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}
Pub Date : 2025-03-02DOI: 10.1109/TRPMS.2025.3561406
{"title":"IEEE Transactions on Radiation and Plasma Medical Sciences Information for Authors","authors":"","doi":"10.1109/TRPMS.2025.3561406","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3561406","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 5","pages":"C3-C3"},"PeriodicalIF":4.6,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10982370","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900520","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.2025.3566630
Jun Hou;Tianqi Chen;Yinchi Zhou;Xiongchao Chen;Huidong Xie;Qiong Liu;Menghua Xia;Vladimir Y. Panin;Takuya Toyonaga;Chi Liu;Bo Zhou
Attenuation correction (AC) is a critical step to ensure accurate quantitative Positron Emission Tomography (PET) imaging. To eliminate the radiation dose from CT, deep learning (DL)-based methods have been extensively investigated to generate the CT-equivalent attenuation map (<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>-CT) directly from the PET signal. However, almost all previous studies only focus on <inline-formula> <tex-math>${}^{18}text {F-FDG}$ </tex-math></inline-formula> due to its extensive data availability which is suitable for DL model training. For other less common tracer types, it is generally believed that new models must be trained separately on these tracer-specific data to ensure reasonable performance. In this work, we explored the cross-tracer generalizability of <inline-formula> <tex-math>$mu $ </tex-math></inline-formula>-DL generation DL models - primarily focusing on whether a model trained on a commonly used tracer like <inline-formula> <tex-math>${}^{18}text {F-FDG}$ </tex-math></inline-formula> can be effectively applied to less common tracers, such as <inline-formula> <tex-math>${}^{68}text {Ga-DOTATE}$ </tex-math></inline-formula> and <inline-formula> <tex-math>${}^{18}text {F-Fluciclovine}$ </tex-math></inline-formula>, and vice versa. Unlike methods that directly generate attenuation-corrected (AC) PET images from nonattenuation corrected (NAC) PET images or maximum likelihood reconstruction of activity and attenuation (MLAA) reconstructions, we generate the CT-based DL attenuation maps (<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>-DL) using MLAA reconstruction with the combined input of attenuation maps (<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>-MLAA) and tracer activity (<inline-formula> <tex-math>$lambda $ </tex-math></inline-formula>-MLAA). This <inline-formula> <tex-math>$mu $ </tex-math></inline-formula>-DL is then used for AC to obtain the final AC PET image. Our comprehensive evaluations on both <inline-formula> <tex-math>$mu $ </tex-math></inline-formula>-CT generation and the PET reconstruction found that the DL model trained on one specific tracer can be adapted to other tracers with competitive performance when compared to the tracer-specific trained DL model. The <inline-formula> <tex-math>${}^{18}text {F-FDG}$ </tex-math></inline-formula>-trained model demonstrated the best generalizability when applied to less common tracer types which often have relatively fewer available data for training. Additionally, we investigated the role of the <inline-formula> <tex-math>$mu $ </tex-math></inline-formula>-MLAA and <inline-formula> <tex-math>$lambda $ </tex-math></inline-formula>-MLAA as inputs for the network performance. We found that combining both inputs resulted in the best performance, but the <inline-formula> <tex-math>$mu $ </tex-math></inline-formula>-MLAA contributed more significantly compared to the <inline-formula> <tex-math>$lambda $ </tex
{"title":"An Investigation on Cross-Tracer Generalizability of Deep Learning-Based PET Attenuation Correction","authors":"Jun Hou;Tianqi Chen;Yinchi Zhou;Xiongchao Chen;Huidong Xie;Qiong Liu;Menghua Xia;Vladimir Y. Panin;Takuya Toyonaga;Chi Liu;Bo Zhou","doi":"10.1109/TRPMS.2025.3566630","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3566630","url":null,"abstract":"Attenuation correction (AC) is a critical step to ensure accurate quantitative Positron Emission Tomography (PET) imaging. To eliminate the radiation dose from CT, deep learning (DL)-based methods have been extensively investigated to generate the CT-equivalent attenuation map (<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>-CT) directly from the PET signal. However, almost all previous studies only focus on <inline-formula> <tex-math>${}^{18}text {F-FDG}$ </tex-math></inline-formula> due to its extensive data availability which is suitable for DL model training. For other less common tracer types, it is generally believed that new models must be trained separately on these tracer-specific data to ensure reasonable performance. In this work, we explored the cross-tracer generalizability of <inline-formula> <tex-math>$mu $ </tex-math></inline-formula>-DL generation DL models - primarily focusing on whether a model trained on a commonly used tracer like <inline-formula> <tex-math>${}^{18}text {F-FDG}$ </tex-math></inline-formula> can be effectively applied to less common tracers, such as <inline-formula> <tex-math>${}^{68}text {Ga-DOTATE}$ </tex-math></inline-formula> and <inline-formula> <tex-math>${}^{18}text {F-Fluciclovine}$ </tex-math></inline-formula>, and vice versa. Unlike methods that directly generate attenuation-corrected (AC) PET images from nonattenuation corrected (NAC) PET images or maximum likelihood reconstruction of activity and attenuation (MLAA) reconstructions, we generate the CT-based DL attenuation maps (<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>-DL) using MLAA reconstruction with the combined input of attenuation maps (<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>-MLAA) and tracer activity (<inline-formula> <tex-math>$lambda $ </tex-math></inline-formula>-MLAA). This <inline-formula> <tex-math>$mu $ </tex-math></inline-formula>-DL is then used for AC to obtain the final AC PET image. Our comprehensive evaluations on both <inline-formula> <tex-math>$mu $ </tex-math></inline-formula>-CT generation and the PET reconstruction found that the DL model trained on one specific tracer can be adapted to other tracers with competitive performance when compared to the tracer-specific trained DL model. The <inline-formula> <tex-math>${}^{18}text {F-FDG}$ </tex-math></inline-formula>-trained model demonstrated the best generalizability when applied to less common tracer types which often have relatively fewer available data for training. Additionally, we investigated the role of the <inline-formula> <tex-math>$mu $ </tex-math></inline-formula>-MLAA and <inline-formula> <tex-math>$lambda $ </tex-math></inline-formula>-MLAA as inputs for the network performance. We found that combining both inputs resulted in the best performance, but the <inline-formula> <tex-math>$mu $ </tex-math></inline-formula>-MLAA contributed more significantly compared to the <inline-formula> <tex-math>$lambda $ </tex","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 8","pages":"1025-1035"},"PeriodicalIF":3.5,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145435701","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 : 2025-02-21DOI: 10.1109/TRPMS.2025.3542994
Jan Debus;Werner Lustermann;Afroditi Eleftheriou;Matthias Wyss;Bruno Weber;Günther Dissertori
SAFIR-II is a preclinical PET insert compatible with a Bruker BioSpec 70/30 magnetic resonance imaging (MRI) scanner. It was designed to acquire data at activities of up to 500 MBq, enabling truly simultaneous preclinical positron emission tomography magnetic resonance imaging for mice and rats using image acquisition times of as little as 5 s. We present a brief overview of the system’s design as well as the results of several performance evaluations. SAFIR-II features an axial field-of-view (FOV) of 145 mm, covered by lutetium-yttrium oxyorthosilicate crystals coupled to Hamamatsu silicon photomultiplier (SiPM) arrays. PETA8 application-specific integrated circuits are used to digitize the SiPM’s analog signals, and custom MR-compatible dc-dc converters condition the system’s internal voltages. The insert exhibits a coincidence timing resolution of 221-ps full width at half maximum (FWHM), a coincidence energy resolution of 12.1%, and a peak sensitivity of 3.89% observed following the NEMA-NU4 standard. It is capable of resolving 1.7-mm hot rods within a Derenzo phantom filled with $^{18}{mathrm { F}}$ and features a peak noise-equivalent count rate of 1.12 Mcps observed at an activity of 451 MBq using the NEMA rat-like phantom. We furthermore present an evaluation of the system’s image quality determined using a NEMA image quality phantom, an evaluation of its MRI-compatibility, as well as images from an initial in vivo measurement using a Sprague-Dawley rat injected with 283-MBq fluordesoxyglucose.
{"title":"SAFIR-II: Performance Evaluation of a High-Rate Preclinical PET-MR System","authors":"Jan Debus;Werner Lustermann;Afroditi Eleftheriou;Matthias Wyss;Bruno Weber;Günther Dissertori","doi":"10.1109/TRPMS.2025.3542994","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3542994","url":null,"abstract":"SAFIR-II is a preclinical PET insert compatible with a Bruker BioSpec 70/30 magnetic resonance imaging (MRI) scanner. It was designed to acquire data at activities of up to 500 MBq, enabling truly simultaneous preclinical positron emission tomography magnetic resonance imaging for mice and rats using image acquisition times of as little as 5 s. We present a brief overview of the system’s design as well as the results of several performance evaluations. SAFIR-II features an axial field-of-view (FOV) of 145 mm, covered by lutetium-yttrium oxyorthosilicate crystals coupled to Hamamatsu silicon photomultiplier (SiPM) arrays. PETA8 application-specific integrated circuits are used to digitize the SiPM’s analog signals, and custom MR-compatible dc-dc converters condition the system’s internal voltages. The insert exhibits a coincidence timing resolution of 221-ps full width at half maximum (FWHM), a coincidence energy resolution of 12.1%, and a peak sensitivity of 3.89% observed following the NEMA-NU4 standard. It is capable of resolving 1.7-mm hot rods within a Derenzo phantom filled with <inline-formula> <tex-math>$^{18}{mathrm { F}}$ </tex-math></inline-formula> and features a peak noise-equivalent count rate of 1.12 Mcps observed at an activity of 451 MBq using the NEMA rat-like phantom. We furthermore present an evaluation of the system’s image quality determined using a NEMA image quality phantom, an evaluation of its MRI-compatibility, as well as images from an initial in vivo measurement using a Sprague-Dawley rat injected with 283-MBq fluordesoxyglucose.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 7","pages":"951-958"},"PeriodicalIF":3.5,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144996102","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}
