Pub Date : 2025-04-30DOI: 10.1109/TRPMS.2025.3565797
Ran Hong;Yuxia Huang;Lei Liu;Mengxiao Geng;Zhonghui Wu;Bingxuan Li;Xuemei Wang;Qiegen Liu
PET imaging is widely employed for observing biological metabolic activities within the human body. However, numerous benign conditions can cause increased uptake of radiopharmaceuticals, confounding differentiation from malignant tumors. Several studies have indicated that dual-time PET imaging holds promise in distinguishing between malignant and benign tumor processes. Nevertheless, the hour-long distribution period of radiopharmaceuticals post-injection complicates the determination of optimal timing for the second scan, presenting challenges in both practical applications and research. Notably, we have identified that delay time PET imaging can be framed as an image-to-image conversion problem. Motivated by this insight, we propose a novel Spatial-Temporal guided diffusion transformer probabilistic model (st-DTPM) to solve dual-time PET imaging prediction problem. Specifically, this architecture leverages the U-net framework that integrates patch-wise features of CNN and pixel-wise relevance of transformer to obtain local and global information, and then employs a conditional DDPM model for image synthesis. Furthermore, on spatial condition, we concatenate early scan PET images and noisy PET images on every denoising step to guide the spatial distribution of denoising sampling. On temporal condition, we convert diffusion time steps and delay time to a universal time vector, then embed it to each layer of model architecture to further improve the accuracy of predictions. Experimental results demonstrated the superiority of our method over alternative approaches in preserving image quality and structural information, thereby affirming its efficacy in predictive task.
{"title":"st-DTPM: Spatial-Temporal Guided Diffusion Transformer Probabilistic Model for Delayed Scan PET Image Prediction","authors":"Ran Hong;Yuxia Huang;Lei Liu;Mengxiao Geng;Zhonghui Wu;Bingxuan Li;Xuemei Wang;Qiegen Liu","doi":"10.1109/TRPMS.2025.3565797","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3565797","url":null,"abstract":"PET imaging is widely employed for observing biological metabolic activities within the human body. However, numerous benign conditions can cause increased uptake of radiopharmaceuticals, confounding differentiation from malignant tumors. Several studies have indicated that dual-time PET imaging holds promise in distinguishing between malignant and benign tumor processes. Nevertheless, the hour-long distribution period of radiopharmaceuticals post-injection complicates the determination of optimal timing for the second scan, presenting challenges in both practical applications and research. Notably, we have identified that delay time PET imaging can be framed as an image-to-image conversion problem. Motivated by this insight, we propose a novel Spatial-Temporal guided diffusion transformer probabilistic model (st-DTPM) to solve dual-time PET imaging prediction problem. Specifically, this architecture leverages the U-net framework that integrates patch-wise features of CNN and pixel-wise relevance of transformer to obtain local and global information, and then employs a conditional DDPM model for image synthesis. Furthermore, on spatial condition, we concatenate early scan PET images and noisy PET images on every denoising step to guide the spatial distribution of denoising sampling. On temporal condition, we convert diffusion time steps and delay time to a universal time vector, then embed it to each layer of model architecture to further improve the accuracy of predictions. Experimental results demonstrated the superiority of our method over alternative approaches in preserving image quality and structural information, thereby affirming its efficacy in predictive task.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 1","pages":"26-40"},"PeriodicalIF":3.5,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861217","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}
Unsupervised learning methods effectively reduce the noise level of positron emission tomography (PET) images with limited training data. Recent research indicates that the performance of these methods is greatly influenced by the network architecture. However, there has been a lack of investigation into the optimal network architecture for unsupervised PET imaging in previous studies. To address this gap, we developed a neural architecture search method to search for a better network architecture for unsupervised PET image denoising tasks. Our approach searches the network architecture in two separate spaces: 1) the network-level search space and 2) the cell-level search space. Continuous relaxation techniques are utilized to reduce time consumption during the search process. In our proposed framework, high-count PET images were used to search the network architecture, while low-count PET images were used to optimize operation parameters. After identifying the optimal network architecture, we evaluated its performance on phantom data and patient data with a variety of tracers. Our experimental results demonstrated that the searched network outperformed other methods.
