da","url":null,"abstract":"<p><p>Interventional procedures expose physicians to scattered radiation, particularly to their upper extremities, posing occupational health risks. Existing extremity dosimeters such as thermoluminescents, optically stimulated luminescence rings and active personal dosimeters provide limited spatial information, exhibit angular and energy dependence and offer little or no real-time feedback. This study develops machine learning (ML) models to estimate radiation dose values at discrete upper-limb locations using Monte Carlo (MC)-derived data and procedure-specific parameters. A dataset of 10 000 MC dose maps was generated under varied clinical and geometric conditions. After log-transformation and normalisation, several ML models, including deep neural networks and tree-based regressor, were trained and assessed using five-fold cross-validation. Mean absolute error and relative error (RE) were evaluated on the original dose scale, and an ensemble of the three best-performing models was constructed to improve robustness. The ensemble consistently outperformed individual models, achieving an average RE of 3.69% and demonstrating stable performance across anatomical regions and dose levels. Highest accuracy was obtained for standard beam geometries, whereas larger discrepancies occurred in extreme configurations with steep dose gradients. Predicted dose patterns were consistent with the expected distributions across the upper-limb regions. The findings demonstrate the feasibility of ML-based extremity dose estimation in interventional environments. The proposed ensemble provides a rapid (around 10 ms), scalable alternative to full MC simulations, enabling near real-time predictions of upper-limb occupational dose and supporting optimisation of radiation protection practices in image-guided procedures.</p>","PeriodicalId":50068,"journal":{"name":"Journal of Radiological Protection","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147787553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pediatric chest radiography in Fukushima prefecture: a multicentre organ dose assessment and optimization analysis.","authors":"Yoshiaki Hirofuji, Hazuki Manome, Koyo Sugai, Momoka Abe, Takaomi Harada","doi":"10.1088/1361-6498/ae5f27","DOIUrl":"10.1088/1361-6498/ae5f27","url":null,"abstract":"<p><p>Pediatric patients are highly radiosensitive, and technical variation in chest radiography can substantially affect dose. Fukushima prefecture also faces persistent post-disaster radiation concerns and declining pediatric imaging experience due to demographic change. A 5 year-old anthropomorphic phantom with 50 radiophotoluminescent dosimeters was examined at 22 facilities. Organ absorbed doses and entrance surface air kerma (ESAK) were directly measured, and effective dose was calculated using International Commission on Radiological Protection Publication 103 tissue weighting factors. One statistical outlier identified by Tukey's extreme outlier criterion was excluded from outlier-excluded analyses. Across 22 facilities, ESAK ranged from 0.03 to 1.25 mGy (median 0.12 mGy) and effective dose from 3.4 to 180.1<i>μ</i>Sv (median 27.3<i>μ</i>Sv). After exclusion of one outlier, ESAK ranged from 0.03 to 0.26 mGy (median 0.12 mGy; 9-fold variation) and effective dose from 3.4 to 94.2<i>μ</i>Sv (median 27.2<i>μ</i>Sv; 28-fold variation). The 75th-percentile ESAK was 0.137 mGy, 2.5% below Japan diagnostic reference levels 2025 (0.14 mGy), although 5 of 21 facilities (23.8%) exceeded this level. Median effective dose was 1.4-fold higher than the international reference value (20<i>μ</i>Sv). Compared with posterior-anterior (PA) projection, anterior-posterior projection showed higher breast dose (151.8 ± 74.3 vs 18.6 ± 10.7<i>μ</i>Gy;<i>p</i>= 0.0011) and higher effective dose (55.9 ± 29.6 vs 21.7 ± 11.5<i>μ</i>Sv;<i>p</i>= 0.0059); the stomach dose difference was exploratory (<i>p</i>= 0.025), whereas the thyroid dose difference was not statistically significant (<i>p</i>= 0.050). Tube current-time product correlated positively with effective dose (<i>r</i>= 0.67,<i>p</i>< 0.001). Marked inter-facility variation indicates substantial opportunities for optimization through greater use of PA projection, appropriate source-to-image receptor distance extension, and careful mAs adjustment. In post-disaster regions with declining pediatric imaging experience, radiation protection may require integrated technical, educational, and societal support.</p>","PeriodicalId":50068,"journal":{"name":"Journal of Radiological Protection","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147677312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}