Time resolution in time-of-flight positron emission tomography (TOF-PET) has improved significantly over the last decade due to advancements in scintillation materials, photodetectors, and readout electronics, which has increased the signal-to-noise ratio (SNR) compared to conventional positron emission tomography. Silicon photomultipliers (SiPMs) in TOF-PET detectors are often operated at high bias voltage to improve the time performance at the expense of increasing signal noise. SiPM noise, both correlated and uncorrelated, can cause baseline fluctuations, leading to time-walk effects when a leading edge trigger strategy is used, and thus limiting timing performance. We examined the effect of SiPM baseline fluctuations using the FastIC ASIC, a scalable multichannel readout for fast timing applications. We flagged noisy events by using a comparator signal triggered by dark counts before the actual scintillation event. We tested different classification and correction methods with scintillating crystals and Cherenkov radiators, coupled to analog SiPMs from Broadcom (NUV-MT) and Hamamatsu Photonics. We reduced the coincidence time resolution (CTR) in bismuth germanate $2times 2times $ 3 mm3 (BGO) crystals from $410~pm ~10$ to $388~pm ~10$ ps FWHM (5%) by correcting the time-walk on the noisy events. We measured an improvement from $107~pm 2$ to $93.5~pm ~0.6$ ps (11%) for LYSO $2times 2times $ 3 mm3 crystals by filtering the noisy events. An improvement of 9% on the CTR of the EJ232 plastic scintillator was also achieved by filtering noisy events, reducing it from $82.2~pm ~0.5$ to $75~pm ~1$ ps. This study presents a scalable method for flagging undesired events in a full TOF-PET system and discusses the impact of SiPM noise on the FastIC readout.
{"title":"Improving CTR With the FastIC ASIC for TOF-PET by Overcoming SiPM Noise With Baseline Correction","authors":"Afonso Silvério Xavier De Matos Pinto;Nicolaus Kratochwil;Sergio Gómez;David Gascón;Pedro Correia;João Veloso;Emilie Roncali;Ana Luísa Silva;Gerard Ariño-Estrada","doi":"10.1109/TRPMS.2025.3532794","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3532794","url":null,"abstract":"Time resolution in time-of-flight positron emission tomography (TOF-PET) has improved significantly over the last decade due to advancements in scintillation materials, photodetectors, and readout electronics, which has increased the signal-to-noise ratio (SNR) compared to conventional positron emission tomography. Silicon photomultipliers (SiPMs) in TOF-PET detectors are often operated at high bias voltage to improve the time performance at the expense of increasing signal noise. SiPM noise, both correlated and uncorrelated, can cause baseline fluctuations, leading to time-walk effects when a leading edge trigger strategy is used, and thus limiting timing performance. We examined the effect of SiPM baseline fluctuations using the FastIC ASIC, a scalable multichannel readout for fast timing applications. We flagged noisy events by using a comparator signal triggered by dark counts before the actual scintillation event. We tested different classification and correction methods with scintillating crystals and Cherenkov radiators, coupled to analog SiPMs from Broadcom (NUV-MT) and Hamamatsu Photonics. We reduced the coincidence time resolution (CTR) in bismuth germanate <inline-formula> <tex-math>$2times 2times $ </tex-math></inline-formula>3 mm3 (BGO) crystals from <inline-formula> <tex-math>$410~pm ~10$ </tex-math></inline-formula> to <inline-formula> <tex-math>$388~pm ~10$ </tex-math></inline-formula> ps FWHM (5%) by correcting the time-walk on the noisy events. We measured an improvement from <inline-formula> <tex-math>$107~pm 2$ </tex-math></inline-formula> to <inline-formula> <tex-math>$93.5~pm ~0.6$ </tex-math></inline-formula> ps (11%) for LYSO <inline-formula> <tex-math>$2times 2times $ </tex-math></inline-formula>3 mm3 crystals by filtering the noisy events. An improvement of 9% on the CTR of the EJ232 plastic scintillator was also achieved by filtering noisy events, reducing it from <inline-formula> <tex-math>$82.2~pm ~0.5$ </tex-math></inline-formula> to <inline-formula> <tex-math>$75~pm ~1$ </tex-math></inline-formula> ps. This study presents a scalable method for flagging undesired events in a full TOF-PET system and discusses the impact of SiPM noise on the FastIC readout.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 7","pages":"857-865"},"PeriodicalIF":3.5,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10893703","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998050","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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