{"title":"Neural Architecture Search for Unsupervised PET Image Denoising","authors":"Jinming Li;Jing Wang;Yang Lv;Puming Zhang;Jun Zhao","doi":"10.1109/TRPMS.2025.3565655","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3565655","url":null,"abstract":"Unsupervised learning methods effectively reduce the noise level of positron emission tomography (PET) images with limited training data. Recent research indicates that the performance of these methods is greatly influenced by the network architecture. However, there has been a lack of investigation into the optimal network architecture for unsupervised PET imaging in previous studies. To address this gap, we developed a neural architecture search method to search for a better network architecture for unsupervised PET image denoising tasks. Our approach searches the network architecture in two separate spaces: 1) the network-level search space and 2) the cell-level search space. Continuous relaxation techniques are utilized to reduce time consumption during the search process. In our proposed framework, high-count PET images were used to search the network architecture, while low-count PET images were used to optimize operation parameters. After identifying the optimal network architecture, we evaluated its performance on phantom data and patient data with a variety of tracers. Our experimental results demonstrated that the searched network outperformed other methods.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 1","pages":"51-62"},"PeriodicalIF":3.5,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145860196","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 : 2025-04-21DOI: 10.1109/TRPMS.2025.3560667
Sanaz Nazari-Farsani;Mojtaba Jafaritadi;Jonathan Fisher;Myungheon Chin;Garry Chinn;Mehdi Khalighi;Greg Zaharchuk;Craig S. Levin
The signal-to-noise ratio (SNR) of positron emission tomography (PET) images is determined by several factors including the geometry of the scanner. Low system sensitivity caused by a short axial field of view (FOV) results in a low reconstructed image SNR that can complicate clinical decision-making. Therefore, a longer FOV is highly desirable (e.g., a total body geometry). However, this raises the scanner’s cost by increasing the volume of crystals, number of detectors, and readout electronics. We have developed a deep-learning framework to enhance the image quality of data acquired from a prototype brain-dedicated PET insert system for PET/MRI with an axial FOV of just 2.8 cm. We employed a retrospective analysis on 18F-fluorodeoxyglucose PET scans of 28 patients with either Glioblastoma (n = 9) or Alzheimer’s disease (n = 19) acquired on a commercial PET/MRI scanner with 60 cm diameter and 25 cm axial FOV. From this data we reconstructed low statistics PET images mimicking that acquired from the 2.8 cm axial FOV brain PET prototype using the 25-cm axial FOV commercial system dataset using a fault-tolerant reconstruction algorithm, which allowed us to constrain the count statistics from a set of detectors in a single ring of the latter system to match the geometry of the former system. A conditional generative adversarial network (cGAN) was trained and tested using the simulated short axial FOV images as input, with the paired 25 cm axial FOV image data as the target. We performed five-fold cross-validation and compared the deep learning (DL)-enhanced images to the target images using four metrics: 1) peak-signal-to-noise-ratio (PSNR); 2) root mean squared error (RMSE); 3) mean absolute error (MAE); and 4) structural similarity index (SSIM). The DL-enhanced PET images from the 2.8 cm axial FOV system had a median PSNR of 39.09 (interquartile range (IQR): 32.80–45.32), a median SSIM of 0.98 (IQR: 0.97–0.99), a median RMSE of 0.07 (IQR: 0.04–0.09), and a median MAE of 0.004 (IQR: 0.000–0.009). We also assessed the pretrained cGAN model’s performance in a zero-shot denoising task using patient data collected with our first generation PETcoil system. The ability of the cGAN model to enhance the quality of PET images acquired with a short axial FOV suggests a potential method to provide high-quality, high-accuracy images comparable to those of large axial FOV systems.
