Pub Date : 2025-02-04DOI: 10.1109/TRPMS.2025.3530624
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Pub Date : 2025-02-04DOI: 10.1109/TRPMS.2025.3530622
{"title":"IEEE Transactions on Radiation and Plasma Medical Sciences Publication Information","authors":"","doi":"10.1109/TRPMS.2025.3530622","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3530622","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 2","pages":"C3-C3"},"PeriodicalIF":4.6,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870459","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106264","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}
Pub Date : 2025-01-02DOI: 10.1109/TRPMS.2024.3519397
{"title":"IEEE Transactions on Radiation and Plasma Medical Sciences Information for Authors","authors":"","doi":"10.1109/TRPMS.2024.3519397","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3519397","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 1","pages":"C2-C2"},"PeriodicalIF":4.6,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820139","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912397","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}
Pub Date : 2025-01-02DOI: 10.1109/TRPMS.2024.3519395
{"title":"IEEE Transactions on Radiation and Plasma Medical Sciences Publication Information","authors":"","doi":"10.1109/TRPMS.2024.3519395","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3519395","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 1","pages":"C3-C3"},"PeriodicalIF":4.6,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10820081","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912528","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}
Pub Date : 2024-11-25DOI: 10.1109/TRPMS.2024.3483528
{"title":"2024 Index IEEE Transactions on Radiation and Plasma Medical Sciences Vol. 8","authors":"","doi":"10.1109/TRPMS.2024.3483528","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3483528","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"1-20"},"PeriodicalIF":4.6,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10766874","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142713905","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}
Pub Date : 2024-11-05DOI: 10.1109/TRPMS.2024.3475531
{"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}
Pub Date : 2024-11-05DOI: 10.1109/TRPMS.2024.3475533
{"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}
Pub Date : 2024-10-03DOI: 10.1109/TRPMS.2024.3471251
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
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.
{"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}
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.
{"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}
Pub Date : 2024-09-30DOI: 10.1109/TRPMS.2024.3470836
Hideaki Tashima;Taiga Yamaya
Three-gamma imaging is attracting attention as a futuristic diagnostic imaging method that surpasses positron emission tomography (PET). Its conceptual key is using $beta ^{+}$ - $gamma $ 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.
{"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}
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