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}
Pub Date : 2025-01-20DOI: 10.1109/TRPMS.2025.3531536
Pablo Cabrales;Víctor V. Onecha;David Izquierdo-García;Luis Mario Fraile;José Manuel Udías;Joaquín L. Herraiz
In proton therapy (PT), accurate dose delivery verification is critical for detecting treatment plan deviations. This can be achieved by imaging activated positron emitters with a positron emission tomography (PET) acquisition and converting the data into a delivered dose image. This work presents PROTOTWIN-PET (PROTOn therapy digital TWIN models for dose verification with PET), a patient-specific, deep learning (DL) and GPU-based workflow for 3-D dose verification. The proposed workflow generates a dataset of simulated, realistic 3-D PET and dose pairs that reflect possible clinical deviations in patient positioning and physical parameters. Using this dataset, a DL model is trained to estimate the delivered dose from the PET image, incorporating a deviation-predicting branch (DPB) to estimate patient positioning deviations. PROTOTWIN-PET is demonstrated on a two-field oropharyngeal cancer treatment plan, estimating the delivered dose in milliseconds with an average mean relative error of 0.6% and near-perfect gamma passing rates (3 mm, 3%). Positioning deviations are estimated on average within a tenth of a millimeter and degree. PROTOTWIN-PET can be implemented within the one-day interval between the plan CT acquisition and the first treatment session, potentially enabling timely treatment plan adjustments and maximizing the precision of PT. PROTOTWIN-PET is available at github.com/pcabrales/prototwin-pet.git.
在质子治疗（PT）中，准确的剂量传递验证对于检测治疗计划偏差至关重要。这可以通过使用正电子发射断层扫描（PET）采集对激活的正电子发射体进行成像并将数据转换为交付的剂量图像来实现。这项工作提出了PROTOTWIN-PET（用于PET剂量验证的质子治疗数字TWIN模型），这是一种针对患者的、基于深度学习（DL）和gpu的3d剂量验证工作流程。所提出的工作流程生成一个模拟的、真实的3-D PET和剂量对的数据集，这些数据集反映了患者体位和身体参数可能的临床偏差。使用该数据集，训练DL模型来估计PET图像中的放射剂量，并结合偏差预测分支（DPB）来估计患者的定位偏差。PROTOTWIN-PET在双场口咽癌治疗方案中得到验证，以毫秒为单位估计剂量，平均相对误差为0.6%，伽玛通过率接近完美（3 mm, 3%）。定位偏差估计平均在十分之一毫米和度以内。PROTOTWIN-PET可以在计划CT采集和第一次治疗之间的一天间隔内实施，有可能及时调整治疗计划并最大化PT的精度。PROTOTWIN-PET可在github.com/pcabrales/prototwin-pet.git上获得。
{"title":"PROTOTWIN-PET: A Deep Learning and GPU-Based Workflow for Dose Verification in Proton Therapy With PET","authors":"Pablo Cabrales;Víctor V. Onecha;David Izquierdo-García;Luis Mario Fraile;José Manuel Udías;Joaquín L. Herraiz","doi":"10.1109/TRPMS.2025.3531536","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3531536","url":null,"abstract":"In proton therapy (PT), accurate dose delivery verification is critical for detecting treatment plan deviations. This can be achieved by imaging activated positron emitters with a positron emission tomography (PET) acquisition and converting the data into a delivered dose image. This work presents PROTOTWIN-PET (PROTOn therapy digital TWIN models for dose verification with PET), a patient-specific, deep learning (DL) and GPU-based workflow for 3-D dose verification. The proposed workflow generates a dataset of simulated, realistic 3-D PET and dose pairs that reflect possible clinical deviations in patient positioning and physical parameters. Using this dataset, a DL model is trained to estimate the delivered dose from the PET image, incorporating a deviation-predicting branch (DPB) to estimate patient positioning deviations. PROTOTWIN-PET is demonstrated on a two-field oropharyngeal cancer treatment plan, estimating the delivered dose in milliseconds with an average mean relative error of 0.6% and near-perfect gamma passing rates (3 mm, 3%). Positioning deviations are estimated on average within a tenth of a millimeter and degree. PROTOTWIN-PET can be implemented within the one-day interval between the plan CT acquisition and the first treatment session, potentially enabling timely treatment plan adjustments and maximizing the precision of PT. PROTOTWIN-PET is available at github.com/pcabrales/prototwin-pet.git.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 6","pages":"821-831"},"PeriodicalIF":4.6,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10847605","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597712","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 : 2025-01-17DOI: 10.1109/TRPMS.2025.3531225
Hsin-Hsiung Huang;Zheyuan Zhu;Slun Booppasiri;Zhuo Chen;Shuo Pang;Chien-Min Kao
Positron emission tomography (PET) is an important modality for diagnosing diseases, such as cancer and Alzheimer’s disease, capable of revealing the uptake of radiolabeled molecules that target specific pathological markers of the diseases. Recently, positronium lifetime imaging (PLI) that adds to traditional PET the ability to explore properties of the tissue microenvironment beyond tracer uptake has been demonstrated with time-of-flight (TOF) PET and the use of nonpure positron emitters. However, achieving accurate reconstruction of lifetime images from data acquired by systems having a finite TOF resolution still presents a challenge. This article focuses on the 2-D PLI, introducing a maximum-likelihood estimation (MLE) method that employs an exponentially modified Gaussian (EMG) probability distribution that describes the positronium lifetime data produced by TOF PET. We evaluate the performance of our EMG-based MLE method against approaches using exponential likelihood functions and penalized surrogate methods. Results from computer-simulated data reveal that the proposed EMG-MLE method can yield quantitatively accurate lifetime images. We also demonstrate that the proposed MLE formulation can be extended to handle PLI data containing multiple positron populations.