{"title":"Image SNR Enhancement for a Short Axial FOV Brain PET System Using Generative Deep Learning","authors":"Sanaz Nazari-Farsani;Mojtaba Jafaritadi;Jonathan Fisher;Myungheon Chin;Garry Chinn;Mehdi Khalighi;Greg Zaharchuk;Craig S. Levin","doi":"10.1109/TRPMS.2025.3560667","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3560667","url":null,"abstract":"The signal-to-noise ratio (SNR) of positron emission tomography (PET) images is determined by several factors including the geometry of the scanner. Low system sensitivity caused by a short axial field of view (FOV) results in a low reconstructed image SNR that can complicate clinical decision-making. Therefore, a longer FOV is highly desirable (e.g., a total body geometry). However, this raises the scanner’s cost by increasing the volume of crystals, number of detectors, and readout electronics. We have developed a deep-learning framework to enhance the image quality of data acquired from a prototype brain-dedicated PET insert system for PET/MRI with an axial FOV of just 2.8 cm. We employed a retrospective analysis on 18F-fluorodeoxyglucose PET scans of 28 patients with either Glioblastoma (n = 9) or Alzheimer’s disease (n = 19) acquired on a commercial PET/MRI scanner with 60 cm diameter and 25 cm axial FOV. From this data we reconstructed low statistics PET images mimicking that acquired from the 2.8 cm axial FOV brain PET prototype using the 25-cm axial FOV commercial system dataset using a fault-tolerant reconstruction algorithm, which allowed us to constrain the count statistics from a set of detectors in a single ring of the latter system to match the geometry of the former system. A conditional generative adversarial network (cGAN) was trained and tested using the simulated short axial FOV images as input, with the paired 25 cm axial FOV image data as the target. We performed five-fold cross-validation and compared the deep learning (DL)-enhanced images to the target images using four metrics: 1) peak-signal-to-noise-ratio (PSNR); 2) root mean squared error (RMSE); 3) mean absolute error (MAE); and 4) structural similarity index (SSIM). The DL-enhanced PET images from the 2.8 cm axial FOV system had a median PSNR of 39.09 (interquartile range (IQR): 32.80–45.32), a median SSIM of 0.98 (IQR: 0.97–0.99), a median RMSE of 0.07 (IQR: 0.04–0.09), and a median MAE of 0.004 (IQR: 0.000–0.009). We also assessed the pretrained cGAN model’s performance in a zero-shot denoising task using patient data collected with our first generation PETcoil system. The ability of the cGAN model to enhance the quality of PET images acquired with a short axial FOV suggests a potential method to provide high-quality, high-accuracy images comparable to those of large axial FOV systems.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 1","pages":"41-50"},"PeriodicalIF":3.5,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861200","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}
Boron Neutron Capture Therapy (BNCT) is an advanced cancer treatment that combines radiation therapy with targeted drug delivery. Patients are administered a boron compound that accumulates in tumour cells and are then irradiated with thermal neutrons that induce 10B(n,$alpha $ )7Li reactions, whose high-LET products locally deposit a high dose to tumour cells. The additional 478 keV gamma ray generated by the de-excitation of 7Li can be detected outside the patient’s body and can be used for dose localization and monitoring using the SPECT technique. In this study, we show the first experimental tomographic results obtained with a prototype BNCT-SPECT system at the LENA neutron facility in Pavia, Italy. Measurements are acquired with the BeNEdiCTE detection module, based on a 5 cm $times $ 5 cm $times $ 2 cm LaBr3(Ce+Sr) monolithic scintillator crystal coupled to an $8times 8$ matrix of Near Ultraviolet High-Density silicon photomultipliers (SiPMs). The system shows good performance in detecting the incoming radiation of interest and in reconstructing 2-D planar images of boron samples irradiated with thermal neutrons. Thanks to the aid of Software for Tomographic Image Reconstruction (STIR), we show a successful 3-D reconstruction of 2 vials containing 7371 ppm of 10B placed at 1.4 cm distance, starting from four partial projections and using 10 iterations of the Maximum Likelihood Expectation Maximization (MLEM) algorithm.
