Pub Date : 2025-02-10DOI: 10.1109/TRPMS.2025.3540212
Tao Fan;Wenhui Qin;Zhongliang Zhang;Xiaoxue Lei;Zhi Liu;Meili Yang;Qianyu Wu;Yang Chen;Guotao Quan;Xiaochun Lai
Image-domain material decomposition is widely used due to its computational efficiency and compatibility with commonly adopted clinical spectral reconstruction platforms. However, it often suffers from beam hardening artifacts, which can degrade both image quality and diagnostic accuracy. In this study, we propose a beam hardening correction (BHC) method specifically designed for image-domain material decomposition in photon-counting computed tomography (PCCT). Our method utilizes spectral information obtained from the photon-counting detector in PCCT to estimate and correct the beam hardening effect. The measured X-ray spectrum for each energy counter is initially estimated using a sinogram from an off-center water phantom. This spectral information is then applied to compute and correct projection errors induced by beam hardening, thereby enhancing material decomposition accuracy. Extensive qualitative and quantitative evaluations using water, Gammex phantoms (for moderate beam hardening), and a head phantom (for severe beam hardening) validate the effectiveness of the proposed method. Our BHC approach demonstrates significant improvements over existing methods, enabling more accurate and reliable image-domain material decomposition in PCCT applications.
{"title":"Beam Hardening Correction for Image-Domain Material Decomposition in Photon-Counting CT","authors":"Tao Fan;Wenhui Qin;Zhongliang Zhang;Xiaoxue Lei;Zhi Liu;Meili Yang;Qianyu Wu;Yang Chen;Guotao Quan;Xiaochun Lai","doi":"10.1109/TRPMS.2025.3540212","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3540212","url":null,"abstract":"Image-domain material decomposition is widely used due to its computational efficiency and compatibility with commonly adopted clinical spectral reconstruction platforms. However, it often suffers from beam hardening artifacts, which can degrade both image quality and diagnostic accuracy. In this study, we propose a beam hardening correction (BHC) method specifically designed for image-domain material decomposition in photon-counting computed tomography (PCCT). Our method utilizes spectral information obtained from the photon-counting detector in PCCT to estimate and correct the beam hardening effect. The measured X-ray spectrum for each energy counter is initially estimated using a sinogram from an off-center water phantom. This spectral information is then applied to compute and correct projection errors induced by beam hardening, thereby enhancing material decomposition accuracy. Extensive qualitative and quantitative evaluations using water, Gammex phantoms (for moderate beam hardening), and a head phantom (for severe beam hardening) validate the effectiveness of the proposed method. Our BHC approach demonstrates significant improvements over existing methods, enabling more accurate and reliable image-domain material decomposition in PCCT applications.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 6","pages":"788-799"},"PeriodicalIF":4.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597600","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}
The total-body positron emission tomography (PET) scanning time is typically reduced to mitigate motion artifacts, yet this can compromise image quality. Current approaches often enhance PET resolution via CT guidance but overlook structural disparities across anatomical sites. Therefore, this article introduces an enhanced Wasserstein generative adversarial network with gradient penalty (WGAN-GP), integrating anatomical information as attributes to enhance quality of multiple short-duration (2.5%, 5%, and 10%) total-body PET images simultaneously. The proposed method is a weight-adaptive three-channel network for different regions, integrating PET/CT features and attributes to optimize short-duration PET image generation. peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), root mean square error (RMSE), and standard uptake value (SUV) are analyzed within whole images and regions of interests (ROIs) to compare proposed method with other networks. The results on the 18F-FDG PET dataset show the method achieves better-visual effects and metrics (like SSIM: 0.94±0.04 for 2.5%; 0.95±0.04 for 5%; and 0.96±0.04 for 10%) across total-body than others. Furthermore, the SUV-maximum and activity distributions of ROIs are closest to standard-duration PET. Additionally, the method demonstrates robustness under varying degrees of 18F-FDG PET/CT misalignment and in the PSMA PET/CT dataset. The proposed method offers reliable technical support for clinical diagnosis via short-duration total-body PET.
