Radiopharmaceutical therapy (RPT) is an established treatment modality and is of increasing interest for different cancer types. A key unmet need, both in the wider adoption of RPT and in the improvement of outcomes with existing RPTs, is in treatment planning and optimization. Research efforts have been hindered by the incomplete understanding of the radiobiology RPTs. Modeling in RPT often mirrors external beam radiotherapy (EBRT), despite key differences. The dose rate (DR) is notably distinct between the two, influencing radiation responses. In EBRT, radiation is acutely delivered in discrete transient fractions of relatively short duration, with a near-constant DR. In RPT, by contrast, exposure is gradual, protracted, and characterized by temporal nonuniformities arising from organ-specific radio-pharmacokinetics. As a result, low-DR (LDR) radiobiology adapted for RPT (LDR-RPT) has emerged as a vibrant area of research. In this review, we discuss the state-of-the-art understanding of the etiological mechanisms underlying cellular and tissue-level dose responses in LDR-RPT, with a focus on how this radiobiological knowledge is codified in mathematical and computational models. We also describe current feasibility and future prospects for utilizing such quantitative radiobiological models to perform personalized RPT planning and highlight research directions that should be prioritized to accelerate clinical adoption.
{"title":"Shifting the Spotlight to Low-Dose Rate Radiobiology in Radiopharmaceutical Therapies: Mathematical Modeling, Challenges, and Future Directions","authors":"Hamid Abdollahi;Babak Saboury;Tahir Yusufaly;Ian Alberts;Carlos Uribe;Arman Rahmim","doi":"10.1109/TRPMS.2025.3540739","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3540739","url":null,"abstract":"Radiopharmaceutical therapy (RPT) is an established treatment modality and is of increasing interest for different cancer types. A key unmet need, both in the wider adoption of RPT and in the improvement of outcomes with existing RPTs, is in treatment planning and optimization. Research efforts have been hindered by the incomplete understanding of the radiobiology RPTs. Modeling in RPT often mirrors external beam radiotherapy (EBRT), despite key differences. The dose rate (DR) is notably distinct between the two, influencing radiation responses. In EBRT, radiation is acutely delivered in discrete transient fractions of relatively short duration, with a near-constant DR. In RPT, by contrast, exposure is gradual, protracted, and characterized by temporal nonuniformities arising from organ-specific radio-pharmacokinetics. As a result, low-DR (LDR) radiobiology adapted for RPT (LDR-RPT) has emerged as a vibrant area of research. In this review, we discuss the state-of-the-art understanding of the etiological mechanisms underlying cellular and tissue-level dose responses in LDR-RPT, with a focus on how this radiobiological knowledge is codified in mathematical and computational models. We also describe current feasibility and future prospects for utilizing such quantitative radiobiological models to perform personalized RPT planning and highlight research directions that should be prioritized to accelerate clinical adoption.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 7","pages":"843-856"},"PeriodicalIF":3.5,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10891667","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998067","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 : 2025-02-17DOI: 10.1109/TRPMS.2025.3542729
Liang Guo;Stephanie L. Thorn;Pedro Gil de Rubio Cruz;Zhao Liu;Jean-Dominique Gallezot;Qiong Liu;Eric Moulton;Richard E. Carson;Albert J. Sinusas;Chi Liu
Accurate assessment of regional flow in the lower extremities is crucial for managing peripheral arterial disease with critical limb ischemia. This study investigates dynamic 82Rb PET imaging with kinetic modeling for evaluating skeletal muscle flow in a porcine model of hindlimb ischemia. Five pigs with acute unilateral occlusion of the right common femoral artery were scanned at rest using two protocols: The first protocol involved two sequential injections to measure the image-derived input function (IDIF) in the left ventricle (LV) and leg blood flow. A three-parameter one-tissue compartment with spillover model estimated skeletal muscle flow in ischemic and nonischemic limbs. The effects of correcting delay and dispersion of LV-IDIF on model fitting were explored. For short axial field of view scanners, the feasibility of a single injection with shuttling between the heart and the leg was also assessed. Flow estimates ranged from 0.012 to 0.077 cm3/min/cm3 across animals and significantly decreased on ischemic muscles (p < 0.05). Delay and dispersion corrections yielded improved Akaike information criterion values and physiological consistency. However, accurate corrections were more difficult using the single injection and shuttling protocol. Future studies to optimize data acquisition are needed.