硼中子俘获疗法（BNCT）是一种结合放射治疗和靶向药物输送的晚期癌症治疗方法。患者服用在肿瘤细胞中积累的硼化合物，然后用热中子照射，诱导10B(n, $ α $)7Li反应，其高let产物在肿瘤细胞局部沉积高剂量。7Li去激发产生的额外478 keV伽马射线可以在患者体外检测到，并且可以使用SPECT技术用于剂量定位和监测。在这项研究中，我们展示了在意大利帕维亚的LENA中子设施使用原型BNCT-SPECT系统获得的第一个实验层析成像结果。BeNEdiCTE检测模块基于一个5 cm × 5 cm × 2 cm的LaBr3（Ce+Sr）单片闪烁体晶体，与一个8 × 8美元的近紫外高密度硅光电倍增管（SiPMs）矩阵耦合。该系统在探测感兴趣的入射辐射和重建受热中子辐照的硼样品的二维平面图像方面表现出良好的性能。在层析图像重建软件（STIR）的帮助下，我们展示了从四个部分投影开始，使用最大似然期望最大化（MLEM）算法的10次迭代，成功地对2个小瓶进行了3-D重建，其中含有7371 ppm的10B，放置在1.4厘米的距离上。
{"title":"Design and Validation of a SPECT Prototype for Treatment Monitoring in BNCT and First Experimental Tomographic Results","authors":"T. Ferri;A. Caracciolo;F. Ghisio;M. Piroddi;M. Pandocchi;C. Fiorini;M. Carminati;V. Pascali;N. Protti;D. Mazzucconi;L. Grisoni;D. Ramos;N. Ferrara;K. Thielemans;G. Borghi","doi":"10.1109/TRPMS.2025.3562079","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3562079","url":null,"abstract":"Boron Neutron Capture Therapy (BNCT) is an advanced cancer treatment that combines radiation therapy with targeted drug delivery. Patients are administered a boron compound that accumulates in tumour cells and are then irradiated with thermal neutrons that induce 10B(n,<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>)7Li reactions, whose high-LET products locally deposit a high dose to tumour cells. The additional 478 keV gamma ray generated by the de-excitation of 7Li can be detected outside the patient’s body and can be used for dose localization and monitoring using the SPECT technique. In this study, we show the first experimental tomographic results obtained with a prototype BNCT-SPECT system at the LENA neutron facility in Pavia, Italy. Measurements are acquired with the BeNEdiCTE detection module, based on a 5 cm <inline-formula> <tex-math>$times $ </tex-math></inline-formula> 5 cm <inline-formula> <tex-math>$times $ </tex-math></inline-formula> 2 cm LaBr3(Ce+Sr) monolithic scintillator crystal coupled to an <inline-formula> <tex-math>$8times 8$ </tex-math></inline-formula> matrix of Near Ultraviolet High-Density silicon photomultipliers (SiPMs). The system shows good performance in detecting the incoming radiation of interest and in reconstructing 2-D planar images of boron samples irradiated with thermal neutrons. Thanks to the aid of Software for Tomographic Image Reconstruction (STIR), we show a successful 3-D reconstruction of 2 vials containing 7371 ppm of 10B placed at 1.4 cm distance, starting from four partial projections and using 10 iterations of the Maximum Likelihood Expectation Maximization (MLEM) algorithm.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 1","pages":"126-136"},"PeriodicalIF":3.5,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10969104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861234","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 : 2025-04-14DOI: 10.1109/TRPMS.2025.3560267
Chunyuan Liu;Tongyuan Huang;Yunze He;Huayu Chen;Zipeng Wu;Yihan Yang
Medical lesion segmentation plays a crucial role in computer-aided diagnosis, yet acquiring fully annotated images remains a significant challenge. Semi-supervised learning has shown great potential in scenarios with limited labeled data. However, pseudo-labels, commonly used for unlabeled data, may adversely affect model performance due to their inherent inaccuracies. To address this issue, we propose a semi-supervised lesion segmentation framework based on a contrast-guided diffusion model (CGDM). To mitigate the impact of inaccurate pseudo-labels, we exploit the contrastive relationship between lesion and healthy images, restoring lesion regions to a healthy-like appearance. By directly incorporating this contrastive semantic information during training, we alleviate the model’s over-reliance on pseudo-labels and mitigate its detrimental effects on model performance. Furthermore, we introduce a structural similarity contrast (SSC) loss function to balance supervised and unsupervised learning. This function constructs sample pairs for contrastive learning, maximizing the disparity between paired lesion and healthy images while minimizing the resemblance of lesion regions in unpaired lesion images. Experimental results on the BUSI, BraTS2018, and KiTS19 datasets demonstrate that CGDM achieves superior performance compared to state-of-the-art semi-supervised segmentation methods.
