Pub Date : 2025-03-20DOI: 10.1109/TRPMS.2025.3553436
Jorge Cabello;Mohammadreza Teimoorisichani;James J. Hamill;Stefan B. Siegel
Single scatter simulation (SSS) is the most commonly used approach to mitigate the effects of scattered photons in PET imaging. However, the introduction of long axial field of view (LAFOV) scanners has required this method to be revisited or alternative methods to be adopted. In this work we compared a version of SSS with tail fitting (SSS-TF) devised for LAFOV, an alternative version of SSS with a statistical method to scale the scatter sinogram (SSS-MLSS), and a simplified fast GPU-based Monte-Carlo method (MC-GPU). These methods were evaluated using the MC toolkit GATE, simulating geometrical and realistic patient-based voxelized phantoms, employing the scatter simulated by GATE as reference. Furthermore, the three scatter correction methods were compared on scanned phantoms and patients. Regarding image artefacts, results showed that SSS-TF and SSS-MLSS performed similarly in general, but SSS-MLSS outperformed SSS-TF in challenging scenarios. MC-GPU consistently outperformed SSS-TF and SSS-MLSS regarding image artefacts, but at slightly longer computation times. Quantitatively, all methods showed relative differences $lt =5$ % compared to the reference, except for those regions with artefacts, but none of them showed consistently overall superior performance among them. Experimental measurements confirmed that SSS-MLSS outperforms SSS-TF in challenging cases, showing similar performance compared to MC-GPU.
{"title":"Comparison of 3-D Scatter Correction Methods for a Long Axial Field of View PET Scanner","authors":"Jorge Cabello;Mohammadreza Teimoorisichani;James J. Hamill;Stefan B. Siegel","doi":"10.1109/TRPMS.2025.3553436","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3553436","url":null,"abstract":"Single scatter simulation (SSS) is the most commonly used approach to mitigate the effects of scattered photons in PET imaging. However, the introduction of long axial field of view (LAFOV) scanners has required this method to be revisited or alternative methods to be adopted. In this work we compared a version of SSS with tail fitting (SSS-TF) devised for LAFOV, an alternative version of SSS with a statistical method to scale the scatter sinogram (SSS-MLSS), and a simplified fast GPU-based Monte-Carlo method (MC-GPU). These methods were evaluated using the MC toolkit GATE, simulating geometrical and realistic patient-based voxelized phantoms, employing the scatter simulated by GATE as reference. Furthermore, the three scatter correction methods were compared on scanned phantoms and patients. Regarding image artefacts, results showed that SSS-TF and SSS-MLSS performed similarly in general, but SSS-MLSS outperformed SSS-TF in challenging scenarios. MC-GPU consistently outperformed SSS-TF and SSS-MLSS regarding image artefacts, but at slightly longer computation times. Quantitatively, all methods showed relative differences <inline-formula> <tex-math>$lt =5$ </tex-math></inline-formula>% compared to the reference, except for those regions with artefacts, but none of them showed consistently overall superior performance among them. Experimental measurements confirmed that SSS-MLSS outperforms SSS-TF in challenging cases, showing similar performance compared to MC-GPU.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 8","pages":"1083-1093"},"PeriodicalIF":3.5,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145435697","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}
In CT/PET imaging applications, reconstructing images from low-dose/low-count acquisitions often leads to lower image quality, necessitating specialized denoising methods and reconstruction algorithms to enhance diagnostic accuracy. While many recent denoising techniques employ convolutional neural networks (CNNs), these architectures may struggle with capturing long-range, nonlocal interactions, potentially resulting in inaccuracies in global structure representation. Recognizing the advantages of transformer architectures over CNNs on that front, our study introduces a novel sinogram denoising algorithm tailored at improving low-dose/low-count sinogram quality. We propose a transformer-based sinogram denoiser module specifically designed to match the structure of sinogram data, enhancing sinogram feature extraction and denoising performance. Furthermore, by incorporating image domain denoising, we propose cross-domain image reconstruction, allowing for further image quality refinement by addressing image-specific noise characteristics. Our cross-domain image reconstruction network, which incorporates the proposed sinogram denoiser module, has been trained with both synthetic and clinical data. Performance evaluations reveal that our sinogram sinusoidal-structure transformer Denoiser achieves outstanding results in sinogram denoising, while our cross-domain image reconstruction network demonstrates excellent image reconstruction capabilities, as validated by both subjective and objective metrics.
