Micro-CT provides tomographic information for small animals and plays an important role in preclinical research. Currently, the most micro-CT systems employ the single-source configuration and low-dose micro-focus X-ray tube, resulting in long scanning time and weak low-contrast discriminative ability. To address the two vital issues, we design a novel rotation gantry mounted triple-source conebeam micro-CT for the first time. Specifically, three pairs of tubes and detectors are installed in a single gantry. Compared with a single source configuration, the proposal increases the scanning efficiency by three times without aggravating any mechanical burden. The low-contrast discrimination is improved by multimaterial decomposition scheme based on triple-energy CT (TECT) images. Experiments were conducted using both phantoms and live rats. In the high-speed study, the proposal has reasonable agreement in signal-to-noise ratio and MTF compared to single-source scanner, even with a threefold increase in scanning speed. In the low-contrast discrimination study, on the digital phantom, the TECT correctly discriminates the materials of 2% linear attenuation coefficient difference. On the live rat, the decomposition accuracy of TECT has been enhanced by up to 21.9% compared to single-energy CT. These results validate the promising of the proposal for high-speed and low-contrast discrimination applications.
{"title":"A Triple-Source Conebeam Micro-CT Scanner for Fast and Spectral Imaging","authors":"Peng Jin;Xianghong Wang;Huihui Li;Lei Xu;Zhiqian Tong;Tianye Niu","doi":"10.1109/TRPMS.2025.3569740","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3569740","url":null,"abstract":"Micro-CT provides tomographic information for small animals and plays an important role in preclinical research. Currently, the most micro-CT systems employ the single-source configuration and low-dose micro-focus X-ray tube, resulting in long scanning time and weak low-contrast discriminative ability. To address the two vital issues, we design a novel rotation gantry mounted triple-source conebeam micro-CT for the first time. Specifically, three pairs of tubes and detectors are installed in a single gantry. Compared with a single source configuration, the proposal increases the scanning efficiency by three times without aggravating any mechanical burden. The low-contrast discrimination is improved by multimaterial decomposition scheme based on triple-energy CT (TECT) images. Experiments were conducted using both phantoms and live rats. In the high-speed study, the proposal has reasonable agreement in signal-to-noise ratio and MTF compared to single-source scanner, even with a threefold increase in scanning speed. In the low-contrast discrimination study, on the digital phantom, the TECT correctly discriminates the materials of 2% linear attenuation coefficient difference. On the live rat, the decomposition accuracy of TECT has been enhanced by up to 21.9% compared to single-energy CT. These results validate the promising of the proposal for high-speed and low-contrast discrimination applications.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 1","pages":"88-98"},"PeriodicalIF":3.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861235","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}
Pub Date : 2025-03-12DOI: 10.1109/TRPMS.2025.3569198
Maël Millardet;Deepak Bharkhada;Juhi Raj;Josh Schaefferkoetter;Vladimir Panin;Maurizio Conti;Samuel Matej
End-to-end deep learning positron emission tomography (PET) reconstruction significantly surpasses traditional iterative methods in speed and shows promise for surpassing them in specific scenarios, such as low-dose imaging. In 2019, a significant advancement was made by using histo-images instead of time of flight (TOF) sinograms as the network’s input. Histo-images, by leveraging the image’s geometry, are more compatible with convolutional neural networks than TOF sinograms. Typically, the network’s input comprises a PET data histo-image patch and an attenuation map patch. However, this method has shown inconsistent bias in the reconstructed images. This work demonstrates that bias present in the prior method can be mitigated with alternative representations of attenuation information. Instead of using the attenuation map directly, we propose using a multiview histo-image of the attenuation correction factors, inspired by the iterative DIRECT framework and standard statistical modeling practices. We tested using them as separate channels, as well as using them to precorrect the data, or both. This histo-image encompasses the attenuation properties of each voxel from all directions within the entire lines of response. Our approaches significantly enhances image quantification, reducing the relative difference from maximum likelihood expectation maximization to an average of 2.0% to 3.0% across 16 regions of interest, compared to 9.1% with the previous method. Our statistical hypothesis test showed that the proposed methods significantly reduced absolute bias compared to the previous method, with p-values ranging from 0.002 to 0.007.
