Pub Date : 2024-09-06DOI: 10.1109/TRPMS.2024.3449313
{"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":null,"pages":null},"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}
Pub Date : 2024-09-06DOI: 10.1109/TRPMS.2024.3449311
{"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":null,"pages":null},"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}
Pub Date : 2024-09-06DOI: 10.1109/TRPMS.2024.3453689
{"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":null,"pages":null},"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}
Pub Date : 2024-09-06DOI: 10.1109/TRPMS.2024.3453691
{"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":null,"pages":null},"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}
Pub Date : 2024-08-01DOI: 10.1109/TRPMS.2024.3436697
Jessica B Hopson, Anthime Flaus, Colm J McGinnity, Radhouene Neji, Andrew J Reader, Alexander Hammers
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 two-dimensional 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 Transmission (PET) image quality (reconstructed at seven simulated doses), based on three clinical image quality metrics (global quality rating, pattern recognition, and diagnostic confidence). Using two-dimensional 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 ~6,000-20,000 trainable parameters) achieved a test mean-absolute-error 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 mean-absolute-error 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 mean-absolute-error 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.
{"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":"https://doi.org/10.1109/TRPMS.2024.3436697","url":null,"abstract":"<p><p>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 two-dimensional 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 [<sup>18</sup>F]FDG brain Positron Emission Transmission (PET) image quality (reconstructed at seven simulated doses), based on three clinical image quality metrics (global quality rating, pattern recognition, and diagnostic confidence). Using two-dimensional 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 ~6,000-20,000 trainable parameters) achieved a test mean-absolute-error 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 mean-absolute-error 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 mean-absolute-error 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.</p>","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":null,"pages":null},"PeriodicalIF":4.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7616552/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477477","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 : 2024-07-03DOI: 10.1109/TRPMS.2024.3405098
{"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":null,"pages":null},"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}
Pub Date : 2024-07-03DOI: 10.1109/TRPMS.2024.3421769
{"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":null,"pages":null},"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}
Pub Date : 2024-07-03DOI: 10.1109/TRPMS.2024.3405100
{"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":null,"pages":null},"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}
Pub Date : 2024-06-24DOI: 10.1109/TRPMS.2024.3417901
Lei Bi;Xiaohang Fu;Qiufang Liu;Shaoli Song;David Dagan Feng;Michael Fulham;Jinman Kim
Background: 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. Methods: 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). Results: 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%. Conclusion: 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.
{"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":null,"pages":null},"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}
Pub Date : 2024-06-14DOI: 10.1109/TRPMS.2024.3414578
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
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 $2times 2times $ 3 mm 3 and LYSO:Ce:0.2%Ca of $3.13times 3.13times $ 20 mm 3 , we measured a coincidence time resolution (CTR) of ( $95~pm ~3$ ) and $156~pm ~4$ ) 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 ( $76~pm ~2$ ) and ( $127~pm ~3$ ) 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.
{"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\u0000<sup>3</sup>\u0000 and LYSO:Ce:0.2%Ca of \u0000<inline-formula> <tex-math>$3.13times 3.13times $ </tex-math></inline-formula>\u0000 20 mm\u0000<sup>3</sup>\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":null,"pages":null},"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}
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