Pub Date : 2025-03-04DOI: 10.1109/TRPMS.2025.3542198
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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 : 2025-02-04DOI: 10.1109/TRPMS.2025.3530624
{"title":"IEEE Transactions on Radiation and Plasma Medical Sciences Information for Authors","authors":"","doi":"10.1109/TRPMS.2025.3530624","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3530624","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 2","pages":"C2-C2"},"PeriodicalIF":4.6,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870458","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106271","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 : 2025-02-04DOI: 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
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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 : 2024-11-18DOI: 10.1109/TRPMS.2024.3501373
Xuzhi He;Carlotta Trigila;Emilie Roncali
The performance of positron emission tomography (PET) detectors has been constrained by the photodetector collection of optical photons emitted in the scintillator, which was limited to photons reaching the exit surface with an angle larger than the critical angle. Photonic crystals (PhCs) are periodic nanostructures with sizes comparable to the optical photons’ wavelengths, which can break through the critical angle limit. Thorough experimental investigation of PhCs effect on optical harvest in scintillator detectors is complex and costly. Simulation can overcome these challenges. Mainstream software, such as GATE does not support PhCs simulation. Here, we generalize the GATE optical model by incorporating the PhCs optical model into the look-up table (LUT) Davis model. We can model the performance of advanced scintillator detectors via the generalized LUT Davis model. The scintillator and PhCs materials tested in this work were lutetium oxyorthosilicate and titanium dioxide, respectively. Scintillators with a cross section of $3times 3$ mm2 or $10times 10$ mm2 and a thickness varying from 9 to 18 mm with a step size of 3 mm were modeled with a PhCs interface to the photodetector. Among the 4 tested PhCs configurations, the best optical photon harvest was improved by 62.4% compared to traditional coupling with variable results between PhCs structures. The energy resolution only slightly improved. We thus investigated the angular distribution of collected optical photons, which can guide the optimization of photodetectors’ detection efficiency at specific angles.
{"title":"Implementation of Photonic Crystals Into Davis LUT Module for GATE Simulation","authors":"Xuzhi He;Carlotta Trigila;Emilie Roncali","doi":"10.1109/TRPMS.2024.3501373","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3501373","url":null,"abstract":"The performance of positron emission tomography (PET) detectors has been constrained by the photodetector collection of optical photons emitted in the scintillator, which was limited to photons reaching the exit surface with an angle larger than the critical angle. Photonic crystals (PhCs) are periodic nanostructures with sizes comparable to the optical photons’ wavelengths, which can break through the critical angle limit. Thorough experimental investigation of PhCs effect on optical harvest in scintillator detectors is complex and costly. Simulation can overcome these challenges. Mainstream software, such as GATE does not support PhCs simulation. Here, we generalize the GATE optical model by incorporating the PhCs optical model into the look-up table (LUT) Davis model. We can model the performance of advanced scintillator detectors via the generalized LUT Davis model. The scintillator and PhCs materials tested in this work were lutetium oxyorthosilicate and titanium dioxide, respectively. Scintillators with a cross section of <inline-formula> <tex-math>$3times 3$ </tex-math></inline-formula> mm2 or <inline-formula> <tex-math>$10times 10$ </tex-math></inline-formula> mm2 and a thickness varying from 9 to 18 mm with a step size of 3 mm were modeled with a PhCs interface to the photodetector. Among the 4 tested PhCs configurations, the best optical photon harvest was improved by 62.4% compared to traditional coupling with variable results between PhCs structures. The energy resolution only slightly improved. We thus investigated the angular distribution of collected optical photons, which can guide the optimization of photodetectors’ detection efficiency at specific angles.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 3","pages":"269-276"},"PeriodicalIF":4.6,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10756607","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553293","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}
In the context of particle therapy monitoring, we are developing a gamma-ray detector to determine the ion range in vivo from the measurement of particle time of flight. For this application, a beam monitor capable to tag in time the incident ion with a time resolution below 235-ps full width at half maximum (FWHM) (100-ps rms) is required to provide a start signal for the acquisition. We have therefore developed a dedicated detector based on a fast organic scintillator (EJ-204) of $25times 25times $ 1 mm3 coupled to four silicon photomultiplier strips that allow measuring the particle incident position by scintillation light sharing. The prototype was characterized with single protons of energies between 63 and 225 MeV at the MEDICYC and ProteusONE facilities of the Antoine Lacassagne proton therapy center in Nice. We obtained a time resolution of 120-ps FWHM at 63 MeV, and a spatial resolution of ~2-mm rms for single particles. Two identical detectors also allowed to measure the MEDICYC proton energy with 0.3% accuracy.