{"title":"Improved Quantification in End-to-End Deep Learning FastPET Reconstruction Using Multiview Histo-Images of Attenuation Correction Factors","authors":"Maël Millardet;Deepak Bharkhada;Juhi Raj;Josh Schaefferkoetter;Vladimir Panin;Maurizio Conti;Samuel Matej","doi":"10.1109/TRPMS.2025.3569198","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3569198","url":null,"abstract":"End-to-end deep learning positron emission tomography (PET) reconstruction significantly surpasses traditional iterative methods in speed and shows promise for surpassing them in specific scenarios, such as low-dose imaging. In 2019, a significant advancement was made by using histo-images instead of time of flight (TOF) sinograms as the network’s input. Histo-images, by leveraging the image’s geometry, are more compatible with convolutional neural networks than TOF sinograms. Typically, the network’s input comprises a PET data histo-image patch and an attenuation map patch. However, this method has shown inconsistent bias in the reconstructed images. This work demonstrates that bias present in the prior method can be mitigated with alternative representations of attenuation information. Instead of using the attenuation map directly, we propose using a multiview histo-image of the attenuation correction factors, inspired by the iterative DIRECT framework and standard statistical modeling practices. We tested using them as separate channels, as well as using them to precorrect the data, or both. This histo-image encompasses the attenuation properties of each voxel from all directions within the entire lines of response. Our approaches significantly enhances image quantification, reducing the relative difference from maximum likelihood expectation maximization to an average of 2.0% to 3.0% across 16 regions of interest, compared to 9.1% with the previous method. Our statistical hypothesis test showed that the proposed methods significantly reduced absolute bias compared to the previous method, with p-values ranging from 0.002 to 0.007.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 1","pages":"63-73"},"PeriodicalIF":3.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861229","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}
Pub Date : 2025-03-10DOI: 10.1109/TRPMS.2025.3549617
Yu-Nong Lin;Shao-Yi Huang;Cheng-Han Tsai;Han-Wei Wang;Meng-Chen Chung;Enhao Gong;Ing-Tsung Hsiao;Kevin T. Chen
Positron emission tomography (PET) with [18F]-fludeoxyglucose (FDG) can visualize the spatial pattern of neurodegeneration-related glucose hypometabolism. We proposed the “MRI-styled PET,” leveraging anatomical information from T1-weighted magnetic resonance imaging to enhance the structural details and quantitative accuracy of FDG-PET, which is degraded by partial volume effects (PVE). The proposed framework comprised a baseline encoder-decoder image fusion model and several task-specific modules; notably, the alternative anatomical input significantly contributes to correcting the under/overestimation of gray/white matter while the adaptive multiscale structural similarity loss utilized learnable ratios across various receptive fields to modulate attention to tissue contrast. Compared to a traditional anatomy-guided post-reconstruction PVE correction method (PVC-PET), MRI-styled PET demonstrated significantly higher structural similarity and peak signal-to-noise ratio than the baseline image fusion model (Baseline), showcasing the effectiveness of the proposed task-specific modules. In several Alzheimer’s Disease-related brain regions, MRI-styled PET exhibited consistent increases in corrective effects regardless of disease stage, compared to Baseline and PVC-PET. In conclusion, this study represented an initial exploration of a deep-learning approach for correcting PVE in PET without prior knowledge regarding the correction method or the underlying radiotracer uptake and without assumptions about the system point-spread function. Our implementation is available at https://github.com/NTUMMIO/MRI-styled-PET.