{"title":"Semi-Supervised Medical Lesion Image Segmentation Based on a Contrast-Guided Diffusion Model","authors":"Chunyuan Liu;Tongyuan Huang;Yunze He;Huayu Chen;Zipeng Wu;Yihan Yang","doi":"10.1109/TRPMS.2025.3560267","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3560267","url":null,"abstract":"Medical lesion segmentation plays a crucial role in computer-aided diagnosis, yet acquiring fully annotated images remains a significant challenge. Semi-supervised learning has shown great potential in scenarios with limited labeled data. However, pseudo-labels, commonly used for unlabeled data, may adversely affect model performance due to their inherent inaccuracies. To address this issue, we propose a semi-supervised lesion segmentation framework based on a contrast-guided diffusion model (CGDM). To mitigate the impact of inaccurate pseudo-labels, we exploit the contrastive relationship between lesion and healthy images, restoring lesion regions to a healthy-like appearance. By directly incorporating this contrastive semantic information during training, we alleviate the model’s over-reliance on pseudo-labels and mitigate its detrimental effects on model performance. Furthermore, we introduce a structural similarity contrast (SSC) loss function to balance supervised and unsupervised learning. This function constructs sample pairs for contrastive learning, maximizing the disparity between paired lesion and healthy images while minimizing the resemblance of lesion regions in unpaired lesion images. Experimental results on the BUSI, BraTS2018, and KiTS19 datasets demonstrate that CGDM achieves superior performance compared to state-of-the-art semi-supervised segmentation methods.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 8","pages":"1036-1050"},"PeriodicalIF":3.5,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145435710","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 : 2025-04-14DOI: 10.1109/TRPMS.2025.3560558
Elena Maria Zannoni;Can Yang;Ling Cai;Matthew D. Wilson;Chin-Tu Chen;Ling-Jian Meng
There is a rising interest in single-photon emission computed tomography (SPECT) imaging systems with improved energy resolution to facilitate multifunctional molecular imaging applications, such as alpha-emitter radiopharmaceutical therapy ($alpha $ -RPT). In this article, we report the design and evaluation of the Alpha-SPECT-Mini system that offers an ultrahigh energy resolution and high sensitivity for small animal studies. The Alpha-SPECT-Mini system is constructed based on small-pixel CdTe detectors that offers sub-1-keV full-width-half-maximum (FWHM) energy resolution for single pixel events and an average ~2.5-keV energy resolution at 122 keV and ~3.5 keV at 218 keV over 153 600 pixels in the system. This allows to easily identify X- and gamma-ray contributions in densely populated spectra, such as from the Ac-225 decay chain. The system uses a 96-loft-hole collimator and six stationary detection panels in a full ring geometry. Finally, the system performance is demonstrated using Tc-99m- and Ac-225-filled resolution and image quality (IQ) phantoms. We have experimentally demonstrated that the Alpha-SPECT-Mini is a high-performance imaging system capable of imaging alpha-emitters in preclinical applications.
{"title":"The Alpha-SPECT-Mini: A Small-Animal SPECT System Based on Hyperspectral Compound-Eye Gamma Cameras","authors":"Elena Maria Zannoni;Can Yang;Ling Cai;Matthew D. Wilson;Chin-Tu Chen;Ling-Jian Meng","doi":"10.1109/TRPMS.2025.3560558","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3560558","url":null,"abstract":"There is a rising interest in single-photon emission computed tomography (SPECT) imaging systems with improved energy resolution to facilitate multifunctional molecular imaging applications, such as alpha-emitter radiopharmaceutical therapy (<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>-RPT). In this article, we report the design and evaluation of the Alpha-SPECT-Mini system that offers an ultrahigh energy resolution and high sensitivity for small animal