IEEE Transactions on Radiation and Plasma Medical Sciences最新文献

{"title":"IEEE Transactions on Radiation and Plasma Medical Sciences Publication Information","authors":"","doi":"10.1109/TRPMS.2024.3475531","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3475531","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"C3-C3"},"PeriodicalIF":4.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10744627","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587633","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}

{"title":"IEEE Transactions on Radiation and Plasma Medical Sciences Information for Authors","authors":"","doi":"10.1109/TRPMS.2024.3475533","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3475533","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"C2-C2"},"PeriodicalIF":4.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10744626","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587518","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}

{"title":"Dedicated 3D-Printed Radioactive Phantoms With ¹⁸F-FDG for Ultrahigh-Resolution PET","authors":"Ezzat Elmoujarkach;Steven Seeger;Luise Morgner;Fabian Schmidt;Julia G. Mannheim;Christian L. Schmidt;Magdalena Rafecas","doi":"10.1109/TRPMS.2024.3483233","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3483233","url":null,"abstract":"This study explores the potential of digital light processing to 3-D print radioactive phantoms for high-resolution positron emission tomography (PET). Using a slightly modified desktop 3-D printer and mixtures of 18F-FDG (T1/2: 109.8 min) and photopolymer resin, we have printed standardized and custom radioactive objects designed for ultrahigh-resolution PET, also as a first step toward complex geometries. The phantoms were: a flood source to assess uniformity, a two-point phantom for spatial resolution assessment, a multiline phantom for validating submillimeter printing resolution, a fish-like phantom with different activity concentrations, and a 50%-downscaled micro-PET image quality phantom (National Electrical Manufacturers Association NU 4-2008). Positron range effects were examined on the latter using a removable cover. The evaluation relied on planar images from a phosphor imager and tomographic images from a commercial small animal PET scanner. We were able to print radioactive uniform distributions with relative standard deviation below 4.5% and structures as small as 0.3 mm. Our two-point phantom outperformed a commercial one in terms of peak difference (6% versus 72%) and peak-to-valley ratio (75.3 versus 14.1). The fish-like phantom shows that printing hot regions and air cavities onto a uniform background is feasible. Future steps include using longer-lived radionuclides like 89Zr and 22Na.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 3","pages":"362-371"},"PeriodicalIF":4.6,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10742298","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553287","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}

{"title":"TlCl:Be,I: A High Sensitivity Scintillation and Cherenkov Radiator for TOF-PET","authors":"Nicolaus Kratochwil;Nathaniel Kaneshige;Giulia Terragni;Roberto Cala;Jared Schott;Edgar van Loef;Lakshmi Soundara Pandian;Emilie Roncali;Jaroslaw Glodo;Etiennette Auffray;Gerard Ariño-Estrada","doi":"10.1109/TRPMS.2024.3487359","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3487359","url":null,"abstract":"The material requirements for gamma-ray detectors for medical imaging applications are multifold and sensitivity is often overlooked. High effective atomic number (Z<inline-formula> <tex-math>$_{text {eff}}$ </tex-math></inline-formula>) Cherenkov radiators have raised the attention in the community due to their potential for harvesting prompt photons. A material with one of the highest Zeff and thus short gamma-ray attenuation length is thallium chloride (TlCl). By doping TlCl with beryllium (Be) or iodine (I), it becomes a scintillator and therefore produces scintillation photons upon gamma-ray interaction on the top of the prompt Cherenkov luminescence. The scintillation response of TlCl:Be,I is investigated in terms of intensity, energy resolution, kinetics, and timing capability with and without energy discrimination. The ratio of prompt to slow scintillation photons is used to derive the intrinsic number of produced Cherenkov photons and compared with analytic calculations avoiding complex Monte Carlo simulations. The experimentally determined number of Cherenkov photons upon 511 keV gamma excitation of <inline-formula> <tex-math>$17.9~pm ~4.6$ </tex-math></inline-formula> photons is in line with our simple calculations yielding 14.5 photons. We observe three scintillation decay time components with an effective decay time of 60 ns. The scintillation light yield of 0.9 ph/keV is sufficient to discriminate events with low energy deposition in the crystal which is used to improve the measured coincidence time resolution from 360-ps FWHM without energy selection down to 235-ps after energy discrimination and time walk correction for 2.8-mm thick TlCl:Be,I crystals, and from 580 to 402 ps for 15.2-mm thick ones. Already with the first generation of doped TlCl encouraging timing capability close to other materials with lower effective atomic number has been achieved.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 3","pages":"296-303"},"PeriodicalIF":4.6,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10740386","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553121","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}