{"title":"A Statistical Reconstruction Algorithm for Positronium Lifetime Imaging Using Time-of-Flight Positron Emission Tomography","authors":"Hsin-Hsiung Huang;Zheyuan Zhu;Slun Booppasiri;Zhuo Chen;Shuo Pang;Chien-Min Kao","doi":"10.1109/TRPMS.2025.3531225","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3531225","url":null,"abstract":"Positron emission tomography (PET) is an important modality for diagnosing diseases, such as cancer and Alzheimer’s disease, capable of revealing the uptake of radiolabeled molecules that target specific pathological markers of the diseases. Recently, positronium lifetime imaging (PLI) that adds to traditional PET the ability to explore properties of the tissue microenvironment beyond tracer uptake has been demonstrated with time-of-flight (TOF) PET and the use of nonpure positron emitters. However, achieving accurate reconstruction of lifetime images from data acquired by systems having a finite TOF resolution still presents a challenge. This article focuses on the 2-D PLI, introducing a maximum-likelihood estimation (MLE) method that employs an exponentially modified Gaussian (EMG) probability distribution that describes the positronium lifetime data produced by TOF PET. We evaluate the performance of our EMG-based MLE method against approaches using exponential likelihood functions and penalized surrogate methods. Results from computer-simulated data reveal that the proposed EMG-MLE method can yield quantitatively accurate lifetime images. We also demonstrate that the proposed MLE formulation can be extended to handle PLI data containing multiple positron populations.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"478-486"},"PeriodicalIF":4.6,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10844904","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761358","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 : 2025-01-16DOI: 10.1109/TRPMS.2025.3528728
Weijie Gan;Huidong Xie;Carl von Gall;Günther Platsch;Michael T. Jurkiewicz;Andrea Andrade;Udunna C. Anazodo;Ulugbek S. Kamilov;Hongyu An;Jorge Cabello
Anatomically guided positron emission tomography (PET) reconstruction using magnetic resonance imaging (MRI) information has been shown to have the potential to improve PET image quality. However, these improvements are limited to PET scans with paired MRI information. In this work, we employed a diffusion probabilistic model (DPM) to infer T1-weighted-MRI (deep-MRI) images from FDG-PET brain images. We then use the DPM-generated T1w-MRI to guide the PET reconstruction. The model was trained with brain FDG scans, and tested in datasets containing multiple levels of counts. Deep-MRI images appeared somewhat degraded and in some cases showed inaccuracies compared to the acquired MRI images. Regarding PET image quality, volume of interest analysis in different brain regions showed that both PET reconstructed images using the acquired and the deep-MRI images improved image quality compared to ordered subset expected maximum (OSEM). Same conclusions were found analysing the decimated datasets. A subjective evaluation performed by two physicians confirmed that OSEM scored consistently worse than the MRI-guided PET images and no significant differences were observed between the MRI-guided PET images. This proof of concept shows that it is possible to infer DPM-based MRI imagery to guide the PET reconstruction, enabling the possibility of changing reconstruction parameters, such as the strength of the prior on anatomically guided PET reconstruction in the absence of MRI.
