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":"Three-Gamma Imaging in Nuclear Medicine: A Review","authors":"Hideaki Tashima;Taiga Yamaya","doi":"10.1109/TRPMS.2024.3470836","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3470836","url":null,"abstract":"Three-gamma imaging is attracting attention as a futuristic diagnostic imaging method that surpasses positron emission tomography (PET). Its conceptual key is using \u0000<inline-formula> <tex-math>$beta ^{+}$ </tex-math></inline-formula>\u0000-\u0000<inline-formula> <tex-math>$gamma $ </tex-math></inline-formula>\u0000 nuclides that simultaneously emit a prompt gamma ray with the positron decay. In this review, we have categorized the utilizations of prompt gamma rays into three categories: 1) multiple positron emitter imaging; 2) reconstruction-less positron emission imaging; and 3) positronium lifetime imaging. Multiple positron emitter imaging utilizes the prompt gamma ray as a trigger to discriminate from signals of pure positron emitters to enable simultaneous injection and imaging of two different radioisotopes. Reconstruction-less positron emission imaging combines PET and Compton imaging technologies to estimate the source position as almost a point for each triple coincidence event. Positronium lifetime imaging utilizes the prompt gamma ray as a starting signal to measure the time difference between positronium formation and annihilation for each triple coincidence event as its lifetime. This is because the positronium lifetime is affected by the surrounding microenvironment of electrons, it is expected to provide new information regarding biological conditions, such as the hypoxia state. In this review we introduce the principles of the three categories of three-gamma imaging methods, prototype development, and demonstration experiments.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"853-866"},"PeriodicalIF":4.6,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10700810","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587582","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.2024.3449313","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3449313","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"C3-C3"},"PeriodicalIF":4.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669124","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143580","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.2024.3449311","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3449311","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"C2-C2"},"PeriodicalIF":4.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669129","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143652","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.2024.3453689","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3453689","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"850-850"},"PeriodicalIF":4.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669127","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143609","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 DataPort","authors":"","doi":"10.1109/TRPMS.2024.3453691","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3453691","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"851-851"},"PeriodicalIF":4.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10669128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143678","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":"Deep Learning-Based Fast Volumetric Image Generation for Image-Guided Proton Radiotherapy","authors":"Chih-Wei Chang;Yang Lei;Tonghe Wang;Sibo Tian;Justin Roper;Liyong Lin;Jeffrey Bradley;Tian Liu;Jun Zhou;Xiaofeng Yang","doi":"10.1109/TRPMS.2024.3439585","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3439585","url":null,"abstract":"Very fast imaging techniques can enhance the precision of image-guided radiation therapy, which can be useful for external beam radiation therapy. This work aims to develop a deep learning (DL)-based image-guide framework to enable fast volumetric image reconstruction for accurate target localization for treating lung cancer patients with gating, and it is presented in the context of FLASH which leverages ultrahigh dose-rate radiation to enhance the sparing of organs at risk without compromising tumor control probability. The proposed framework comprises four modules, including orthogonal kV X-ray projection acquisition, DL-based volumetric image generation, image quality analyses, and proton water equivalent thickness (WET) evaluation. We investigated volumetric image reconstruction using kV projection pairs with four different source angles. Thirty patients with lung targets were identified from an institutional database, each patient having a 4-D computed tomography (CT) dataset with ten respiratory phases. Considering all evaluation metrics, the kV projections with source angles of 135° and 225° yielded the optimal volumetric images. The patient-averaged mean absolute error, peak signal-to-noise ratio, structural similarity index measure, and WET error were \u0000<inline-formula> <tex-math>$75pm 22$ </tex-math></inline-formula>\u0000 hounsfield unit, \u0000<inline-formula> <tex-math>$19pm 3$ </tex-math></inline-formula>\u0000.7 dB, \u0000<inline-formula> <tex-math>$0.938pm 0.044$ </tex-math></inline-formula>\u0000, and −1.3%±4.1%. The proposed framework can rapidly deliver volumetric images to potentially guide proton FLASH treatment delivery systems.