Pub Date : 2024-03-11DOI: 10.1109/TRPMS.2024.3372189
Aafke Christine Kraan;Alberto Del Guerra
In the last decades, important technological progress has been made to enhance the quality and efficiency of particle therapy treatments. Continuous improvements in dose delivery, treatment planning and verification techniques have led to higher-dose conformity and better sparing of healthy tissue. At the same time, particle therapy treatments are complex and much more expensive than conventional radiotherapy, and only highly specialized facilities can offer these treatments. Cost reduction is thus a strong drive behind technological developments in the field. The number of treatment facilities offering proton and carbon therapy has strongly grown in the last decades, and the amount of research efforts and innovations have increased continuously. From a technological perspective, advances in hardware are often accompanied by innovations in software and computation, and vice versa. In this review we will present a basic overview of technological advances in particle therapy hardware (accelerators, gantries, applications of superconductivity, treatment verification techniques), software (Monte Carlo simulations, treatment planning calculations), and studies toward clinical applications. By combining a broad selection of topics into a single review and by covering both proton and carbon therapy, we aim at providing the reader a unique overview of the evolution of various technologies developed for particle therapy.
{"title":"Technological Developments and Future Perspectives in Particle Therapy: A Topical Review","authors":"Aafke Christine Kraan;Alberto Del Guerra","doi":"10.1109/TRPMS.2024.3372189","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3372189","url":null,"abstract":"In the last decades, important technological progress has been made to enhance the quality and efficiency of particle therapy treatments. Continuous improvements in dose delivery, treatment planning and verification techniques have led to higher-dose conformity and better sparing of healthy tissue. At the same time, particle therapy treatments are complex and much more expensive than conventional radiotherapy, and only highly specialized facilities can offer these treatments. Cost reduction is thus a strong drive behind technological developments in the field. The number of treatment facilities offering proton and carbon therapy has strongly grown in the last decades, and the amount of research efforts and innovations have increased continuously. From a technological perspective, advances in hardware are often accompanied by innovations in software and computation, and vice versa. In this review we will present a basic overview of technological advances in particle therapy hardware (accelerators, gantries, applications of superconductivity, treatment verification techniques), software (Monte Carlo simulations, treatment planning calculations), and studies toward clinical applications. By combining a broad selection of topics into a single review and by covering both proton and carbon therapy, we aim at providing the reader a unique overview of the evolution of various technologies developed for particle therapy.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 5","pages":"453-481"},"PeriodicalIF":4.4,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10466736","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820217","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 : 2024-03-08DOI: 10.1109/TRPMS.2024.3398360
Haiyan Wang;Han Jiang;Gefei Chen;Yu Du;Zhonglin Lu;Zhanli Hu;Greta S. P. Mok
Striatum segmentation on dopamine transporter (DaT) SPECT is necessary to quantify striatal uptake for Parkinson’s disease (PD), but is challenging due to the inferior resolution. This work proposes a cross-modality automatic striatum segmentation, estimating MR-derived striatal contours from clinical SPECT images using the deep learning (DL) methods. 123 I-Ioflupane DaT SPECT and T1-weighted MR images from 200 subjects with 152 PD and 48 healthy controls are analyzed from the Parkinson’s progression markers initiative database. SPECT and MR images are registered, and four striatal compartment contours are manually segmented from MR images as the label. DL methods including nnU-Net, U-Net, generative adversarial networks, and SPECT thresholding-based method are implemented for comparison. SPECT and MR label pairs are split into train, validation, and test groups (136:24:40). Dice, Hausdorff distance (HD) 95%, and relative volume difference (RVD), striatal binding ratio (SBR) and asymmetry index (ASI) are analyzed. Results show that nnU-Net achieves better Dice (~0.7), HD 95% (~1.8), and RVD (~0.1) as compared to other methods for all striatal compartments and whole striatum. For clinical PD evaluation, nnU-Net also yields strong SBR consistency (mean difference, −0.012) and ASI correlation (Pearson correlation coefficient, 0.81). The proposed DL-based cross-modality striatum segmentation method is feasible for clinical DaT SPECT in PD.