{"title":"Cross-Domain Reconstruction Network Incorporating Sinogram Sinusoidal-Structure Transformer Denoiser and UNet for Low-Dose/Low-Count Sinograms","authors":"Hamidreza Rashidy Kanan;Anton Adelöw;Massimiliano Colarieti-Tosti","doi":"10.1109/TRPMS.2025.3571281","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3571281","url":null,"abstract":"In CT/PET imaging applications, reconstructing images from low-dose/low-count acquisitions often leads to lower image quality, necessitating specialized denoising methods and reconstruction algorithms to enhance diagnostic accuracy. While many recent denoising techniques employ convolutional neural networks (CNNs), these architectures may struggle with capturing long-range, nonlocal interactions, potentially resulting in inaccuracies in global structure representation. Recognizing the advantages of transformer architectures over CNNs on that front, our study introduces a novel sinogram denoising algorithm tailored at improving low-dose/low-count sinogram quality. We propose a transformer-based sinogram denoiser module specifically designed to match the structure of sinogram data, enhancing sinogram feature extraction and denoising performance. Furthermore, by incorporating image domain denoising, we propose cross-domain image reconstruction, allowing for further image quality refinement by addressing image-specific noise characteristics. Our cross-domain image reconstruction network, which incorporates the proposed sinogram denoiser module, has been trained with both synthetic and clinical data. Performance evaluations reveal that our sinogram sinusoidal-structure transformer Denoiser achieves outstanding results in sinogram denoising, while our cross-domain image reconstruction network demonstrates excellent image reconstruction capabilities, as validated by both subjective and objective metrics.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 1","pages":"74-87"},"PeriodicalIF":3.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11006893","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145860188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-19DOI: 10.1109/TRPMS.2025.3571308
S. Ranjbar;F. Mas Milian;D. Bersani;P. Cerello;R. Cirio;E. M. Data;M. Donetti;M. Fadavi Mazinani;V. Ferrero;S. Giordanengo;M. Hosseini;D. Montalvan Olivares;M. Pullia;M. Rafecas;R. Sacchi;A. Vignati;J. Werner;F. Pennazio;E. Fiorina
The superconducting ion gantry (SIG) project aims to develop a reliable in vivo range verification system (RVS) for integration into a multi-ion gantry. The project includes researching, designing, and testing the system’s fundamental components. Based on RVS element performance, the ultimate goal is to design a full system that meets clinical requirements. Therefore, in this study, we present the performance evaluation of a small in-beam positron emission tomography (PET) prototype for carbon ion irradiations. The experimental setup consists of six-PET modules arranged in hexagonal geometry (3 versus 3 partial ring configuration), with a radius of 98 mm. Each detector block features $16times 16$ pixels, 3.2 mm pitch of segmented lutetium fine silicate (LFS) scintillator crystals, coupled one-to-one to silicon photomultiplier (SiPM) matrices. Homogeneous phantoms were irradiated with two monoenergetic beams at different energies at CNAO (Italian National Center of Oncological Hadron Therapy). Data were acquired online during the irradiation. For this study, images are reconstructed from the irradiation in the pauses between beam spills (interspill). The performance analysis was focused on evaluating the stability of range difference estimation considering different subsets of coincidence events along the beam irradiation.