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"973-983"},"PeriodicalIF":4.6,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587625","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":"Experimental Measurement of Secondary Particle Count for Real-Time Proton Range Verification","authors":"Chuan Huang;Zhengguo Hu;Wei Lv;Yucong Chen;Xiuling Zhang;Zhiguo Xu;Faming Luo;Xinle Lang;Zulong Zhao;Ruishi Mao;Yongzhi Yin;Zhongming Wang;Di Wang;Guoqing Xiao","doi":"10.1109/TRPMS.2024.3439517","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3439517","url":null,"abstract":"The real-time positioning of the particle beam range during treatment is a critical technology for improving the quality of the patient treatment. This article presents a scheme for the real-time proton range verification, and an experimental prototype is built at the Xi’an proton application facility (XiPAF) terminal. The experiment utilized a 150 MeV passive proton beam delivery mode to bombard the polymethyl methacrylate (PMMA) target for the real-time proton range verification. This scheme utilizes the secondary particle counts generated per monitor unit (MU) of primary particles and does not require identification of the secondary particle species, only its deposition energy in the cerium bromide (CeBr3) scintillator module exceeding 73.24 keV. The accuracy of range verification was evaluated at various acquisition periods by establishing the relationship between the secondary particle counts generated per MU of primary particles and the proton range. The range verification accuracy after one spill (\u0000<inline-formula> <tex-math>$sim ~1.67times 10$ </tex-math></inline-formula>\u00009 protons) delivery was measured at \u0000<inline-formula> <tex-math>$0.01~pm ~0$ </tex-math></inline-formula>\u0000.29 mm. The accuracy of range verification within milliseconds is mainly affected by the statistical fluctuations in the secondary particle counts caused by the accumulation of activation products. Under constrained conditions, the range verification accuracy was measured at \u0000<inline-formula> <tex-math>$0.16~pm ~0$ </tex-math></inline-formula>\u0000.69 mm within 110 ms acquisition time and \u0000<inline-formula> <tex-math>$0.16~pm ~0$ </tex-math></inline-formula>\u0000.94 mm within 55 ms acquisition time. The experimental results confirm the feasibility of the scheme for the real-time range verification practice. The study hopes to provide a new reference scheme for reducing the impact of range uncertainty on the patient treatment quality.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"984-989"},"PeriodicalIF":4.6,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587539","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":"Deep Convolutional Backbone Comparison for Automated PET Image Quality Assessment","authors":"Jessica B. Hopson;Anthime Flaus;Colm J. McGinnity;Radhouene Neji;Andrew J. Reader;Alexander Hammers","doi":"10.1109/TRPMS.2024.3436697","DOIUrl":"10.1109/TRPMS.2024.3436697","url":null,"abstract":"Pretraining deep convolutional network mappings using natural images helps with medical imaging analysis tasks; this is important given the limited number of clinically annotated medical images. Many 2-D pretrained backbone networks, however, are currently available. This work compared 18 different backbones from 5 architecture groups (pretrained on ImageNet) for the task of assessing [18F]FDG brain positron emission tomography (PET) image quality (reconstructed at seven simulated doses), based on three clinical image quality metrics (global quality rating, pattern recognition, and diagnostic confidence). Using 2-D randomly sampled patches, up to eight patients (at three dose levels each) were used for training, with three separate patient datasets used for testing. Each backbone was trained five times with the same training and validation sets, and with six cross-folds. Training only the final fully connected layer (with ~6000–20000 trainable parameters) achieved a test mean-absolute-error (MAE) of ~0.5 (which was within the intrinsic uncertainty of clinical scoring). To compare “classical” and over-parameterized regimes, the pretrained weights of the last 40% of the network layers were then unfrozen. The MAE fell below 0.5 for 14 out of the 18 backbones assessed, including two that previously failed to train. Generally, backbones with residual units (e.g., DenseNets and ResNetV2s), were suited to this task, in terms of achieving the lowest MAE at test time (~0.45–0.5). This proof-of-concept study shows that over-parameterization may also be important for automated PET image quality assessments.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"893-901"},"PeriodicalIF":4.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477477","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}