{"title":"Deep-Learning-Based Cross-Modality Striatum Segmentation for Dopamine Transporter SPECT in Parkinson’s Disease","authors":"Haiyan Wang;Han Jiang;Gefei Chen;Yu Du;Zhonglin Lu;Zhanli Hu;Greta S. P. Mok","doi":"10.1109/TRPMS.2024.3398360","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3398360","url":null,"abstract":"Striatum segmentation on dopamine transporter (DaT) SPECT is necessary to quantify striatal uptake for Parkinson’s disease (PD), but is challenging due to the inferior resolution. This work proposes a cross-modality automatic striatum segmentation, estimating MR-derived striatal contours from clinical SPECT images using the deep learning (DL) methods. \u0000<sup>123</sup>\u0000I-Ioflupane DaT SPECT and T1-weighted MR images from 200 subjects with 152 PD and 48 healthy controls are analyzed from the Parkinson’s progression markers initiative database. SPECT and MR images are registered, and four striatal compartment contours are manually segmented from MR images as the label. DL methods including nnU-Net, U-Net, generative adversarial networks, and SPECT thresholding-based method are implemented for comparison. SPECT and MR label pairs are split into train, validation, and test groups (136:24:40). Dice, Hausdorff distance (HD) 95%, and relative volume difference (RVD), striatal binding ratio (SBR) and asymmetry index (ASI) are analyzed. Results show that nnU-Net achieves better Dice (~0.7), HD 95% (~1.8), and RVD (~0.1) as compared to other methods for all striatal compartments and whole striatum. For clinical PD evaluation, nnU-Net also yields strong SBR consistency (mean difference, −0.012) and ASI correlation (Pearson correlation coefficient, 0.81). The proposed DL-based cross-modality striatum segmentation method is feasible for clinical DaT SPECT in PD.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 7","pages":"752-761"},"PeriodicalIF":4.6,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10525203","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142143654","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 : 2024-03-08DOI: 10.1109/TRPMS.2024.3374207
Hao Sun;Yu Du;Ching-Ni Lin;Han Jiang;Wenbo Huang;Pai-Yi Chiu;Guang-Uei Hung;Lijun Lu;Greta S. P. Mok
This study aims to investigate robust attenuation correction (AC) by generating attenuation maps $(mu $ -maps) from nonattenuation-corrected (NAC) brain SPECT data using transfer learning (TL). Four sets of brain SPECT data ( $4times 30$ ) were retrospectively collected: S-TRODAT-1, S-ECD, G-TRODAT-1, and G-ECD. A 3-D attention-based conditional generative adversarial network was pretrained using 22 paired 3-D NAC SPECT images and corresponding CT $mu $ -maps for four patient groups. Various numbers ( $n,,=$ 4–22) of paired NAC SPECT and corresponding $mu $ -maps from S-TRODAT-1 were then used to fine-tune (FT) the other three pretrained deep learning (DL) networks, i.e., S-ECD, G-TRODAT-1, and G-ECD. All patients in S-TRODAT-1 group were tested on their own network (DL-AC), and on the pretrained models with FT (FT-AC) and without FT (NFT-AC). The FT-AC methods used 22 (FT22), 12 (FT12), 8 (FT8), and 4 (FT4) paired data for FT, respectively. Our results show that FT22 and FT12 could outperform DL-AC for cross-tracer S-ECD and cross-scanner G-TRODAT-1 using CT-based AC (CT-AC) as the reference. FT22 also outperforms DL-AC for cross-tracer+cross-scanner G-ECD. FT8 performs comparably to DL-AC, while FT4 is worse than DL-AC but still better than NAC and NFT-AC in each group. Attenuation map generation is feasible for brain SPECT based on cross-tracer and/or cross-scanner FT-AC using a smaller number of patient data. The FT-AC performance improves as the number of data used for FT increases.