{"title":"Performance Analysis of In-Beam PET Range Verification System for Carbon Ion Beams","authors":"S. Ranjbar;F. Mas Milian;D. Bersani;P. Cerello;R. Cirio;E. M. Data;M. Donetti;M. Fadavi Mazinani;V. Ferrero;S. Giordanengo;M. Hosseini;D. Montalvan Olivares;M. Pullia;M. Rafecas;R. Sacchi;A. Vignati;J. Werner;F. Pennazio;E. Fiorina","doi":"10.1109/TRPMS.2025.3571308","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3571308","url":null,"abstract":"The superconducting ion gantry (SIG) project aims to develop a reliable in vivo range verification system (RVS) for integration into a multi-ion gantry. The project includes researching, designing, and testing the system’s fundamental components. Based on RVS element performance, the ultimate goal is to design a full system that meets clinical requirements. Therefore, in this study, we present the performance evaluation of a small in-beam positron emission tomography (PET) prototype for carbon ion irradiations. The experimental setup consists of six-PET modules arranged in hexagonal geometry (3 versus 3 partial ring configuration), with a radius of 98 mm. Each detector block features <inline-formula> <tex-math>$16times 16$ </tex-math></inline-formula> pixels, 3.2 mm pitch of segmented lutetium fine silicate (LFS) scintillator crystals, coupled one-to-one to silicon photomultiplier (SiPM) matrices. Homogeneous phantoms were irradiated with two monoenergetic beams at different energies at CNAO (Italian National Center of Oncological Hadron Therapy). Data were acquired online during the irradiation. For this study, images are reconstructed from the irradiation in the pauses between beam spills (interspill). The performance analysis was focused on evaluating the stability of range difference estimation considering different subsets of coincidence events along the beam irradiation.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 1","pages":"137-143"},"PeriodicalIF":3.5,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145861221","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}
Multiorgan segmentation in total-body positron emission tomography (PET) images is crucial for accurately locating abnormalities and assisting in the observation of corresponding metabolic regions in the human body. Despite the emergence of numerous advanced methods in the field of multiorgan segmentation in recent years, available PET image segmentation techniques remain relatively limited. The complexity and variability of textures in PET images, the varying visibility and contrast of organs due to different metabolic activities, and the challenges posed by blurred organ boundaries in PET images all contribute to the increased difficulty of multiorgan segmentation. In this article, we propose the dual-prompt enhanced multiorgan segmentation model (DPESeg) for total-body PET image segmentation. Our approach focuses on enhancing the model’s ability to perceive organ thresholds and shapes by introducing textual and disentangled organ features, thereby improving segmentation accuracy. We validate our model on a dataset of total-body PET images obtained from 110 patients. Both visual and quantitative results demonstrate that DPESeg performs well in the multiorgan segmentation task, with a 2.02% improvement in the Dice coefficient and a 1.90% improvement in the Jaccard index compared to the best-performing comparison algorithm.
{"title":"Dual-Prompt-Enhanced Multiorgan Segmentation Model for Total-Body PET Images","authors":"Yunlong Gao;Zhenxing Huang;Yaping Wu;Wenbo Li;Meiyuan Wen;Wenjie Zhao;Qianyi Yang;Chuanli Cheng;Xinlan Yang;Yongfeng Yang;Hairong Zheng;Dong Liang;Meiyun Wang;Zhanli Hu","doi":"10.1109/TRPMS.2025.3551755","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3551755","url":null,"abstract":"Multiorgan segmentation in total-body positron emission tomography (PET) images is crucial for accurately locating abnormalities and assisting in the observation of corresponding metabolic regions in the human body. Despite the emergence of numerous advanced methods in the field of multiorgan segmentation in recent years, available PET image segmentation techniques remain relatively limited. The complexity and variability of textures in PET images, the varying visibility and contrast of organs due to different metabolic activities, and the challenges posed by blurred organ boundaries in PET images all contribute to the increased difficulty of multiorgan segmentation. In this article, we propose the dual-prompt enhanced multiorgan segmentation model (DPESeg) for total-body PET image segmentation. Our approach focuses on enhancing the model’s ability to perceive organ thresholds and shapes by introducing textual and disentangled organ features, thereby improving segmentation accuracy. We validate our model on a dataset of total-body PET images obtained from 110 patients. Both visual and quantitative results demonstrate that DPESeg performs well in the multiorgan segmentation task, with a 2.02% improvement in the Dice coefficient and a 1.90% improvement in the Jaccard index compared to the best-performing comparison algorithm.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 8","pages":"1062-1073"},"PeriodicalIF":3.5,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145435698","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}
In cone-beam X-ray transmission imaging, perspective distortion (PD) causes difficulty in direct, accurate geometric assessments of anatomical structures. Since PD correction from a single view is highly ill-posed due to missing stereo/3-D information, the efficacy of different view combinations is investigated in this work. Our theoretical analysis reveals that the 0°&180° complementary view setting provides a practical way to identify perspectively deformed structures by assessing the deviation between the two views. In addition, it provides bounding information and reduces uncertainty for learning PD. Beyond view combinations, the impact of learning PD in different spatial domains, specifically Cartesian and polar coordinates, is explored. Two representative networks Pix2pixGAN and TransU-Net for correcting PD are investigated. Experiments on numerical bead phantom data and head CT data demonstrate the advantage of complementary views over other view combinations (a 0° single view, 0°&90° orthogonal views, and 0°&5° small angular views). Results further show that both Pix2pixGAN and TransU-Net achieve better performance in polar space than Cartesian space. The efficacy of the proposed framework on real cone-beam computed tomography (CBCT) projection data and its potential to handle bulky metal implants and surgical screws indicate the promising aspects of future real applications.
