IEEE Transactions on Radiation and Plasma Medical Sciences最新文献

{"title":"Exploring Cell Type-Specific Efficacy of Plasma-Activated Medium (PAM) on Endometrial Cancer Using Patient-Specific 2-D and 3-D cell Culture Systems (2024)","authors":"Eva Becker;Christina B. Walter;Juliane Scheid;Sara Y. Brucker;André Koch;Martin Weiss","doi":"10.1109/TRPMS.2024.3421601","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3421601","url":null,"abstract":"Endometrial cancer (EC) is the most common tumor of the female reproductive organs in industrialized nations and an increasingly frequent disease in premenopausal women, which necessitates the development of fertility-preserving alternative treatment modalities without radical hysterectomy. In this study, we progressively elucidate the cancer specific the impact of plasma activated media (PAM) on EC, transitioning from conventional single cell models to more clinically relevant patient-derived 3-D organoid systems of different tumor gradings compared to healthy endometrial tissue, emphasizing a novel experimental approach. Significantly, we demonstrate an increasing impact of PAM on patient-derived high-grade EC organoids accompanied with a dose-dependent rise in oxidative stress levels, contrasting with no alterations in healthy endometrial tissue. These findings collectively suggest that the application of plasma-activated liquid holds promise for expanding fertility-preserving therapies for endometrial carcinoma and contributing to future disease control. In conclusion, this research pioneers a patient-specific and stepwise investigation into the therapeutic potential of PAM on EC and contributes to the evolving landscape of personalized cancer therapies, offering promising avenues for future clinical applications.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 2","pages":"259-268"},"PeriodicalIF":4.6,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10589343","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106265","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.2024.3405098","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3405098","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 6","pages":"C3-C3"},"PeriodicalIF":4.6,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10584412","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500379","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":"Member Get-A-Member (MGM) Program","authors":"","doi":"10.1109/TRPMS.2024.3421769","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3421769","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 6","pages":"708-708"},"PeriodicalIF":4.6,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10584434","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500370","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 Publication Information","authors":"","doi":"10.1109/TRPMS.2024.3405100","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3405100","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 6","pages":"C2-C2"},"PeriodicalIF":4.6,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10584413","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500326","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":"Performance Investigations of Two Channel Readout Configurations on the Cross-Strip Cadmium Zinc Telluride Detector","authors":"Emily Enlow;Yuli Wang;Greyson Shoop;Shiva Abbaszadeh","doi":"10.1109/TRPMS.2024.3411522","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3411522","url":null,"abstract":"In a detector system where the number of channels exceeds the number of channels available on an application-specific integrated circuit (ASIC), there is a need to configure channels among the multiple ASICs to achieve the lowest electronic noise and highest count rate. In this work, two board configurations were designed to experimentally assess which one provides the more favorable performance. In the half-half configuration, contiguous channels from one edge to the center of CZT detector are read by one ASIC, and the other half are read by the other ASIC. In the alternate configuration, the CZT channels are read by alternating ASICs. A lower electronic noise level, better FWHM energy resolution performance (5.35% \u0000<inline-formula> <tex-math>$pm ~1.08$ </tex-math></inline-formula>\u0000% compared to 7.84% \u0000<inline-formula> <tex-math>$pm ~0.98$ </tex-math></inline-formula>\u0000%), and higher count rate was found for the anode electrode strips with the half-half configuration. Cross-talk between the ASICs and deadtime play a role in the different performances, and the total count rate of the half-half configuration has a count rate 18.1% higher than that of the alternate configuration.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"886-892"},"PeriodicalIF":4.