{"title":"Lower Extremity Flow Quantification Using Dynamic ⁸²Rb PET: A Preclinical Investigation","authors":"Liang Guo;Stephanie L. Thorn;Pedro Gil de Rubio Cruz;Zhao Liu;Jean-Dominique Gallezot;Qiong Liu;Eric Moulton;Richard E. Carson;Albert J. Sinusas;Chi Liu","doi":"10.1109/TRPMS.2025.3542729","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3542729","url":null,"abstract":"Accurate assessment of regional flow in the lower extremities is crucial for managing peripheral arterial disease with critical limb ischemia. This study investigates dynamic 82Rb PET imaging with kinetic modeling for evaluating skeletal muscle flow in a porcine model of hindlimb ischemia. Five pigs with acute unilateral occlusion of the right common femoral artery were scanned at rest using two protocols: The first protocol involved two sequential injections to measure the image-derived input function (IDIF) in the left ventricle (LV) and leg blood flow. A three-parameter one-tissue compartment with spillover model estimated skeletal muscle flow in ischemic and nonischemic limbs. The effects of correcting delay and dispersion of LV-IDIF on model fitting were explored. For short axial field of view scanners, the feasibility of a single injection with shuttling between the heart and the leg was also assessed. Flow estimates ranged from 0.012 to 0.077 cm3/min/cm3 across animals and significantly decreased on ischemic muscles (p < 0.05). Delay and dispersion corrections yielded improved Akaike information criterion values and physiological consistency. However, accurate corrections were more difficult using the single injection and shuttling protocol. Future studies to optimize data acquisition are needed.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 7","pages":"918-926"},"PeriodicalIF":3.5,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998087","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 : 2025-02-14DOI: 10.1109/TRPMS.2025.3542024
Zhixiang Zhao;Qiu Huang;Craig S. Levin
This study introduces and evaluates a new front-end electronics design for time-of-flight (TOF) 3-D position sensitive (TOF-3-DPS) detectors with a side-readout configuration. This design employs an RF amplifier and summing circuit-based timing multiplexing scheme to achieve 24:1 timing multiplexing. Additionally, complex programmable logic devices are utilized for precise energy measurement and 3-D positioning, accommodating both single and multiinteraction intercrystal scatter (ICS) events within a detector unit. Experimental results on a single $3times 3times $ 10 mm3 LYSO:Ce crystal side-coupled to three $3times $ 3 mm2 SiPMs in a $4times 6$ SiPM array demonstrated a $9.17pm 0.20$ % energy resolution, a $1.20pm 0$ .26 mm FWHM depth-of-interaction (DOI) resolution, and a $112.46pm 1.91$ ps FWHM coincidence time resolution (CTR) after DOI-related time skew correction. Further tests on a detector unit comprising a $4times 2$ array of $3times 3times $ 10 mm3 LYSO:Ce crystals, side-coupled with the same $4times 6$ SiPM array, yielded a $10.56pm 1.05$ % energy resolution and a $121.28pm 3.35$ ps FWHM DOI-calibrated CTR. The ICS event ratio for each crystal element within the detector unit was also preliminarily assessed. The front-end readout circuit consumes approximately 0.75 W per 24-SiPMs detector unit and features a compact $27times $ 95 mm2 footprint capable of reading out two units, enabling easy stacking of multiple units to form a complete TOF-3-DPS detector module.