{"title":"Demonstration of the High Efficiency of an Air Plasma Jet Combining Electric Field and RONS in the Treatment of Chronic Wounds","authors":"Osvaldo Daniel Cortázar;Ana Megía-Macías;Bernardo Hontanilla;Hernán Cortázar-Gallicchio","doi":"10.1109/TRPMS.2024.3486975","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3486975","url":null,"abstract":"A cold atmospheric air plasma jet (CAAPJ) for the treatment of skin injuries in medicine and veterinary medicine is presented with experimental evidence that point to the electric field inside the plasmas jet could be a determinant therapeutic mechanism. The device is characterized by producing a CAAPJ compatible with living tissues at a low-heat transfer rate with a temperature on the skin surface below 40 °C. It has a practical design to be used by physicians and veterinaries during daily practice, with a special focus on the treatment of skin injuries and unhealed ulcers. Plasma diagnostics, including currents-voltage signals, UV-VIS spectroscopy, IR images of the skin, and electric field measurements in the air cold plasma jet, are presented. The last is made for the first time in this type of plasma, and them can justify the induction of local electric currents on the wound surface to accelerate healing by highlighting the possible synergy with reactive oxygen and nitrogen species (RONSs) as a decontaminant agent for bacteria (including resistant), fungi and viruses without damaging healthy tissue. A remarkable clinical case study example is reported.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 5","pages":"680-688"},"PeriodicalIF":4.6,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10736565","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900605","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}

{"title":"MaS-TransUNet: A Multiattention Swin Transformer U-Net for Medical Image Segmentation","authors":"Ashwini Kumar Upadhyay;Ashish Kumar Bhandari","doi":"10.1109/TRPMS.2024.3477528","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3477528","url":null,"abstract":"U-shaped encoder-decoder models have excelled in automatic medical image segmentation due to their hierarchical feature learning capabilities, robustness, and upgradability. Purely CNN-based models are excellent at extracting local details but struggle with long-range dependencies, whereas transformer-based models excel in global context modeling but have higher data and computational requirements. Self-attention-based transformers and other attention mechanisms have been shown to enhance segmentation accuracy in the encoder-decoder framework. Drawing from these challenges and opportunities, we propose a novel multiattention Swin transformer U-net (MaS-TransUNet) model, incorporating self-attention, edge attention, channel attention, and feedback attention. MaS-TransUNet leverages the strengths of both CNNs and transformers within a U-shaped encoder-decoder framework. For self-attention, we developed modules using Swin Transformer blocks, offering hierarchical feature representations. We designed specialized modules, including an edge attention module (EAM) to guide the network with edge information, a feedback attention module (FAM) to utilize previous epoch segmentation masks for refining subsequent predictions, and a channel attention module (CAM) to focus on relevant feature channels. We also introduced advanced data augmentation, regularizations, and an optimal training scheme for enhanced training. Comprehensive experiments across five diverse medical image segmentation datasets demonstrate that MaS-TransUNet significantly outperforms existing state-of-the-art methods while maintaining computational efficiency. It achieves the highest-Dice scores of 0.903, 0.841, 0.908, 0.906, and 0.906 on the Cancer genome atlas low-grade glioma Brain MRI, COVID-19 Lung CT, data science bowl-2018, Kvasir-SEG, and international skin imaging collaboration-2018 datasets, respectively. These results highlight the model’s robustness and versatility, consistently delivering exceptional performance without modality-specific adaptations.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 5","pages":"613-626"},"PeriodicalIF":4.6,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900588","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}

{"title":"A New Ensemble Transfer Learning Approach With Rejection Mechanism for Tuberculosis Disease Detection","authors":"Seng Hansun;Ahmadreza Argha;Hamid Alinejad-Rokny;Roohallah Alizadehsani;Juan M. Gorriz;Siaw-Teng Liaw;Branko G. Celler;Guy B. Marks","doi":"10.1109/TRPMS.2024.3474708","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3474708","url":null,"abstract":"Transfer learning (TL) is a strategic solution to handle vast data volume requirements in deep learning (DL). It transfers knowledge learned from a large base dataset, as a pretrained model (PTM), to a new domain. In this study, we introduce an ensemble of classifiers trained on features extracted from some intermediate layers of a PTM for Tuberculosis (TB) detection task. We use different EfficientNet variants: EfficientNet-B0–EfficientNet-B3, as the PTM. Moreover, we introduce a rejection mechanism and implement post-hoc calibration methods to enhance the reliability and trustworthiness of the developed models. Additionally, we conduct analyses on domain-shift distribution, a topic rarely discussed in the context of TB detection. Through a fivefold cross-validation on two prominent chest X-ray datasets, the Montgomery County (MC) and Shenzhen (SZ), our ensemble approach achieved competitive results with accuracies of 94.89% (MC) and 92.75% (SZ). The incorporation of the devised rejection mechanism resulted in enhanced model accuracy, albeit with a coverage tradeoff. In domain-shift experiments, the proposed approach achieved an accuracy of 83.57% (63% coverage) when applying the MC-trained model on SZ, and an accuracy of 88.50% (82% coverage) when applying the SZ-trained model on MC.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"433-446"},"PeriodicalIF":4.6,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706096","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761568","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}