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587583","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":"XGenRecon: A New Perspective in Ultrasparse Volumetric CBCT Reconstruction Through Geometry-Controlled X-Ray Projection Generation","authors":"Chulong Zhang;Yaoqin Xie;Xiaokun Liang","doi":"10.1109/TRPMS.2024.3420742","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3420742","url":null,"abstract":"We propose a novel paradigm for cone-beam computed tomography (CBCT) reconstruction from ultrasparse X-ray projections, by introducing a framework that generates auxiliary X-ray projections under controlled geometric parameters. This innovation overcomes the limitations of conventional methods that are constrained to producing fixed-angle projections. Our approach is organized into three key modules: 1) the XGen module; 2) X-Correction module; and 3) CT-Correction module. Through the XGen module, we generate projections based on any given geometric parameters to supplement the geometric information in the projection domain. The X-Correction module then introduces geometric corrections to harmonize the generated projections. Finally, through the CT-Correction module, the reconstructed image undergoes refining, thereby enhancing the image quality within the image domain. We have validated our model on several datasets, including a large-scale publicly available lung CT dataset (LIDC-IDRI with 1018 patients); an extensive abdominal CT dataset (AbdomenCT-1K, with a selected 1k patients); and our proprietary pelvic CT dataset, collated from a hospital (445 patients). Real walnut projection data were also incorporated for genuine projection validation. Compared to the traditional projection generation methods and the state-of-the-art ultrasparse reconstruction techniques on 2-view and 10-view tasks, our method has demonstrated consistently superior performance across various tasks.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 1","pages":"95-106"},"PeriodicalIF":4.6,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912533","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":"MAG-Net: A Multiscale Adaptive Generation Network for PET Synthetic CT","authors":"Huabin Wang;Zongguang Li;Xianjun Han;Gong Zhang;Qiang Zhang;Dailei Zhang;Fei Liu","doi":"10.1109/TRPMS.2024.3418831","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3418831","url":null,"abstract":"In traditional positron emission computed tomography (PET)/computed tomography (CT) imaging, CT can be used to accurately display lesion anatomical structure. However, CT is not available in single brain PET imaging system. Therefore, this article proposes a novel generation network (MAG-Net) for generating CT images with clear morphological details from PET. The MAG-Net contains three unique features: 1) a parallel multiscale adaptive module is designed to extract robust features of PET, which can improve the quality of the generated images with various resolutions; 2) a binarized contour mask module is applied to constrain the generating process of the fake CT. It can guide the model focusing on generating more CT texture details; and 3) a pixel-level feature encoder is designed to reduce the pixel difference and achieve the accuracy of generated CT by mapping the position information of CT tissues and structures corresponding to bright and dark areas. Experimental results on the SCHERI dataset show that compared with real CT images, structural similarity and PSNR index of generated images reach 0.909 and 26.386. The results of visualization experiments show that the generated CT has clear texture details and realistic morphological structure, which can make the single brain PET imaging system close to the PET/CT imaging system.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 1","pages":"83-94"},"PeriodicalIF":4.6,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912395","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":"Co-Learning Multimodality PET-CT Features via a Cascaded CNN-Transformer Network","authors":"Lei Bi;Xiaohang Fu;Qiufang Liu;Shaoli Song;David Dagan Feng;Michael Fulham;Jinman Kim","doi":"10.1109/TRPMS.2024.3417901","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3417901","url":null,"abstract":"<italic>Background:</i>\u0000 Automated segmentation of multimodality positron emission tomography—computed tomography (PET-CT) data is a major challenge in the development of computer-aided diagnosis systems (CADs). In this context, convolutional neural network (CNN)-based methods are considered as the state-of-the-art. These CNN-based methods, however, have difficulty in co-learning the complementary PET-CT image features and in learning the global context when focusing solely on local patterns. \u0000<italic>Methods:</i>\u0000 We propose a cascaded CNN-transformer network (CCNN-TN) tailored for PET-CT image segmentation. We employed a transformer network (TN) because of its ability to establish global context via self-attention and embedding image patches. We extended the TN definition by cascading multiple TNs and CNNs to learn the global and local contexts. We also introduced a hyper fusion branch that iteratively fuses the separately extracted complementary image features. We evaluated our approach, when compared to current state-of-the-art CNN methods, on three datasets: two nonsmall cell lung cancer (NSCLC) and one soft tissue sarcoma (STS). \u0000<italic>Results:</i>\u0000 Our CCNN-TN method achieved a dice similarity coefficient (DSC) score of 72.25% (NSCLC), 67.11% (NSCLC), and 66.36% (STS) for segmentation of tumors. Compared to other methods the DSC was higher for our CCNN-TN by 4.5%, 1.31%, and 3.44%. \u0000<italic>Conclusion:</i>\u0000 Our experimental results demonstrate that CCNN-TN, when compared to the existing methods, achieved more generalizable results across different datasets and has consistent performance across various image fusion strategies and network backbones.