{"title":"Front-End Electronics Design for 3-D Position Sensitive TOF-PET Detector That Achieves ~120-ps CTR and ~1.2-mm DOI Resolution","authors":"Zhixiang Zhao;Qiu Huang;Craig S. Levin","doi":"10.1109/TRPMS.2025.3542024","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3542024","url":null,"abstract":"This study introduces and evaluates a new front-end electronics design for time-of-flight (TOF) 3-D position sensitive (TOF-3-DPS) detectors with a side-readout configuration. This design employs an RF amplifier and summing circuit-based timing multiplexing scheme to achieve 24:1 timing multiplexing. Additionally, complex programmable logic devices are utilized for precise energy measurement and 3-D positioning, accommodating both single and multiinteraction intercrystal scatter (ICS) events within a detector unit. Experimental results on a single <inline-formula> <tex-math>$3times 3times $ </tex-math></inline-formula> 10 mm3 LYSO:Ce crystal side-coupled to three <inline-formula> <tex-math>$3times $ </tex-math></inline-formula> 3 mm2 SiPMs in a <inline-formula> <tex-math>$4times 6$ </tex-math></inline-formula> SiPM array demonstrated a <inline-formula> <tex-math>$9.17pm 0.20$ </tex-math></inline-formula>% energy resolution, a <inline-formula> <tex-math>$1.20pm 0$ </tex-math></inline-formula>.26 mm FWHM depth-of-interaction (DOI) resolution, and a <inline-formula> <tex-math>$112.46pm 1.91$ </tex-math></inline-formula> ps FWHM coincidence time resolution (CTR) after DOI-related time skew correction. Further tests on a detector unit comprising a <inline-formula> <tex-math>$4times 2$ </tex-math></inline-formula> array of <inline-formula> <tex-math>$3times 3times $ </tex-math></inline-formula> 10 mm3 LYSO:Ce crystals, side-coupled with the same <inline-formula> <tex-math>$4times 6$ </tex-math></inline-formula> SiPM array, yielded a <inline-formula> <tex-math>$10.56pm 1.05$ </tex-math></inline-formula>% energy resolution and a <inline-formula> <tex-math>$121.28pm 3.35$ </tex-math></inline-formula> ps FWHM DOI-calibrated CTR. The ICS event ratio for each crystal element within the detector unit was also preliminarily assessed. The front-end readout circuit consumes approximately 0.75 W per 24-SiPMs detector unit and features a compact <inline-formula> <tex-math>$27times $ </tex-math></inline-formula> 95 mm2 footprint capable of reading out two units, enabling easy stacking of multiple units to form a complete TOF-3-DPS detector module.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 6","pages":"736-746"},"PeriodicalIF":4.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10887305","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597922","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}
Computed tomography (CT) is widely used to generate cross-sectional views of the internal anatomy of a subject. Conventional CT imaging with single energy is, however, incapable of providing material composition information for various clinical applications because different materials may lead to the same CT numbers. Dual-energy CT (DECT) with physical means of simultaneously generating and measuring photon signals of two different spectra is designed to break this degeneracy. While valuable, this approach adds an extra layer of complexity on top of the widely used single-energy CT (SECT) and increases system costs, hindering the use of DECT scanners in less developed regions. Leveraging the ability of deep learning in nonlinear mapping and prior knowledge extraction from routine clinical data, here we develop a data-driven, lightweight strategy of obtaining DECT images from SECT images using a physically constrained attention mechanism. The proposed strategy is evaluated comprehensively by using high-fidelity simulation datasets and clinical contrast-enhanced DECT datasets. In terms of both prediction accuracy and inference speed, our method exhibits notable advantages over a variety of existing approaches. This technique holds the potential to provide a fast and cost-effective solution for contrast-enhanced spectral CT, catering to a broad range of CT applications.