{"title":"Semi-Supervised 3-D Medical Image Segmentation Using Multiconsistency Learning With Fuzzy Perception-Guided Target Selection","authors":"Tao Lei;Wenbiao Song;Weichuan Zhang;Xiaogang Du;Chenxia Li;Lifeng He;Asoke K. Nandi","doi":"10.1109/TRPMS.2024.3473929","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3473929","url":null,"abstract":"Semi-supervised learning methods based on the mean teacher model have achieved great success in the field of 3-D medical image segmentation. However, most of the existing methods provide auxiliary supervised signals only for reliable regions, but ignore the effect of fuzzy regions from unlabeled data during the process of consistency learning, which results in the loss of more valuable information. Besides, some of these methods only employ multitask learning to improve models’ performance, but ignore the role of consistency learning between tasks and models, thereby weakening geometric shape constraints. To address the above issues, in this article, we propose a semi-supervised 3-D medical image segmentation framework with multiconsistency learning for fuzzy perception-guided target selection. First, we design a fuzzy perception-guided target selection strategy from multiple perspectives and adopt the fusion method of fuzziness minimization and the fuzzy map momentum update to obtain a fuzzy region. By incorporating the fuzzy region into consistency learning, our model can effectively exploit more useful information from the fuzzy region of unlabeled data. Second, we design a multiconsistency learning strategy that employs intratask and intermodal mutual consistency learning as well as cross-model cross-task consistency learning to effectively learn the shape representation of fuzzy regions. The strategy can encourage the model to agree on predictions for different tasks in fuzzy regions. Experiments demonstrate that the proposed framework outperforms the current mainstream methods on two popular 3-D medical datasets, the left atrium segmentation dataset, and the brain tumor segmentation dataset. The code will be released at: <uri>https://github.com/SUST-reynole</uri>.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"421-432"},"PeriodicalIF":4.6,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706819","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761566","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}

{"title":"Toward a Second Generation of Metascintillators Using the Purcell Effect","authors":"A. Shultzman;R. Schütz;Y. Kurman;N. Lahav;G. Dosovitskiy;C. Roques-Carmes;Y. Bekenstein;G. Konstantinou;R. Latella;L. Zhang;F. Loignon-Houle;A. J. Gonzalez;J. M. Benlloch;I. Kaminer;P. Lecoq","doi":"10.1109/TRPMS.2024.3471251","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3471251","url":null,"abstract":"This study focuses on advancing metascintillators to break the 100 ps barrier and approach the 10 ps target. We exploitnanophotonic features, specifically the Purcell effect, to shape and enhance the scintillation properties of the first-generation metascintillator. We demonstrate that a faster emission is achievable along with a more efficient conversionefficiency. This results in a coincidence time resolution improved by a factor of 1.3, crucial for TOF-PET applications.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 2","pages":"141-147"},"PeriodicalIF":4.6,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704688","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106284","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}

{"title":"PrideDiff: Physics-Regularized Generalized Diffusion Model for CT Reconstruction","authors":"Zexin Lu;Qi Gao;Tao Wang;Ziyuan Yang;Zhiwen Wang;Hui Yu;Hu Chen;Jiliu Zhou;Hongming Shan;Yi Zhang","doi":"10.1109/TRPMS.2024.3471677","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3471677","url":null,"abstract":"Achieving a lower radiation dose and a faster imaging speed is a pivotal objective of computed tomography (CT) reconstruction. However, these often come at the cost of compromised reconstruction quality. With the advent of deep learning, numerous CT reconstruction methods rooted in this field have significantly improved the reconstruction performance. Recently, diffusion models have further enhanced training stability and imaging quality for CT. However, many of these methods only focus on CT image domain features, ignoring the intrinsic physical information of the imaging process. Although compressive sensing-based iterative reconstruction algorithms utilize physical prior information, their intricate iterative process poses challenges in training, subsequently influencing their efficiency. Motivated by these observations, we introduce a novel physics-regularized generalized diffusion model for CT reconstruction (PrideDiff). On the one hand, our method further improves the quality of reconstructed images by fusing physics-regularized iterative reconstruction methods with diffusion models. On the other hand, we propose a prior extraction module embedded with temporal features, which effectively improves the performance of the iteration process. Extensive experimental results demonstrate that PrideDiff outperforms several state-of-the-art methods in low-dose and sparse-view CT reconstruction tasks on different datasets, with faster reconstruction speed. We further discuss the effectiveness of relevant components in PrideDiff and validate the stability of the iterative reconstruction process, followed by detailed analysis of computational cost and inference time.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 2","pages":"157-168"},"PeriodicalIF":4.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10701005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106286","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}