{"title":"A Fast Plastic Scintillator for Low-Intensity Proton Beam Monitoring","authors":"A. Andrè;C. Hoarau;Y. Boursier;A. Cherni;M. Dupont;L. Gallin Martel;M.-L. Gallin Martel;A. Garnier;J. Hèrault;J.-P. Hofverberg;P. Kavrigin;C. Morel;J.-F Muraz;M. Pinson;G. Tripodo;D. Maneval;S. Marcatili","doi":"10.1109/TRPMS.2024.3498959","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3498959","url":null,"abstract":"In the context of particle therapy monitoring, we are developing a gamma-ray detector to determine the ion range in vivo from the measurement of particle time of flight. For this application, a beam monitor capable to tag in time the incident ion with a time resolution below 235-ps full width at half maximum (FWHM) (100-ps rms) is required to provide a start signal for the acquisition. We have therefore developed a dedicated detector based on a fast organic scintillator (EJ-204) of <inline-formula> <tex-math>$25times 25times $ </tex-math></inline-formula>1 mm3 coupled to four silicon photomultiplier strips that allow measuring the particle incident position by scintillation light sharing. The prototype was characterized with single protons of energies between 63 and 225 MeV at the MEDICYC and ProteusONE facilities of the Antoine Lacassagne proton therapy center in Nice. We obtained a time resolution of 120-ps FWHM at 63 MeV, and a spatial resolution of ~2-mm rms for single particles. Two identical detectors also allowed to measure the MEDICYC proton energy with 0.3% accuracy.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 3","pages":"382-387"},"PeriodicalIF":4.6,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553285","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 : 2024-11-15DOI: 10.1109/TRPMS.2024.3496779
Jessica B. Hopson;Sam Ellis;Anthime Flaus;Colm J. McGinnity;Radhouene Neji;Andrew J. Reader;Alexander Hammers
Reduced dose positron emission tomography (PET) lowers the radiation dose to patients and reduces costs. Lower-count data, however, degrades reconstructed image quality. Advanced reconstruction methods help mitigate image quality losses, but it is important to assess the resulting images from a clinical perspective. Two experienced clinicians assessed four PET reconstruction algorithms for [18F]FDG brain data, compared to a clinical standard reference (maximum-likelihood expectation-maximization (MLEM)), based on seven clinical image quality metrics: global quality rating, pattern recognition, diagnostic confidence (all on a scale of 0–4), sharpness, caudate-putamen separation (CP), noise, and contrast (on a scale between 0–2). The reconstruction methods assessed were a guided and unguided version of self-supervised maximum a posteriori EM (MAPEM) (where the guidance case used the patient’s MR image to control the smoothness penalty). For 3 of the 11 patient datasets reconstructed, post-smoothed versions of the MAPEM reconstruction were also considered, where the smoothing was with the point-spread-function used in the resolution modelling. Statistically significant improvements were observed in sharpness, CP, and contrast for self-supervised MR-guided MAPEM compared to MLEM. For example, MLEM scored between 1-1.1 out of 2 for sharpness, CP, and contrast, whereas self-supervised MR-guided MAPEM scored between 1.5-1.75. In addition to the clinical evaluation, pretrained convolutional neural networks (CNNs) were used to assess the image quality of a further 62 images. The CNNs demonstrated similar trends to the clinician, showing their potential as automated standalone observers. Both the clinical and CNN assessments suggest when using only 5% of the standard injected dose, self-supervised MR-guided MAPEM reconstruction matches the 100% MLEM case for overall performance. This makes the images far more clinically useful than standard MLEM.
{"title":"Clinical and Deep-Learned Evaluation of MR-Guided Self-Supervised PET Reconstruction","authors":"Jessica B. Hopson;Sam Ellis;Anthime Flaus;Colm J. McGinnity;Radhouene Neji;Andrew J. Reader;Alexander Hammers","doi":"10.1109/TRPMS.2024.3496779","DOIUrl":"10.1109/TRPMS.2024.3496779","url":null,"abstract":"Reduced dose positron emission tomography (PET) lowers the radiation dose to patients and reduces costs. Lower-count data, however, degrades reconstructed image quality. Advanced reconstruction methods help mitigate image quality losses, but it is important to assess the resulting images from a clinical perspective. Two experienced clinicians assessed four PET reconstruction algorithms for [18F]FDG brain data, compared to a clinical standard reference (maximum-likelihood expectation-maximization (MLEM)), based on seven clinical image quality metrics: global quality rating, pattern recognition, diagnostic confidence (all on a scale of 0–4), sharpness, caudate-putamen separation (CP), noise, and contrast (on a scale between 0–2). The reconstruction methods assessed were a guided and unguided version of self-supervised maximum a posteriori EM (MAPEM) (where the guidance case used the patient’s MR image to control the smoothness penalty). For 3 of the 11 patient datasets reconstructed, post-smoothed versions of the MAPEM reconstruction were also considered, where the smoothing was with the point-spread-function used in the resolution modelling. Statistically significant improvements were observed in sharpness, CP, and contrast for self-supervised MR-guided MAPEM compared to MLEM. For example, MLEM scored between 1-1.1 out of 2 for sharpness, CP, and contrast, whereas self-supervised MR-guided MAPEM scored between 1.5-1.75. In addition to the clinical evaluation, pretrained convolutional neural networks (CNNs) were used to assess the image quality of a further 62 images. The CNNs demonstrated similar trends to the clinician, showing their potential as automated standalone observers. Both the clinical and CNN assessments suggest when using only 5% of the standard injected dose, self-supervised MR-guided MAPEM reconstruction matches the 100% MLEM case for overall performance. This makes the images far more clinically useful than standard MLEM.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 3","pages":"337-346"},"PeriodicalIF":4.6,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143504796","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}
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