{"title":"Weight-Adaptive Network With CT Enhancement for Short-Duration PET Imaging Utilizing the uEXPLORER Total-Body System","authors":"Fanting Luo;Hongyan Tang;Wenbo Li;Haiyan Wang;Ruohua Chen;Jianjun Liu;Chao Zhou;Xu Zhang;Wei Fan;Yumo Zhao;Yongfeng Yang;Hairong Zheng;Dong Liang;Shengping Liu;Zhenxing Huang;Zhanli Hu","doi":"10.1109/TRPMS.2025.3540112","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3540112","url":null,"abstract":"The total-body positron emission tomography (PET) scanning time is typically reduced to mitigate motion artifacts, yet this can compromise image quality. Current approaches often enhance PET resolution via CT guidance but overlook structural disparities across anatomical sites. Therefore, this article introduces an enhanced Wasserstein generative adversarial network with gradient penalty (WGAN-GP), integrating anatomical information as attributes to enhance quality of multiple short-duration (2.5%, 5%, and 10%) total-body PET images simultaneously. The proposed method is a weight-adaptive three-channel network for different regions, integrating PET/CT features and attributes to optimize short-duration PET image generation. peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), root mean square error (RMSE), and standard uptake value (SUV) are analyzed within whole images and regions of interests (ROIs) to compare proposed method with other networks. The results on the 18F-FDG PET dataset show the method achieves better-visual effects and metrics (like SSIM: 0.94±0.04 for 2.5%; 0.95±0.04 for 5%; and 0.96±0.04 for 10%) across total-body than others. Furthermore, the SUV-maximum and activity distributions of ROIs are closest to standard-duration PET. Additionally, the method demonstrates robustness under varying degrees of 18F-FDG PET/CT misalignment and in the PSMA PET/CT dataset. The proposed method offers reliable technical support for clinical diagnosis via short-duration total-body PET.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 6","pages":"800-814"},"PeriodicalIF":4.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597879","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}
To improve coincidence time resolution (CTR) in time-of-flight positron emission tomography (TOF-PET), various approaches have been explored, including the use of novel materials like heterostructured scintillators. These scintillators combine different materials with complementary properties like Bismuth Germanate for its high detection efficiency and EJ232 for fast timing. By layering these materials on a micrometer scale, energy sharing between them becomes possible, enabling fast timing, while maintaining high detection efficiency. For TOF-PET applications, scalable electronics are essential. While earlier models characterized heterostructured scintillators in analog, single-pixel setups, the digital and scalable systems required for full positron emission tomography (PET) scanners present additional challenges due to increased signal complexity. In this study, we explored neural networks to characterize heterostructured scintillators using parameters available in scalable systems. We trained one neural network to identify photoelectric events and another one to estimate the amount of energy sharing between the two materials. The method demonstrated promising results using multiple combinations of the aforementioned parameters, with prediction accuracy for photoelectric events ranging from 91.6% to 96.8%, and a mean average error in the energy sharing estimation between 7.7 and 43.9 keV. This suggests the potential application of heterostructured scintillators in scalable readout electronics for full TOF-PET systems.
{"title":"Event Classification in Heterostructured Scintillators With Limited Readout Information Using Neural Networks","authors":"Carsten Lowis;Fiammetta Pagano;Marco Pizzichemi;Karl-Josef Langen;Karl Ziemons;Etiennette Auffray","doi":"10.1109/TRPMS.2025.3540559","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3540559","url":null,"abstract":"To improve coincidence time resolution (CTR) in time-of-flight positron emission tomography (TOF-PET), various approaches have been explored, including the use of novel materials like heterostructured scintillators. These scintillators combine different materials with complementary properties like Bismuth Germanate for its high detection efficiency and EJ232 for fast timing. By layering these materials on a micrometer scale, energy sharing between them becomes possible, enabling fast timing, while maintaining high detection efficiency. For TOF-PET applications, scalable electronics are essential. While earlier models characterized heterostructured scintillators in analog, single-pixel setups, the digital and scalable systems required for full positron emission tomography (PET) scanners present additional challenges due to increased signal complexity. In this study, we explored neural networks to characterize heterostructured scintillators using parameters available in scalable systems. We trained one neural network to identify photoelectric events and another one to estimate the amount of energy sharing between the two materials. The method demonstrated promising results using multiple combinations of the aforementioned parameters, with prediction accuracy for photoelectric events ranging from 91.6% to 96.8%, and a mean average error in the energy sharing estimation between 7.7 and 43.9 keV. This suggests the potential application of heterostructured scintillators in scalable readout electronics for full TOF-PET systems.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 6","pages":"756-761"},"PeriodicalIF":4.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10879225","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597711","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 : 2025-02-07DOI: 10.1109/TRPMS.2025.3539191