{"title":"Pseudo-MRI-Guided PET Image Reconstruction Method Based on a Diffusion Probabilistic Model","authors":"Weijie Gan;Huidong Xie;Carl von Gall;Günther Platsch;Michael T. Jurkiewicz;Andrea Andrade;Udunna C. Anazodo;Ulugbek S. Kamilov;Hongyu An;Jorge Cabello","doi":"10.1109/TRPMS.2025.3528728","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3528728","url":null,"abstract":"Anatomically guided positron emission tomography (PET) reconstruction using magnetic resonance imaging (MRI) information has been shown to have the potential to improve PET image quality. However, these improvements are limited to PET scans with paired MRI information. In this work, we employed a diffusion probabilistic model (DPM) to infer T1-weighted-MRI (deep-MRI) images from FDG-PET brain images. We then use the DPM-generated T1w-MRI to guide the PET reconstruction. The model was trained with brain FDG scans, and tested in datasets containing multiple levels of counts. Deep-MRI images appeared somewhat degraded and in some cases showed inaccuracies compared to the acquired MRI images. Regarding PET image quality, volume of interest analysis in different brain regions showed that both PET reconstructed images using the acquired and the deep-MRI images improved image quality compared to ordered subset expected maximum (OSEM). Same conclusions were found analysing the decimated datasets. A subjective evaluation performed by two physicians confirmed that OSEM scored consistently worse than the MRI-guided PET images and no significant differences were observed between the MRI-guided PET images. This proof of concept shows that it is possible to infer DPM-based MRI imagery to guide the PET reconstruction, enabling the possibility of changing reconstruction parameters, such as the strength of the prior on anatomically guided PET reconstruction in the absence of MRI.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"412-420"},"PeriodicalIF":4.6,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10843797","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761569","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 : 2025-01-16DOI: 10.1109/TRPMS.2025.3530774
S. Di Giacomo;M. Ronchi;M. Amadori;G. Borghi;M. Carminati;C. Fiorini
machine learning (ML) accelerators represent an attractive area of research, offering the potential to streamline algorithmic complexity and handle massively parallel in-memory computations, with substantial improvements in energy efficiency and speed related to data transmission and processing. Analog computing can further boost ML acceleration due to its superior computational density compared to digital platforms and its ability to deal with analog data acquired from sensors. The analog approach to edge computing can be beneficial for signal processing in long-axial field-of-view (LA-FOV) scintillation detectors used in nuclear medical tomographic imaging (PET and SPECT). In such scenarios, the deployment of analog computations in close proximity to the sensors would significantly diminish the volume of data that must be digitized and transmitted, and ML reconstruction algorithms, such as neural networks (NNs), could enhance the image reconstruction process. We present an ASIC fabricated in 0.35-$mathrm { {mu }text {m}}$ CMOS technology implementing an analog NN featuring 64 inputs, two hidden layers of 20 neurons each, and two outputs. It is intended for use in the reconstruction of the 2-D position of interaction of gamma photons inside a monolithic scintillator crystal readout by a matrix of silicon photomultipliers (SiPMs) for PET/SPECT applications. This chip can interact directly with analog signals originating from the photosensors, and is able to provide the predicted interaction coordinates of the gamma-ray at its output. The vector-matrix multiplications for inference are executed in the charge domain using programmable switched capacitors (SC) organized in crossbar arrays. Experimental measurements of this first proof-of-concept prototype ASIC are reported, demonstrating the correct functionality of the NN circuit. With an energy efficiency of $50~{mathrm {GOPS/W}}$ and power consumption of $17~{mathrm {mW}}$ per inference, the achieved results are promising for the integration of the ASIC with the photodetector front-end for in situ analog computing.
{"title":"Experimental Validation of ANNA: Analog Neural Network ASIC for Event Positioning in Monolithic Scintillation Detectors","authors":"S. Di Giacomo;M. Ronchi;M. Amadori;G. Borghi;M. Carminati;C. Fiorini","doi":"10.1109/TRPMS.2025.3530774","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3530774","url":null,"abstract":"machine learning (ML) accelerators represent an attractive area of research, offering the potential to streamline algorithmic complexity and handle massively parallel in-memory computations, with substantial improvements in energy efficiency and speed related to data transmission and processing. Analog computing can further boost ML acceleration due to its superior computational density compared to digital platforms and its ability to deal with analog data acquired from sensors. The analog approach to edge computing can be beneficial for signal processing in long-axial field-of-view (LA-FOV) scintillation detectors used in nuclear medical tomographic imaging (PET and SPECT). In such scenarios, the deployment of analog computations in close proximity to the sensors would significantly diminish the volume of data that must be digitized and transmitted, and ML reconstruction algorithms, such as neural networks (NNs), could enhance the image reconstruction process. We present an ASIC fabricated in 0.35-<inline-formula> <tex-math>$mathrm { {mu }text {m}}$ </tex-math></inline-formula> CMOS technology implementing an analog NN featuring 64 inputs, two hidden layers of 20 neurons each, and two outputs. It is intended for use in the reconstruction of the 2-D position of interaction of gamma photons inside a monolithic scintillator crystal readout by a matrix of silicon photomultipliers (SiPMs) for PET/SPECT applications. This chip can interact directly with analog signals originating from the photosensors, and is able to provide the predicted interaction coordinates of the gamma-ray at its output. The vector-matrix multiplications for inference are executed in the charge domain using programmable switched capacitors (SC) organized in crossbar arrays. Experimental measurements of this first proof-of-concept prototype ASIC are reported, demonstrating the correct functionality of the NN circuit. With an energy efficiency of <inline-formula> <tex-math>$50~{mathrm {GOPS/W}}$ </tex-math></inline-formula> and power consumption of <inline-formula> <tex-math>$17~{mathrm {mW}}$ </tex-math></inline-formula> per inference, the achieved results are promising for the integration of the ASIC with the photodetector front-end for in situ analog computing.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 5","pages":"542-552"},"PeriodicalIF":4.6,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900602","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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