{"title":"Cross-Tracer and Cross-Scanner Transfer Learning-Based Attenuation Correction for Brain SPECT","authors":"Hao Sun;Yu Du;Ching-Ni Lin;Han Jiang;Wenbo Huang;Pai-Yi Chiu;Guang-Uei Hung;Lijun Lu;Greta S. P. Mok","doi":"10.1109/TRPMS.2024.3374207","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3374207","url":null,"abstract":"This study aims to investigate robust attenuation correction (AC) by generating attenuation maps \u0000<inline-formula> <tex-math>$(mu $ </tex-math></inline-formula>\u0000-maps) from nonattenuation-corrected (NAC) brain SPECT data using transfer learning (TL). Four sets of brain SPECT data (\u0000<inline-formula> <tex-math>$4times 30$ </tex-math></inline-formula>\u0000) were retrospectively collected: S-TRODAT-1, S-ECD, G-TRODAT-1, and G-ECD. A 3-D attention-based conditional generative adversarial network was pretrained using 22 paired 3-D NAC SPECT images and corresponding CT \u0000<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>\u0000-maps for four patient groups. Various numbers (\u0000<inline-formula> <tex-math>$n,,=$ </tex-math></inline-formula>\u0000 4–22) of paired NAC SPECT and corresponding \u0000<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>\u0000-maps from S-TRODAT-1 were then used to fine-tune (FT) the other three pretrained deep learning (DL) networks, i.e., S-ECD, G-TRODAT-1, and G-ECD. All patients in S-TRODAT-1 group were tested on their own network (DL-AC), and on the pretrained models with FT (FT-AC) and without FT (NFT-AC). The FT-AC methods used 22 (FT22), 12 (FT12), 8 (FT8), and 4 (FT4) paired data for FT, respectively. Our results show that FT22 and FT12 could outperform DL-AC for cross-tracer S-ECD and cross-scanner G-TRODAT-1 using CT-based AC (CT-AC) as the reference. FT22 also outperforms DL-AC for cross-tracer+cross-scanner G-ECD. FT8 performs comparably to DL-AC, while FT4 is worse than DL-AC but still better than NAC and NFT-AC in each group. Attenuation map generation is feasible for brain SPECT based on cross-tracer and/or cross-scanner FT-AC using a smaller number of patient data. The FT-AC performance improves as the number of data used for FT increases.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 6","pages":"664-676"},"PeriodicalIF":4.6,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10461117","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500371","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 : 2024-03-06DOI: 10.1109/TRPMS.2024.3397318
Mojtaba Jafaritadi;Emily Anaya;Garry Chinn;Jarrett Rosenberg;Tie Liang;Craig S. Levin
We present a context-aware generative deep learning framework to produce photon attenuation and scatter corrected (ASC) positron emission tomography (PET) images directly from nonattenuation and nonscatter corrected (NASC) images. We trained conditional generative adversarial networks (cGANs) on either single-modality (NASC) or multimodality (NASC+MRI) input data to map NASC images to pixel-wise continuously valued ASC PET images. We designed and evaluated four cGAN models including Pix2Pix, attention-guided cGAN (AG-Pix2Pix), vision transformer cGAN (ViT-GAN), and shifted window transformer cGAN (Swin-GAN). Retrospective 18F-fluorodeoxyglucose (18F-FDG) full-body PET images from 33 subjects were collected and analyzed. Notably, as a particular strength of this work, each patient in the study underwent both a PET/CT scan and a multisequence PET/MRI scan on the same day giving us a gold standard from the former as we investigate ASC for the latter. Quantitative analysis, evaluating image quality using peak signal-to-noise ratio (PSNR), multiscale structural similarity index (MS-SSIM), normalized mean-squared error (NRMSE), and mean absolute error (MAE) metrics, showed no significant impact of input type on PSNR ( $p=0.95$ ), MS-SSIM ( $p=0.083$ ), NRMSE ( $p=0.72$ ), or MAE ( $p=0.70$ ). For multimodal input data, Swin-GAN outperformed Pix2Pix ( $p=0.023$ ) and AG-Pix2Pix ( $p lt 0.001$ ), but not ViT-GAN ( $p=0.154$ ) in PSNR. Swin-GAN achieved significantly higher MS-SSIM than ViT-GAN ( $p=0.007$ ) and AG-Pix2Pix ( $p=0.002$ ). Multimodal Swin-GAN demonstrated reduced NRMSE and MAE compared to ViT-GAN ( $p=0.023$ and 0.031, respectively) and AG-Pix2Pix (both $p lt 0.001$ ), with marginal improvement over Pix2Pix ( $p lt 0.064$ ). The cGAN models, in particular Swin-GAN, consistently generated reliable and accurate ASC PET images, whether using multimodal or single-modal input data. The findings indicate that this methodology can be used to generate ASC data from standalone PET scanners or integrated PET/MRI systems, without relying on transmission scan-based attenuation maps.