Nicolaus Kratochwil;Emilie Roncali;Joshua W. Cates;Gerard Ariño-Estrada
Detection time performance is a key aspect for time-of-flight positron emission tomography. With recent advancement in SiPM technology and fast readout electronics, one limiting factor on timing performance is light transport in the crystal. For high aspect-ratio crystals with single-ended readout, the time information of approximately half the optical photons is severely degraded as they initially travel in the direction opposed to the photodetector. For promptly-emitted Cherenkov photons, the increase of variance of optical path length limits their intrinsic advantage. Low-noise and high-frequency dual-ended SiPM readout can be employed to mitigate the aforementioned challenges and has the potential to combine ultrafast timing with highest gamma-ray detection efficiency. We have studied the timing properties of cerium-doped lutetium-yttrium-oxyorthosilicate (LYSO:Ce) and bismuth germanate (BGO) in a symmetric dual-ended SiPM readout configuration. A time-based depth-of-interaction correction and a novel adaptive timestamp weighting was used to optimize the timing performance. Coupling 3x3x20 mm3 polished BGO crystals to Broadcom AFBR-S4N44P014M SiPMs a CTR of 234 ± 4 ps FWHM (harmonic average) was obtained for all photopeak events. For same-sized LYSO:Ce crystals, the measured CTR value is 98 ± 2 ps, which is in excellent agreement with analytic calculations on the timing limits considering scintillation properties and modeling of light transport. The results demonstrate significant timing improvement with dual-ended readout, both for Cherenkov photons in BGO and for standard scintillation for enhanced diagnostic accuracy in PET imaging.
{"title":"High-Performance Dual-Ended SiPM Readout for TOF-PET With BGO and LYSO:Ce","authors":"Nicolaus Kratochwil;Emilie Roncali;Joshua W. Cates;Gerard Ariño-Estrada","doi":"10.1109/TRPMS.2025.3539191","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3539191","url":null,"abstract":"Detection time performance is a key aspect for time-of-flight positron emission tomography. With recent advancement in SiPM technology and fast readout electronics, one limiting factor on timing performance is light transport in the crystal. For high aspect-ratio crystals with single-ended readout, the time information of approximately half the optical photons is severely degraded as they initially travel in the direction opposed to the photodetector. For promptly-emitted Cherenkov photons, the increase of variance of optical path length limits their intrinsic advantage. Low-noise and high-frequency dual-ended SiPM readout can be employed to mitigate the aforementioned challenges and has the potential to combine ultrafast timing with highest gamma-ray detection efficiency. We have studied the timing properties of cerium-doped lutetium-yttrium-oxyorthosilicate (LYSO:Ce) and bismuth germanate (BGO) in a symmetric dual-ended SiPM readout configuration. A time-based depth-of-interaction correction and a novel adaptive timestamp weighting was used to optimize the timing performance. Coupling 3x3x20 mm3 polished BGO crystals to Broadcom AFBR-S4N44P014M SiPMs a CTR of 234 ± 4 ps FWHM (harmonic average) was obtained for all photopeak events. For same-sized LYSO:Ce crystals, the measured CTR value is 98 ± 2 ps, which is in excellent agreement with analytic calculations on the timing limits considering scintillation properties and modeling of light transport. The results demonstrate significant timing improvement with dual-ended readout, both for Cherenkov photons in BGO and for standard scintillation for enhanced diagnostic accuracy in PET imaging.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 6","pages":"721-735"},"PeriodicalIF":4.6,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10878413","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598070","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}
Silicon photomultipliers (SiPMs) fabricated in a standard complementary metal-oxide-semiconductor (CMOS) process enable the development of cost-effective, reliable, and power-efficient photosensors for positron emission tomography (PET) applications. However, PET manufacturers prefer SiPMs in customized technologies for their high photon detection efficiency (PDE) and low noise, which are crucial parameters for energy and time resolution in PET scanners. Recently, RAYQUANT Technology Ltd. has developed a high PDE SiPM fabricated in 0.11-$mu $ m CMOS process, with large area of 9 mm2. This article investigates for the first time the ability of this SiPM to collect scintillation light from LYSO crystals for PET applications, evaluating energy resolution, and coincidence time resolution (CTR). The LYSO/SiPM detector achieves the best energy resolution (FWHM) of $mathbf {(28. 0pm 1.0)}$ % at 60 keV, $mathbf {(10.6pm 0.4)}$ % at 511 keV, and $mathbf {(8.5pm 0.4)}$ % at 662 keV. The best CTR (FWHM) is $mathbf {(172pm 2)}$ ps, $mathbf {(147pm 2)}$ ps, and $mathbf {(115pm 1)}$ ps, when the SiPM is coupled to crystals of 20, 10, and 5 mm length, respectively. These results confirm that the studied CMOS-based SiPM is not only suitable for PET applications but is even competitive with SiPMs fabricated in customized technologies.