studies. The Alpha-SPECT-Mini system is constructed based on small-pixel CdTe detectors that offers sub-1-keV full-width-half-maximum (FWHM) energy resolution for single pixel events and an average ~2.5-keV energy resolution at 122 keV and ~3.5 keV at 218 keV over 153 600 pixels in the system. This allows to easily identify X- and gamma-ray contributions in densely populated spectra, such as from the Ac-225 decay chain. The system uses a 96-loft-hole collimator and six stationary detection panels in a full ring geometry. Finally, the system performance is demonstrated using Tc-99m- and Ac-225-filled resolution and image quality (IQ) phantoms. We have experimentally demonstrated that the Alpha-SPECT-Mini is a high-performance imaging system capable of imaging alpha-emitters in preclinical applications.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 8","pages":"1107-1117"},"PeriodicalIF":3.5,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145435706","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 : 2025-04-09DOI: 10.1109/TRPMS.2025.3559095
Zerui Yu;Zhenlei Lyu;Peng Fan;Jing Wu;Yaqiang Liu;Tianyu Ma
In nuclear medicine imaging systems, intrinsic spatial resolution of the detector is one of the most important performance metrics. In this work, we aim to develop a high-resolution single photon emission computed tomography (SPECT) detector using pixelated Ce-doped gadolinium aluminum gallium garnet (GAGG:Ce) scintillators and silicon photomultiplier (SiPM) arrays. Special attention is paid to improving the resolving capability of edge crystals. We propose to place optical barrier (OB) slits onto the light guide that enhances the difference in light distribution for edge crystals. We experimentally optimize OB designs for two scintillator arrays, named as Array-ESR and Array-BaSO4, which uses enhanced specular reflector (ESR) film and barium sulfate (BaSO4) as the reflectors, respectively. Both arrays have $31times 31~0$ .8 mm $times 0$ .8 mm $times $ 6 mm GAGG:Ce crystals. We introduce the flood map quality (FMQ) parameter to assess the separation of responses of neighboring crystals. The results demonstrate that for Array-ESR, an optimal light guide with two 7° OB slits and two 11° OB slits resolves 92.40% crystals with an energy resolution of 13.19% $pm ~0.68$ %. The FMQ is $1.52~pm ~0.38$ . For Array-BaSO4, the optimal design is a light guide with four 7° OB slits. 98.75% crystals are resolvable with an energy resolution of 15.33% $pm ~0.96$ % and FMQ parameter of $1.81~pm ~0.45$ . Overall, Array-BaSO4 is more suitable for building SPECT detector for its good crystal resolving performance and fabrication convenience. This study proposes a practical submillimeter pixelated SPECT detector design with no detection dead space and compact electronics. It is promising for being used to build large-scale detectors for high resolution SPECT systems.
在核医学成像系统中，探测器的固有空间分辨率是最重要的性能指标之一。在这项工作中，我们的目标是使用像素化掺Ce钆铝镓石榴石（GAGG:Ce）闪烁体和硅光电倍增管（SiPM）阵列开发高分辨率单光子发射计算机断层扫描（SPECT）探测器。特别注意提高边缘晶体的分辨能力。我们建议在光导上放置光学屏障（OB）狭缝，以增强边缘晶体的光分布差异。本文通过实验优化了两种闪烁体阵列（Array-ESR和Array-BaSO4）的OB设计，这两种闪烁体阵列分别使用增强镜面反射器（ESR）薄膜和硫酸钡（BaSO4）作为反射器。两个数组都有$31乘以31~0$。8 mm $乘以0$。8毫米$乘以6毫米$ GAGG:Ce晶体。我们引入洪水图质量（FMQ）参数来评估相邻晶体的分离响应。结果表明，对于Array-ESR，具有两个7°OB狭缝和两个11°OB狭缝的最优光导可以分辨92.40%的晶体，能量分辨率为13.19% ~0.68美元%。FMQ为1.52~ 0.38美元。对于Array-BaSO4，最优设计是具有四个7°OB狭缝的光导。98.75%的晶体可分辨，能量分辨率为15.33% ~0.96美元%，FMQ参数为1.81~ 0.45美元。综上所述，阵列- baso4具有良好的晶体分辨性能和制作方便，更适合用于构建SPECT探测器。本研究提出一种实用的亚毫米像素化SPECT探测器设计，无检测死区，电子元件紧凑。它有望用于构建高分辨率SPECT系统的大规模探测器。