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"814-825"},"PeriodicalIF":4.6,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143751","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":"Toward Sub-100 ps TOF-PET Systems Employing the FastIC ASIC With Analog SiPMs","authors":"A. Mariscal-Castilla;S. Gómez;R. Manera;J. M. Fernández-Tenllado;J. Mauricio;N. Kratochwil;J. Alozy;M. Piller;S. Portero;A. Sanuy;D. Guberman;J. J. Silva;E. Auffray;R. Ballabriga;G. Ariño-Estrada;M. Campbell;D. Gascón","doi":"10.1109/TRPMS.2024.3414578","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3414578","url":null,"abstract":"Time of Flight positron emission tomography (TOF-PET) scanners demand electronics that are power-efficient, low-noise, cost-effective, and possess a large bandwidth. Recent developments have demonstrated sub-100 ps time resolution with elevated power consumption per channel, rendering this unfeasible to build a scanner. In this work, we evaluate the performance for the TOF-PET of the FastIC front-end using different scintillators and silicon photomultipliers (SiPMs). FastIC is an eight-channel application specific integrated circuit developed in CMOS 65 nm capable of measuring the energy and the arrival time of a detected pulse with 12 mW per channel. Using Hamamatsu SiPMs (S13360-3050PE) coupled to LSO:Ce:0.2%Ca crystals of \u0000<inline-formula> <tex-math>$2times 2times $ </tex-math></inline-formula>\u0000 3 mm\u0000³\u0000 and LYSO:Ce:0.2%Ca of \u0000<inline-formula> <tex-math>$3.13times 3.13times $ </tex-math></inline-formula>\u0000 20 mm\u0000³\u0000, we measured a coincidence time resolution (CTR) of (\u0000<inline-formula> <tex-math>$95~pm ~3$ </tex-math></inline-formula>\u0000) and \u0000<inline-formula> <tex-math>$156~pm ~4$ </tex-math></inline-formula>\u0000) ps full width half maximum (FWHM), respectively. With Fondazione Bruno Kessler NUV-HD LF2 M0 SiPMs coupled to the same crystals, we obtained a CTR of (\u0000<inline-formula> <tex-math>$76~pm ~2$ </tex-math></inline-formula>\u0000) and (\u0000<inline-formula> <tex-math>$127~pm ~3$ </tex-math></inline-formula>\u0000) ps FWHM. We employed FastIC with a TlCl pure Cherenkov emitter, demonstrating time resolutions comparable to those achieved with the high-power-consuming electronics. These findings shows that the FastIC represents a cost-effective alternative that can significantly enhance the time resolution of the current TOF-PET systems while maintaining low power consumption.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"718-733"},"PeriodicalIF":4.6,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10557761","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143649","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":"Accurate Whole-Brain Segmentation for Bimodal PET/MR Images via a Cross-Attention Mechanism","authors":"Wenbo Li;Zhenxing Huang;Qiyang Zhang;Na Zhang;Wenjie Zhao;Yaping Wu;Jianmin Yuan;Yang Yang;Yan Zhang;Yongfeng Yang;Hairong Zheng;Dong Liang;Meiyun Wang;Zhanli Hu","doi":"10.1109/TRPMS.2024.3413862","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3413862","url":null,"abstract":"The PET/MRI system plays a significant role in the functional and anatomical quantification of the brain, providing accurate diagnostic data for a variety of brain disorders. However, most of the current methods for segmenting the brain are based on unimodal MRI and rarely combine structural and functional dual-modality information. Therefore, we aimed to employ deep-learning techniques to achieve automatic and accurate segmentation of the whole brain while incorporating functional and anatomical information. To leverage dual-modality information, a novel 3-D network with a cross-attention module was proposed to capture the correlation between dual-modality features and improve segmentation accuracy. Moreover, several deep-learning methods were employed as comparison measures to evaluate the model performance, with the dice similarity coefficient (DSC), Jaccard index (JAC), recall, and precision serving as quantitative metrics. Experimental results demonstrated our advantages in whole-brain segmentation, achieving an 85.35% DSC, 77.22% JAC, 88.86% recall, and 84.81% precision, which were better than those comparative methods. In addition, consistent and correlated analyses based on segmentation results also demonstrated that our approach achieved superior performance. In future work, we will try to apply our method to other multimodal tasks, such as PET/CT data analysis.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 1","pages":"47-56"},"PeriodicalIF":4.6,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912440","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}