{"title":"Experimental Study of a Large Area High PDE SiPM in 0.11-μm CMOS Process for PET Applications","authors":"Jingbin Chen;Nicola D’Ascenzo;Daniele Passaretti;Hui Lao;Yuexuan Hua;Qingguo Xie","doi":"10.1109/TRPMS.2025.3534221","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3534221","url":null,"abstract":"Silicon photomultipliers (SiPMs) fabricated in a standard complementary metal-oxide-semiconductor (CMOS) process enable the development of cost-effective, reliable, and power-efficient photosensors for positron emission tomography (PET) applications. However, PET manufacturers prefer SiPMs in customized technologies for their high photon detection efficiency (PDE) and low noise, which are crucial parameters for energy and time resolution in PET scanners. Recently, RAYQUANT Technology Ltd. has developed a high PDE SiPM fabricated in 0.11-<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>m CMOS process, with large area of 9 mm2. This article investigates for the first time the ability of this SiPM to collect scintillation light from LYSO crystals for PET applications, evaluating energy resolution, and coincidence time resolution (CTR). The LYSO/SiPM detector achieves the best energy resolution (FWHM) of <inline-formula> <tex-math>$mathbf {(28. 0pm 1.0)}$ </tex-math></inline-formula>% at 60 keV, <inline-formula> <tex-math>$mathbf {(10.6pm 0.4)}$ </tex-math></inline-formula>% at 511 keV, and <inline-formula> <tex-math>$mathbf {(8.5pm 0.4)}$ </tex-math></inline-formula>% at 662 keV. The best CTR (FWHM) is <inline-formula> <tex-math>$mathbf {(172pm 2)}$ </tex-math></inline-formula> ps, <inline-formula> <tex-math>$mathbf {(147pm 2)}$ </tex-math></inline-formula> ps, and <inline-formula> <tex-math>$mathbf {(115pm 1)}$ </tex-math></inline-formula> ps, when the SiPM is coupled to crystals of 20, 10, and 5 mm length, respectively. These results confirm that the studied CMOS-based SiPM is not only suitable for PET applications but is even competitive with SiPMs fabricated in customized technologies.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 6","pages":"747-755"},"PeriodicalIF":4.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597948","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}
The generation of synthetic Computed Tomography (sCT) images from cone-beam CT (CBCT) data using deep learning (DL) methodologies represents a significant advancement in radiation oncology. This systematic review, following PRISMA guidelines and using the PICO model, comprehensively evaluates the literature from 2014 to 2024 on the generation of sCT images for radiation therapy planning in oncology. A total of 35 relevant studies were identified and analyzed, revealing the prevalence of DL approaches in the generation of sCT. This review comprehensively covers sCT generation based on CBCT and proton-based studies. Some of the commonly employed architectures explored are convolutional neural networks (CNNs), generative adversarial networks (GANs), transformers, and diffusion models. Evaluation metrics, including mean absolute error (MAE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), consistently demonstrate the comparability of sCT images with gold-standard planning CTs (pCT), indicating their potential to improve treatment precision and patient outcomes. Challenges, such as field-of-view (FOV) disparities and integration into clinical workflows, are discussed, along with recommendations for future research and standardization efforts. In general, the findings underscore the promising role of sCT-based approaches in personalized treatment planning and adaptive radiation therapy, with potential implications for improved oncology treatment delivery and patient care.