{"title":"Submillimeter Pixelated SPECT Detector Using GAGG:Ce and Light Guide With Optical Barrier Slits","authors":"Zerui Yu;Zhenlei Lyu;Peng Fan;Jing Wu;Yaqiang Liu;Tianyu Ma","doi":"10.1109/TRPMS.2025.3559095","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3559095","url":null,"abstract":"In nuclear medicine imaging systems, intrinsic spatial resolution of the detector is one of the most important performance metrics. In this work, we aim to develop a high-resolution single photon emission computed tomography (SPECT) detector using pixelated Ce-doped gadolinium aluminum gallium garnet (GAGG:Ce) scintillators and silicon photomultiplier (SiPM) arrays. Special attention is paid to improving the resolving capability of edge crystals. We propose to place optical barrier (OB) slits onto the light guide that enhances the difference in light distribution for edge crystals. We experimentally optimize OB designs for two scintillator arrays, named as Array-ESR and Array-BaSO4, which uses enhanced specular reflector (ESR) film and barium sulfate (BaSO4) as the reflectors, respectively. Both arrays have <inline-formula> <tex-math>$31times 31~0$ </tex-math></inline-formula>.8 mm <inline-formula> <tex-math>$times 0$ </tex-math></inline-formula>.8 mm <inline-formula> <tex-math>$times $ </tex-math></inline-formula> 6 mm GAGG:Ce crystals. We introduce the flood map quality (FMQ) parameter to assess the separation of responses of neighboring crystals. The results demonstrate that for Array-ESR, an optimal light guide with two 7° OB slits and two 11° OB slits resolves 92.40% crystals with an energy resolution of 13.19% <inline-formula> <tex-math>$pm ~0.68$ </tex-math></inline-formula>%. The FMQ is <inline-formula> <tex-math>$1.52~pm ~0.38$ </tex-math></inline-formula>. For Array-BaSO4, the optimal design is a light guide with four 7° OB slits. 98.75% crystals are resolvable with an energy resolution of 15.33% <inline-formula> <tex-math>$pm ~0.96$ </tex-math></inline-formula>% and FMQ parameter of <inline-formula> <tex-math>$1.81~pm ~0.45$ </tex-math></inline-formula>. Overall, Array-BaSO4 is more suitable for building SPECT detector for its good crystal resolving performance and fabrication convenience. This study proposes a practical submillimeter pixelated SPECT detector design with no detection dead space and compact electronics. It is promising for being used to build large-scale detectors for high resolution SPECT systems.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 8","pages":"1015-1024"},"PeriodicalIF":3.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145435702","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}
This study aims to develop a compact, low-cost, and high-performance benchtop small-animal PET/MRI scanner that achieves functional and anatomical image fusion. The system is designed to address challenges in cost reduction, spatial resolution, sensitivity, image quality (IQ), and quantitative accuracy. The PET/MRI system was developed with a parallel configuration, integrating a custom-designed PET scanner and a 0.5-T permanent magnet MRI system. Quantitative assessments included spatial resolution, sensitivity, IQ, and quantitative accuracy, as well as signal-to-noise ratio (SNR), geometric distortion (GD), and image uniformity (IU) for MRI. The spatial resolution at the axial center is 1.31 (axial), 1.26 (radial), and 1.22 mm (tangential), with a center sensitivity of 8.05% under a wide energy window. Image quality (IQ) tests using an IQ phantom demonstrated a uniformity of 10.08% standard deviation, recovery coefficients (RC) ranging from 0.23 to 0.96, and spill-over ratios (SOR) of 0.08 and 0.18 in air and water regions, respectively. The MRI system achieved an SNR of 14.16 in phantom tests, a GD of less than 1%, and IU of 90.13%. Fusion imaging of PET and MRI demonstrated high registration accuracy in both phantom and mouse studies, with complementary functional and anatomical information. The proposed PET/MRI system achieves high spatial resolution, sensitivity, IQ, and quantitative accuracy while maintaining a simple, low-cost design. The parallel configuration facilitates precise PET/MRI image fusion and allows for efficient multianimal imaging. The results highlight the potential of this system for preclinical research and its feasibility for future in-vehicle imaging applications. Further optimization of the MRI system and data transmission methods will enhance its performance in high-activity studies and broaden its application scope, with potential applications in preclinical research and in-vehicle imaging.