{"title":"MRI-Styled PET: A Dual Modality Fusion Approach to PET Partial Volume Correction","authors":"Yu-Nong Lin;Shao-Yi Huang;Cheng-Han Tsai;Han-Wei Wang;Meng-Chen Chung;Enhao Gong;Ing-Tsung Hsiao;Kevin T. Chen","doi":"10.1109/TRPMS.2025.3549617","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3549617","url":null,"abstract":"Positron emission tomography (PET) with [18F]-fludeoxyglucose (FDG) can visualize the spatial pattern of neurodegeneration-related glucose hypometabolism. We proposed the “MRI-styled PET,” leveraging anatomical information from T1-weighted magnetic resonance imaging to enhance the structural details and quantitative accuracy of FDG-PET, which is degraded by partial volume effects (PVE). The proposed framework comprised a baseline encoder-decoder image fusion model and several task-specific modules; notably, the alternative anatomical input significantly contributes to correcting the under/overestimation of gray/white matter while the adaptive multiscale structural similarity loss utilized learnable ratios across various receptive fields to modulate attention to tissue contrast. Compared to a traditional anatomy-guided post-reconstruction PVE correction method (PVC-PET), MRI-styled PET demonstrated significantly higher structural similarity and peak signal-to-noise ratio than the baseline image fusion model (Baseline), showcasing the effectiveness of the proposed task-specific modules. In several Alzheimer’s Disease-related brain regions, MRI-styled PET exhibited consistent increases in corrective effects regardless of disease stage, compared to Baseline and PVC-PET. In conclusion, this study represented an initial exploration of a deep-learning approach for correcting PVE in PET without prior knowledge regarding the correction method or the underlying radiotracer uptake and without assumptions about the system point-spread function. Our implementation is available at <uri>https://github.com/NTUMMIO/MRI-styled-PET</uri>.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 7","pages":"939-950"},"PeriodicalIF":3.5,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10918787","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998180","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 : 2025-03-07DOI: 10.1109/TRPMS.2025.3567663
Denis B. Zolotukhin;Alex H. Horkowitz;Michael Keidar
Immersion of a 12.5-kHz helium plasma discharge tube (DT) inside a metallic reflective bowl-like electrode enhances the anisotropy and directs the electromagnetic emission outwards from DT. Fitting the emission amplitude spatial decay by exponential functions shows that with the reflective electrode, the emission spatially decreases ~10 times slower than without the electrode. Such a way for directing the electromagnetic emission extends the effective spatial range of physical sensitization effect on UMG87 glioblastoma cancer cells from typical distance of several millimeters without the reflective electrode, up to several tens of millimeters with reflective electrode at floating or full DT central electrode potential. The decrease of UMG87 cell viability proportionally grows with the concentration of the intracellular Reactive Oxygen Species, and is detected not only at much larger axial distances (up to 10 cm), but also at nonzero radial distances from the DT center, reaching maximal effect in the vicinity of the walls of the reflective electrode. These findings look promising for the development of technology for distant nonionizing and noninvasive electromagnetic treatment of deep-lying tumors.
{"title":"Long-Range Noninvasive Electromagnetic Treatment of U87-MG Glioblastoma (in Vitro) by Plasma Discharge Tube With a Concave Reflective Electrode","authors":"Denis B. Zolotukhin;Alex H. Horkowitz;Michael Keidar","doi":"10.1109/TRPMS.2025.3567663","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3567663","url":null,"abstract":"Immersion of a 12.5-kHz helium plasma discharge tube (DT) inside a metallic reflective bowl-like electrode enhances the anisotropy and directs the electromagnetic emission outwards from DT. Fitting the emission amplitude spatial decay by exponential functions shows that with the reflective electrode, the emission spatially decreases ~10 times slower than without the electrode. Such a way for directing the electromagnetic emission extends the effective spatial range of physical sensitization effect on UMG87 glioblastoma cancer cells from typical distance of several millimeters without the reflective electrode, up to several tens of millimeters with reflective electrode at floating or full DT central electrode potential. The decrease of UMG87 cell viability proportionally grows with the concentration of the intracellular Reactive Oxygen Species, and is detected not only at much larger axial distances (up to 10 cm), but also at nonzero radial distances from the DT center, reaching maximal effect in the vicinity of the walls of the reflective electrode. These findings look promising for the development of technology for distant nonionizing and noninvasive electromagnetic treatment of deep-lying tumors.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 1","pages":"159-167"},"PeriodicalIF":3.5,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861220","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}