{"title":"Synthetic CT Image Generation From CBCT: A Systematic Review","authors":"Alzahra Altalib;Scott McGregor;Chunhui Li;Alessandro Perelli","doi":"10.1109/TRPMS.2025.3533749","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3533749","url":null,"abstract":"The generation of synthetic Computed Tomography (sCT) images from cone-beam CT (CBCT) data using deep learning (DL) methodologies represents a significant advancement in radiation oncology. This systematic review, following PRISMA guidelines and using the PICO model, comprehensively evaluates the literature from 2014 to 2024 on the generation of sCT images for radiation therapy planning in oncology. A total of 35 relevant studies were identified and analyzed, revealing the prevalence of DL approaches in the generation of sCT. This review comprehensively covers sCT generation based on CBCT and proton-based studies. Some of the commonly employed architectures explored are convolutional neural networks (CNNs), generative adversarial networks (GANs), transformers, and diffusion models. Evaluation metrics, including mean absolute error (MAE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM), consistently demonstrate the comparability of sCT images with gold-standard planning CTs (pCT), indicating their potential to improve treatment precision and patient outcomes. Challenges, such as field-of-view (FOV) disparities and integration into clinical workflows, are discussed, along with recommendations for future research and standardization efforts. In general, the findings underscore the promising role of sCT-based approaches in personalized treatment planning and adaptive radiation therapy, with potential implications for improved oncology treatment delivery and patient care.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 6","pages":"691-707"},"PeriodicalIF":4.6,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10852217","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597953","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 : 2025-01-22DOI: 10.1109/TRPMS.2025.3532592
Stefan J. van der Sar;Dennis R. Schaart
We investigate silicon photomultiplier (SiPM)-based scintillation detectors for medical X-ray photon-counting applications, where the input count rate (ICR) can reach a few Mcps/mm2 in cone-beam CT for radiotherapy, for example, up to a few hundred Mcps/mm2 in diagnostic CT. Thus, pulse pile-up can severely distort the measurement of counts and energies. Here, we experimentally evaluate the counting and spectral performance of SiPM-based scintillation detectors at 60 keV as a function of ICR/pile-up level. We coupled $0.9times 0.9times 3.5~{mathrm { mm}}^{3}$ LYSO:Ce and $0.9times 0.9times 4.5~{mathrm { mm}}^{3}$ YAP:Ce scintillators to $1.0times 1.0~{mathrm { mm}}^{2}$ ultrafast SiPMs and exposed these single-pixel detectors to a 10-GBq Am-241 source. We varied ICR from 0 to 5 Mcps/pixel and studied detector performance for paralyzable-like (p-like) and nonparalyzable-like (np-like) counting algorithms, after applying a second-order low-pass filter with cut-off frequencies $f_{mathrm { c}}$ of 5, 10, or 20 MHz to the pulse trains. Counting performance was quantified by the output count rate (OCR) and the count-rate loss factor (CRLF). In addition to the traditional spectral performance measure of the full-width-at-half-maximum (FWHM) energy resolution at low ICR, we propose the spectral degradation factor (SDF) to quantify spectral effects of pile-up at any ICR. Best counting performance is obtained with np-like counting and $f_{mathrm { c}}{=}$ 20 MHz, for which the count-rate loss is at most 10% in the investigated range of ICRs, whereas p-like counting yields best spectral performance. Due to less pile-up, the fastest pulses obtained with $f_{mathrm { c}}{=}$ 20 MHz already provide the best SDF values at ICRs of a few Mcps/pixel, despite their worse low-rate energy resolution. Hence, spectral performance under pile-up conditions appears to benefit more from substantially faster pulses than a somewhat better low-rate energy resolution. Moreover, we show that the pulse shape of SiPM-based detectors allows to improve spectral performance under pile-up conditions using dedicated peak detection windows.