{"title":"Development and Performance Evaluation of a Benchtop Small-Animal PET/MRI Scanner","authors":"Xin Yu;Zhijun Zhao;Han Liu;Da Liang;Wenjing Zhu;Ying Lin;Jiayang Zeng;Chenxuan Liu;Jianfeng Xu;Siwei Xie;Weimin Wang;Qiyu Peng","doi":"10.1109/TRPMS.2025.3557789","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3557789","url":null,"abstract":"This study aims to develop a compact, low-cost, and high-performance benchtop small-animal PET/MRI scanner that achieves functional and anatomical image fusion. The system is designed to address challenges in cost reduction, spatial resolution, sensitivity, image quality (IQ), and quantitative accuracy. The PET/MRI system was developed with a parallel configuration, integrating a custom-designed PET scanner and a 0.5-T permanent magnet MRI system. Quantitative assessments included spatial resolution, sensitivity, IQ, and quantitative accuracy, as well as signal-to-noise ratio (SNR), geometric distortion (GD), and image uniformity (IU) for MRI. The spatial resolution at the axial center is 1.31 (axial), 1.26 (radial), and 1.22 mm (tangential), with a center sensitivity of 8.05% under a wide energy window. Image quality (IQ) tests using an IQ phantom demonstrated a uniformity of 10.08% standard deviation, recovery coefficients (RC) ranging from 0.23 to 0.96, and spill-over ratios (SOR) of 0.08 and 0.18 in air and water regions, respectively. The MRI system achieved an SNR of 14.16 in phantom tests, a GD of less than 1%, and IU of 90.13%. Fusion imaging of PET and MRI demonstrated high registration accuracy in both phantom and mouse studies, with complementary functional and anatomical information. The proposed PET/MRI system achieves high spatial resolution, sensitivity, IQ, and quantitative accuracy while maintaining a simple, low-cost design. The parallel configuration facilitates precise PET/MRI image fusion and allows for efficient multianimal imaging. The results highlight the potential of this system for preclinical research and its feasibility for future in-vehicle imaging applications. Further optimization of the MRI system and data transmission methods will enhance its performance in high-activity studies and broaden its application scope, with potential applications in preclinical research and in-vehicle imaging.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 8","pages":"1118-1126"},"PeriodicalIF":3.5,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145435709","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}
Proton therapy is one of the most advanced radiotherapy techniques. Despite its advantages in dose delivery, it has not yet achieved significant clinical benefits for patients due to uncertainties in proton range. Accurate, real-time monitoring of proton dose and range is crucial for ensuring the precision of proton therapy. In prior work, a dual-head prompt gamma imaging system was proposed and evaluated through Monte Carlo simulations, demonstrating high spatial resolution and sufficient detection efficiency for proton pencil beam imaging at clinical doses. This study focuses on the assembly, calibration, and testing of one of the detectors in this system. Spatial resolution and detection efficiency were evaluated using a 22Na point source, while range shift detection and accuracy were assessed with 60 and 100 MeV proton beams under low proton count conditions. The single-head system achieved a detection efficiency of 0.22% and a full-width at half-maximum (FWHM) spatial resolution of 2.8 mm at the center of the field of view (FOV). The system was able to detect a 1 mm range shift by identifying the most distal edge position (MDEP) of the prompt gamma profile. The detector demonstrated a range accuracy of less than 1 mm at typical count levels for a single spot in proton pencil beam scanning. The results suggest that this system performs well in terms of both detection efficiency and spatial resolution, and the system could achieve real-time range verification with high accuracy.
{"title":"Proton Range Verification Realized via a Multislit Prompt Gamma Imaging System","authors":"Hongyang Zhang;Bo Zhao;Peng Fan;Shi Wang;Wenzhuo Lu;Yancheng Yu;Zhaoxia Wu;Tianyu Ma;Hui Liu;Yaqiang Liu","doi":"10.1109/TRPMS.2025.3553133","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3553133","url":null,"abstract":"Proton therapy is one of the most advanced radiotherapy techniques. Despite its advantages in dose delivery, it has not yet achieved significant clinical benefits for patients due to uncertainties in proton range. Accurate, real-time monitoring of proton dose and range is crucial for ensuring the precision of proton therapy. In prior work, a dual-head prompt gamma imaging system was proposed and evaluated through Monte Carlo simulations, demonstrating high spatial resolution and sufficient detection efficiency for proton pencil beam imaging at clinical doses. This study focuses on the assembly, calibration, and testing of one of the detectors in this system. Spatial resolution and detection efficiency were evaluated using a 22Na point source, while range shift detection and accuracy were assessed with 60 and 100 MeV proton beams under low proton count conditions. The single-head system achieved a detection efficiency of 0.22% and a full-width at half-maximum (FWHM) spatial resolution of 2.8 mm at the center of the field of view (FOV). The system was able to detect a 1 mm range shift by identifying the most distal edge position (MDEP) of the prompt gamma profile. The detector demonstrated a range accuracy of less than 1 mm at typical count levels for a single spot in proton pencil beam scanning. The results suggest that this system performs well in terms of both detection efficiency and spatial resolution, and the system could achieve real-time range verification with high accuracy.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 8","pages":"1127-1134"},"PeriodicalIF":3.5,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145435694","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 : 2025-04-02DOI: 10.1109/TRPMS.2025.3552178
{"title":">Member Get-a-Member (MGM) Program","authors":"","doi":"10.1109/TRPMS.2025.3552178","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3552178","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 4","pages":"529-529"},"PeriodicalIF":4.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10947670","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761356","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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