Pub Date : 2025-03-04DOI: 10.1109/TRPMS.2025.3542198
{"title":"IEEE Transactions on Radiation and Plasma Medical Sciences Information for Authors","authors":"","doi":"10.1109/TRPMS.2025.3542198","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3542198","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 3","pages":"C2-C2"},"PeriodicalIF":4.6,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10910004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553120","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 : 2025-03-04DOI: 10.1109/TRPMS.2025.3542196
{"title":"IEEE Transactions on Radiation and Plasma Medical Sciences Publication Information","authors":"","doi":"10.1109/TRPMS.2025.3542196","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3542196","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 3","pages":"C3-C3"},"PeriodicalIF":4.6,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10910005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553330","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 : 2025-03-03DOI: 10.1109/TRPMS.2025.3546998
Neus Cucarella;John Barrio;David Sanchez;Jose M. Benlloch;Antonio J. Gonzalez
Traditional PET detectors based on pixelated scintillation crystals with single-ended readout do not provide depth of interaction (DOI) information in an easy and cost-effective way. In this work, we propose a PET detector with single-ended readout and 1:1 coupling, based on arrays of naked pixelated crystals that are glued in one direction, and optically separated in the other one. We have named this approach as pseudo-slab. In this configuration, some of the optical photons will propagate in the glued direction, generating a light distribution from which DOI information can be retrieved. We have characterized four different detector configurations, all of them consisting of a linear array of $1times 8$ LYSO crystals of $3times 3times 20~{mathrm { mm}}^{3}$ each, with an optical glue of approximately $70~mu $ m in between them. The top and bottom faces are polished, and with a different number of unpolished lateral surfaces (2 versus 4) and different wrappings (Enhanced Specular Reflector versus BaSO4). The results obtained for the four detector configurations show energy resolutions ranging from 8.5% to 9.8% and coincidence time resolutions (with a reference pixel) below 290 ps for all cases using only the fastest timestamp and close to 230 ps when energy-weighted averaging of multiple timestamps is applied (corresponding to 182 ps detector time resolution). Regarding DOI performance, all configurations provide DOI information, showing a better performance with more number of unpolished faces and also when using ${mathrm { BaSO}}_{4}$ as a reflector.
{"title":"Single-Ended Readout PET Detector Based on Pixelated Crystals With TOF and DOI Capabilities","authors":"Neus Cucarella;John Barrio;David Sanchez;Jose M. Benlloch;Antonio J. Gonzalez","doi":"10.1109/TRPMS.2025.3546998","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3546998","url":null,"abstract":"Traditional PET detectors based on pixelated scintillation crystals with single-ended readout do not provide depth of interaction (DOI) information in an easy and cost-effective way. In this work, we propose a PET detector with single-ended readout and 1:1 coupling, based on arrays of naked pixelated crystals that are glued in one direction, and optically separated in the other one. We have named this approach as pseudo-slab. In this configuration, some of the optical photons will propagate in the glued direction, generating a light distribution from which DOI information can be retrieved. We have characterized four different detector configurations, all of them consisting of a linear array of <inline-formula> <tex-math>$1times 8$ </tex-math></inline-formula> LYSO crystals of <inline-formula> <tex-math>$3times 3times 20~{mathrm { mm}}^{3}$ </tex-math></inline-formula> each, with an optical glue of approximately <inline-formula> <tex-math>$70~mu $ </tex-math></inline-formula>m in between them. The top and bottom faces are polished, and with a different number of unpolished lateral surfaces (2 versus 4) and different wrappings (Enhanced Specular Reflector versus BaSO4). The results obtained for the four detector configurations show energy resolutions ranging from 8.5% to 9.8% and coincidence time resolutions (with a reference pixel) below 290 ps for all cases using only the fastest timestamp and close to 230 ps when energy-weighted averaging of multiple timestamps is applied (corresponding to 182 ps detector time resolution). Regarding DOI performance, all configurations provide DOI information, showing a better performance with more number of unpolished faces and also when using <inline-formula> <tex-math>${mathrm { BaSO}}_{4}$ </tex-math></inline-formula> as a reflector.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 7","pages":"866-871"},"PeriodicalIF":3.5,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908693","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998165","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 : 2025-03-02DOI: 10.1109/TRPMS.2025.3566556