{"title":"Data-Driven Contrast-Enhanced Dual-Energy CT Imaging via Physically Constrained Attention","authors":"Wenwen Zhang;Tianling Lyu;Yongqing Li;Yang Chen;Baohua Sun;Wei Zhao","doi":"10.1109/TRPMS.2025.3541742","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3541742","url":null,"abstract":"Computed tomography (CT) is widely used to generate cross-sectional views of the internal anatomy of a subject. Conventional CT imaging with single energy is, however, incapable of providing material composition information for various clinical applications because different materials may lead to the same CT numbers. Dual-energy CT (DECT) with physical means of simultaneously generating and measuring photon signals of two different spectra is designed to break this degeneracy. While valuable, this approach adds an extra layer of complexity on top of the widely used single-energy CT (SECT) and increases system costs, hindering the use of DECT scanners in less developed regions. Leveraging the ability of deep learning in nonlinear mapping and prior knowledge extraction from routine clinical data, here we develop a data-driven, lightweight strategy of obtaining DECT images from SECT images using a physically constrained attention mechanism. The proposed strategy is evaluated comprehensively by using high-fidelity simulation datasets and clinical contrast-enhanced DECT datasets. In terms of both prediction accuracy and inference speed, our method exhibits notable advantages over a variety of existing approaches. This technique holds the potential to provide a fast and cost-effective solution for contrast-enhanced spectral CT, catering to a broad range of CT applications.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 7","pages":"905-917"},"PeriodicalIF":3.5,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10884935","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998090","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 : 2025-02-12DOI: 10.1109/TRPMS.2025.3541169
Jiping Wang;Hao Fan;Zhongyi Wu;Qiang Du;Ming Li;Jian Zheng;Greta S. P. Mok;Benjamin M. W. Tsui
Low-dose computed tomography (LDCT) denoising methods based on supervised learning with labeled simulation data have made significant progress. However, these methods usually struggle to directly process unlabeled LDCT images due to inherent biases. While unsupervised methods have been explored to utilize unlabeled LDCT images, they typically involve complex network structures with limited denoising performance. To address these issues, we propose a self-adaptive weight embedded lightweight semi-supervised network (SWELNet) for unlabeled LDCT image denoising, which integrates supervised and unsupervised learning in a lightweight architecture. Unlike other semi-supervised algorithms that only consider the correlations between labeled simulation data and unlabeled data, the proposed SWELNet not only takes into account correlations but also the differences between data. There are three modules in the proposed network, respectively, for feature extraction, refinement and self-adaptive weight. Specially, the multiscale convolution feature extraction module (MCFEM) and recursive module (RECM) extract and refine common representations from labeled simulation and unlabeled data with the well-designed. After that, the softmax feature fusion module (SFFM) with self-adaptive weighted learning for forming different feature spaces for two types of data. Extensive experiments using one simulation and two unlabeled datasets demonstrate that the proposed SWELNet outperforms several state-of-the-art baseline network methods in terms of robustness and generalization, as well as inference efficiency. The code is available at https://github.com/nightastars/SWELNet-main.git.
{"title":"Self-Adaptive Weight Embedded Lightweight Network Using Semi-Supervised Learning for Low-Dose CT Image Denoising","authors":"Jiping Wang;Hao Fan;Zhongyi Wu;Qiang Du;Ming Li;Jian Zheng;Greta S. P. Mok;Benjamin M. W. Tsui","doi":"10.1109/TRPMS.2025.3541169","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3541169","url":null,"abstract":"Low-dose computed tomography (LDCT) denoising methods based on supervised learning with labeled simulation data have made significant progress. However, these methods usually struggle to directly process unlabeled LDCT images due to inherent biases. While unsupervised methods have been explored to utilize unlabeled LDCT images, they typically involve complex network structures with limited denoising performance. To address these issues, we propose a self-adaptive weight embedded lightweight semi-supervised network (SWELNet) for unlabeled LDCT image denoising, which integrates supervised and unsupervised learning in a lightweight architecture. Unlike other semi-supervised algorithms that only consider the correlations between labeled simulation data and unlabeled data, the proposed SWELNet not only takes into account correlations but also the differences between data. There are three modules in the proposed network, respectively, for feature extraction, refinement and self-adaptive weight. Specially, the multiscale convolution feature extraction module (MCFEM) and recursive module (RECM) extract and refine common representations from labeled simulation and unlabeled data with the well-designed. After that, the softmax feature fusion module (SFFM) with self-adaptive weighted learning for forming different feature spaces for two types of data. Extensive experiments using one simulation and two unlabeled datasets demonstrate that the proposed SWELNet outperforms several state-of-the-art baseline network methods in terms of robustness and generalization, as well as inference efficiency. The code is available at <uri>https://github.com/nightastars/SWELNet-main.git</uri>.