{"title":"Performance of X-Ray Photon-Counting Scintillation Detectors Under Pile-Up Conditions at 60 keV","authors":"Stefan J. van der Sar;Dennis R. Schaart","doi":"10.1109/TRPMS.2025.3532592","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3532592","url":null,"abstract":"We investigate silicon photomultiplier (SiPM)-based scintillation detectors for medical X-ray photon-counting applications, where the input count rate (ICR) can reach a few Mcps/mm2 in cone-beam CT for radiotherapy, for example, up to a few hundred Mcps/mm2 in diagnostic CT. Thus, pulse pile-up can severely distort the measurement of counts and energies. Here, we experimentally evaluate the counting and spectral performance of SiPM-based scintillation detectors at 60 keV as a function of ICR/pile-up level. We coupled <inline-formula> <tex-math>$0.9times 0.9times 3.5~{mathrm { mm}}^{3}$ </tex-math></inline-formula> LYSO:Ce and <inline-formula> <tex-math>$0.9times 0.9times 4.5~{mathrm { mm}}^{3}$ </tex-math></inline-formula> YAP:Ce scintillators to <inline-formula> <tex-math>$1.0times 1.0~{mathrm { mm}}^{2}$ </tex-math></inline-formula> ultrafast SiPMs and exposed these single-pixel detectors to a 10-GBq Am-241 source. We varied ICR from 0 to 5 Mcps/pixel and studied detector performance for paralyzable-like (p-like) and nonparalyzable-like (np-like) counting algorithms, after applying a second-order low-pass filter with cut-off frequencies <inline-formula> <tex-math>$f_{mathrm { c}}$ </tex-math></inline-formula> of 5, 10, or 20 MHz to the pulse trains. Counting performance was quantified by the output count rate (OCR) and the count-rate loss factor (CRLF). In addition to the traditional spectral performance measure of the full-width-at-half-maximum (FWHM) energy resolution at low ICR, we propose the spectral degradation factor (SDF) to quantify spectral effects of pile-up at any ICR. Best counting performance is obtained with np-like counting and <inline-formula> <tex-math>$f_{mathrm { c}}{=}$ </tex-math></inline-formula> 20 MHz, for which the count-rate loss is at most 10% in the investigated range of ICRs, whereas p-like counting yields best spectral performance. Due to less pile-up, the fastest pulses obtained with <inline-formula> <tex-math>$f_{mathrm { c}}{=}$ </tex-math></inline-formula> 20 MHz already provide the best SDF values at ICRs of a few Mcps/pixel, despite their worse low-rate energy resolution. Hence, spectral performance under pile-up conditions appears to benefit more from substantially faster pulses than a somewhat better low-rate energy resolution. Moreover, we show that the pulse shape of SiPM-based detectors allows to improve spectral performance under pile-up conditions using dedicated peak detection windows.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 6","pages":"708-720"},"PeriodicalIF":4.6,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10850655","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597677","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}
This article presents a novel approach for learned synergistic reconstruction of medical images using multibranch generative models. Leveraging variational autoencoders (VAEs), our model learns from pairs of images simultaneously, enabling effective denoising and reconstruction. Synergistic image reconstruction is achieved by incorporating the trained models in a regularizer that evaluates the distance between the images and the model. We demonstrate the efficacy of our approach on both Modified National Institute of Standards and Technology (MNIST) and positron emission tomography (PET)/computed tomography (CT) datasets, showcasing improved image quality for low-dose imaging. Despite challenges, such as patch decomposition and model limitations, our results underscore the potential of generative models for enhancing medical imaging reconstruction.
{"title":"Multibranch Generative Models for Multichannel Imaging With an Application to PET/CT Synergistic Reconstruction","authors":"Noel Jeffrey Pinton;Alexandre Bousse;Catherine Cheze-Le-Rest;Dimitris Visvikis","doi":"10.1109/TRPMS.2025.3532176","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3532176","url":null,"abstract":"This article presents a novel approach for learned synergistic reconstruction of medical images using multibranch generative models. Leveraging variational autoencoders (VAEs), our model learns from pairs of images simultaneously, enabling effective denoising and reconstruction. Synergistic image reconstruction is achieved by incorporating the trained models in a regularizer that evaluates the distance between the images and the model. We demonstrate the efficacy of our approach on both Modified National Institute of Standards and Technology (MNIST) and positron emission tomography (PET)/computed tomography (CT) datasets, showcasing improved image quality for low-dose imaging. Despite challenges, such as patch decomposition and model limitations, our results underscore the potential of generative models for enhancing medical imaging reconstruction.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 5","pages":"654-666"},"PeriodicalIF":4.6,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900606","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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