Meiyuan Wen;Yaping Wu;Zhenxing Huang;Xiangjian He;Fiseha B. Tesema;Zixiang Chen;Yunlong Gao;Wenbo Li;Xinlan Yang;Yongfeng Yang;Hairong Zheng;Dong Liang;Meiyun Wang;Zhanli Hu
Dynamic positron emission tomography (PET) parametric imaging typically requires a 60-min acquisition period, causing patient discomfort and reducing clinical efficiency. This study explores the feasibility of generating parametric $K_{i}$ images from 10-min dynamic PET images acquired in the early or late scanning phases employing a multichannel feature fusion cold sampling (MCFFCoS) framework. PET data from 103 patients are acquired using the uEXPLORER total-body PET/CT scanner during 60-min scans. This study conducts deep learning experiments, taking early-phase or late-phase PET images as input, respectively. The generated $K_{i}$ images are evaluated by visual quality and quantitative metrics, including root-mean-squared error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Volumes of interest (VOIs) analysis is performed using linear regression and Bland-Altman plots. In the quantitative evaluation of total-body data, the parametric $K_{i}$ images generated from late-phase PET data generally outperform those derived from early-phase data. The analysis of VOIs indicates that the appropriate scanning protocol for PET parametric imaging may vary for different body regions. The deep learning approach is able to generate high-quality parametric $K_{i}$ images from 10-min dynamic PET scans, bypassing the requirements of long acquisition time for the estimation of blood input function in kinetic modeling.
{"title":"Diffusion-Based Model for Parametric Ki Generation From Total-Body Dynamic PET of Short-Duration Scan","authors":"Meiyuan Wen;Yaping Wu;Zhenxing Huang;Xiangjian He;Fiseha B. Tesema;Zixiang Chen;Yunlong Gao;Wenbo Li;Xinlan Yang;Yongfeng Yang;Hairong Zheng;Dong Liang;Meiyun Wang;Zhanli Hu","doi":"10.1109/TRPMS.2025.3566556","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3566556","url":null,"abstract":"Dynamic positron emission tomography (PET) parametric imaging typically requires a 60-min acquisition period, causing patient discomfort and reducing clinical efficiency. This study explores the feasibility of generating parametric <inline-formula> <tex-math>$K_{i}$ </tex-math></inline-formula> images from 10-min dynamic PET images acquired in the early or late scanning phases employing a multichannel feature fusion cold sampling (MCFFCoS) framework. PET data from 103 patients are acquired using the uEXPLORER total-body PET/CT scanner during 60-min scans. This study conducts deep learning experiments, taking early-phase or late-phase PET images as input, respectively. The generated <inline-formula> <tex-math>$K_{i}$ </tex-math></inline-formula> images are evaluated by visual quality and quantitative metrics, including root-mean-squared error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Volumes of interest (VOIs) analysis is performed using linear regression and Bland-Altman plots. In the quantitative evaluation of total-body data, the parametric <inline-formula> <tex-math>$K_{i}$ </tex-math></inline-formula> images generated from late-phase PET data generally outperform those derived from early-phase data. The analysis of VOIs indicates that the appropriate scanning protocol for PET parametric imaging may vary for different body regions. The deep learning approach is able to generate high-quality parametric <inline-formula> <tex-math>$K_{i}$ </tex-math></inline-formula> images from 10-min dynamic PET scans, bypassing the requirements of long acquisition time for the estimation of blood input function in kinetic modeling.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 1","pages":"16-25"},"PeriodicalIF":3.5,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861197","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}
Pub Date : 2025-03-02DOI: 10.1109/TRPMS.2025.3561408
{"title":"IEEE Transactions on Radiation and Plasma Medical Sciences Publication Information","authors":"","doi":"10.1109/TRPMS.2025.3561408","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3561408","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 5","pages":"C2-C2"},"PeriodicalIF":4.6,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10982363","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900607","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 : 2025-03-02DOI: 10.1109/TRPMS.2025.3561414
{"title":">Member Get-a-Member (MGM) Program","authors":"","doi":"10.1109/TRPMS.2025.3561414","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3561414","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 5","pages":"690-690"},"PeriodicalIF":4.6,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10982359","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900609","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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