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 7","pages":"890-904"},"PeriodicalIF":3.5,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998009","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 : 2025-02-12DOI: 10.1109/TRPMS.2025.3539739
Jiaqi Cui;Yuanyuan Xu;Hanci Zheng;Xi Wu;Jiliu Zhou;Yuanjun Liu;Yan Wang
Survival prediction is crucial for cancer patients as it offers prognostic information for treatment planning. Recently, deep learning-based multimodal survival prediction models have demonstrated promising performance. However, current models face challenges in effectively utilizing heterogeneous multimodal data (e.g., positron emission tomography (PET)/computed tomography (CT) images and clinical tabular) and extracting essential information from tumor regions, resulting in suboptimal survival prediction accuracy. To tackle these limitations, in this article, we propose a novel hybrid multimodal transformer model (HMT), namely HMT, for survival prediction from PET/CT images and clinical tabular in Head and Neck (H&N) cancer. Specifically, we develop hybrid attention modules to capture intramodal information and intermodal correlations from multimodal PET/CT images. Moreover, we design hierarchical Tabular Affine transformation modules (TATMs) to integrate supplementary insights from the heterogenous tabular with images via affine transformations. The TATM dynamically emphasizes features contributing to the survival prediction while suppressing irrelevant ones during integration. To achieve finer feature fusion, TATMs are hierarchically embedded into the network, allowing for consistent interaction between tabular and multimodal image features across multiple scales. To mitigate interferences caused by irrelevant information, we introduce tumor segmentation as an auxiliary task to capture features related to tumor regions, thus enhancing prediction accuracy. Experiments demonstrate our superior performance. The code is available at https://github.com/gluucose/HMT.
{"title":"HMT: A Hybrid Multimodal Transformer With Multitask Learning for Survival Prediction in Head and Neck Cancer","authors":"Jiaqi Cui;Yuanyuan Xu;Hanci Zheng;Xi Wu;Jiliu Zhou;Yuanjun Liu;Yan Wang","doi":"10.1109/TRPMS.2025.3539739","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3539739","url":null,"abstract":"Survival prediction is crucial for cancer patients as it offers prognostic information for treatment planning. Recently, deep learning-based multimodal survival prediction models have demonstrated promising performance. However, current models face challenges in effectively utilizing heterogeneous multimodal data (e.g., positron emission tomography (PET)/computed tomography (CT) images and clinical tabular) and extracting essential information from tumor regions, resulting in suboptimal survival prediction accuracy. To tackle these limitations, in this article, we propose a novel hybrid multimodal transformer model (HMT), namely HMT, for survival prediction from PET/CT images and clinical tabular in Head and Neck (H&N) cancer. Specifically, we develop hybrid attention modules to capture intramodal information and intermodal correlations from multimodal PET/CT images. Moreover, we design hierarchical Tabular Affine transformation modules (TATMs) to integrate supplementary insights from the heterogenous tabular with images via affine transformations. The TATM dynamically emphasizes features contributing to the survival prediction while suppressing irrelevant ones during integration. To achieve finer feature fusion, TATMs are hierarchically embedded into the network, allowing for consistent interaction between tabular and multimodal image features across multiple scales. To mitigate interferences caused by irrelevant information, we introduce tumor segmentation as an auxiliary task to capture features related to tumor regions, thus enhancing prediction accuracy. Experiments demonstrate our superior performance. The code is available at <uri>https://github.com/gluucose/HMT</uri>.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 7","pages":"879-889"},"PeriodicalIF":3.5,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997932","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 : 2025-02-10DOI: 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}
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