{"title":"Context-Aware Transformer GAN for Direct Generation of Attenuation and Scatter Corrected PET Data","authors":"Mojtaba Jafaritadi;Emily Anaya;Garry Chinn;Jarrett Rosenberg;Tie Liang;Craig S. Levin","doi":"10.1109/TRPMS.2024.3397318","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3397318","url":null,"abstract":"We present a context-aware generative deep learning framework to produce photon attenuation and scatter corrected (ASC) positron emission tomography (PET) images directly from nonattenuation and nonscatter corrected (NASC) images. We trained conditional generative adversarial networks (cGANs) on either single-modality (NASC) or multimodality (NASC+MRI) input data to map NASC images to pixel-wise continuously valued ASC PET images. We designed and evaluated four cGAN models including Pix2Pix, attention-guided cGAN (AG-Pix2Pix), vision transformer cGAN (ViT-GAN), and shifted window transformer cGAN (Swin-GAN). Retrospective 18F-fluorodeoxyglucose (18F-FDG) full-body PET images from 33 subjects were collected and analyzed. Notably, as a particular strength of this work, each patient in the study underwent both a PET/CT scan and a multisequence PET/MRI scan on the same day giving us a gold standard from the former as we investigate ASC for the latter. Quantitative analysis, evaluating image quality using peak signal-to-noise ratio (PSNR), multiscale structural similarity index (MS-SSIM), normalized mean-squared error (NRMSE), and mean absolute error (MAE) metrics, showed no significant impact of input type on PSNR (\u0000<inline-formula> <tex-math>$p=0.95$ </tex-math></inline-formula>\u0000), MS-SSIM (\u0000<inline-formula> <tex-math>$p=0.083$ </tex-math></inline-formula>\u0000), NRMSE (\u0000<inline-formula> <tex-math>$p=0.72$ </tex-math></inline-formula>\u0000), or MAE (\u0000<inline-formula> <tex-math>$p=0.70$ </tex-math></inline-formula>\u0000). For multimodal input data, Swin-GAN outperformed Pix2Pix (\u0000<inline-formula> <tex-math>$p=0.023$ </tex-math></inline-formula>\u0000) and AG-Pix2Pix (\u0000<inline-formula> <tex-math>$p lt 0.001$ </tex-math></inline-formula>\u0000), but not ViT-GAN (\u0000<inline-formula> <tex-math>$p=0.154$ </tex-math></inline-formula>\u0000) in PSNR. Swin-GAN achieved significantly higher MS-SSIM than ViT-GAN (\u0000<inline-formula> <tex-math>$p=0.007$ </tex-math></inline-formula>\u0000) and AG-Pix2Pix (\u0000<inline-formula> <tex-math>$p=0.002$ </tex-math></inline-formula>\u0000). Multimodal Swin-GAN demonstrated reduced NRMSE and MAE compared to ViT-GAN (\u0000<inline-formula> <tex-math>$p=0.023$ </tex-math></inline-formula>\u0000 and 0.031, respectively) and AG-Pix2Pix (both \u0000<inline-formula> <tex-math>$p lt 0.001$ </tex-math></inline-formula>\u0000), with marginal improvement over Pix2Pix (\u0000<inline-formula> <tex-math>$p lt 0.064$ </tex-math></inline-formula>\u0000). The cGAN models, in particular Swin-GAN, consistently generated reliable and accurate ASC PET images, whether using multimodal or single-modal input data. The findings indicate that this methodology can be used to generate ASC data from standalone PET scanners or integrated PET/MRI systems, without relying on transmission scan-based attenuation maps.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 6","pages":"677-689"},"PeriodicalIF":4.6,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10521624","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500327","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 : 2024-03-04DOI: 10.1109/TRPMS.2024.3369272
{"title":"Member Get-A-Member (MGM) Program","authors":"","doi":"10.1109/TRPMS.2024.3369272","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3369272","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 3","pages":"331-331"},"PeriodicalIF":4.4,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10459099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140031650","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 : 2024-03-04DOI: 10.1109/TRPMS.2024.3366371
{"title":"IEEE Transactions on Radiation and Plasma Medical Sciences Information for Authors","authors":"","doi":"10.1109/TRPMS.2024.3366371","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3366371","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 3","pages":"C3-C3"},"PeriodicalIF":4.4,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10459069","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140031633","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 : 2024-03-04DOI: 10.1109/TRPMS.2024.3369270
{"title":"IEEE Data Port","authors":"","doi":"10.1109/TRPMS.2024.3369270","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3369270","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 3","pages":"332-332"},"PeriodicalIF":4.4,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10459100","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140031609","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 : 2024-03-04DOI: 10.1109/TRPMS.2024.3366373
{"title":"IEEE Transactions on Radiation and Plasma Medical Sciences Publication Information","authors":"","doi":"10.1109/TRPMS.2024.3366373","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3366373","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 3","pages":"C2-C2"},"PeriodicalIF":4.4,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10459103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140031569","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 : 2024-03-02DOI: 10.1109/TRPMS.2024.3390829
{"title":"Member Get-A-Member (MGM) Program","authors":"","doi":"10.1109/TRPMS.2024.3390829","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3390829","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 5","pages":"580-580"},"PeriodicalIF":4.4,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10517731","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820218","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 : 2024-03-02DOI: 10.1109/TRPMS.2024.3390831
{"title":"IEEE Nuclear Science Symposium","authors":"","doi":"10.1109/TRPMS.2024.3390831","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3390831","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 5","pages":"579-579"},"PeriodicalIF":4.4,"publicationDate":"2024-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10517730","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820336","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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