{"title":"Learning Perspective Distortion Correction in Cone-Beam X-Ray Transmission Imaging","authors":"Yixing Huang;Andreas Maier;Fuxin Fan;Björn Kreher;Xiaolin Huang;Rainer Fietkau;Hongbin Han;Florian Putz;Christoph Bert","doi":"10.1109/TRPMS.2025.3551501","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3551501","url":null,"abstract":"In cone-beam X-ray transmission imaging, perspective distortion (PD) causes difficulty in direct, accurate geometric assessments of anatomical structures. Since PD correction from a single view is highly ill-posed due to missing stereo/3-D information, the efficacy of different view combinations is investigated in this work. Our theoretical analysis reveals that the 0°&180° complementary view setting provides a practical way to identify perspectively deformed structures by assessing the deviation between the two views. In addition, it provides bounding information and reduces uncertainty for learning PD. Beyond view combinations, the impact of learning PD in different spatial domains, specifically Cartesian and polar coordinates, is explored. Two representative networks Pix2pixGAN and TransU-Net for correcting PD are investigated. Experiments on numerical bead phantom data and head CT data demonstrate the advantage of complementary views over other view combinations (a 0° single view, 0°&90° orthogonal views, and 0°&5° small angular views). Results further show that both Pix2pixGAN and TransU-Net achieve better performance in polar space than Cartesian space. The efficacy of the proposed framework on real cone-beam computed tomography (CBCT) projection data and its potential to handle bulky metal implants and surgical screws indicate the promising aspects of future real applications.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 7","pages":"927-938"},"PeriodicalIF":3.5,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-17DOI: 10.1109/TRPMS.2025.3551946
Yongyi Shi;Chuang Niu;Amber L. Simpson;Bruno De Man;Richard Do;Ge Wang
CT is a main modality for imaging liver diseases, valuable in detecting and localizing liver tumors. Traditional anomaly detection methods analyze reconstructed images to identify pathological structures. However, these methods may produce suboptimal results, overlooking subtle differences among various tissue types. To address this challenge, here we employ generative AI to inpaint the liver as the reference facilitating anomaly detection. Specifically, we use an adaptive threshold to extract a mask of abnormal regions, which are then inpainted using a diffusion prior to calculating an anomaly score based on the discrepancy between the original CT image and the inpainted counterpart. Our methodology has been tested on two liver CT datasets, demonstrating a significant improvement in detection accuracy, with a 7.9% boost in the area under the curve (AUC) compared to the state-of-the-art. This performance gain underscores the potential of our approach to refine the radiological assessment of liver diseases.
{"title":"Generative Inpainting-Based Anomaly Detection for CT Liver Tumor Detection","authors":"Yongyi Shi;Chuang Niu;Amber L. Simpson;Bruno De Man;Richard Do;Ge Wang","doi":"10.1109/TRPMS.2025.3551946","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3551946","url":null,"abstract":"CT is a main modality for imaging liver diseases, valuable in detecting and localizing liver tumors. Traditional anomaly detection methods analyze reconstructed images to identify pathological structures. However, these methods may produce suboptimal results, overlooking subtle differences among various tissue types. To address this challenge, here we employ generative AI to inpaint the liver as the reference facilitating anomaly detection. Specifically, we use an adaptive threshold to extract a mask of abnormal regions, which are then inpainted using a diffusion prior to calculating an anomaly score based on the discrepancy between the original CT image and the inpainted counterpart. Our methodology has been tested on two liver CT datasets, demonstrating a significant improvement in detection accuracy, with a 7.9% boost in the area under the curve (AUC) compared to the state-of-the-art. This performance gain underscores the potential of our approach to refine the radiological assessment of liver diseases.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 8","pages":"1051-1061"},"PeriodicalIF":3.5,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145435708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-15DOI: 10.1109/TRPMS.2025.3570314
Jiadan Song;Shaozhou Pu;Liang Li
X-ray transmission computed tomography (XCT) is a method to image the structure of objects. X-ray fluorescence computed tomography (XFCT) is a way for quantitative imaging of high-Z element concentrations. The dual-modality imaging system integrating XCT and XFCT has been designed and extensively studied. In the existing dual-modality imaging system, the detector of XFCT not only collects fluorescence photons but also scattering photons, and the scattering photons are always regarded as noise signals. But in fact, the scattering photons contain electron density information, which is complementary to XCT. In this article, we design a new three-mode imaging system for transmission, fluorescence, and scattering tomography, which consists of a conventional polychromatic X-ray tube, a pinhole collimator, a photon-counting detector with high energy resolution for scattering and fluorescence imaging, and an energy-integrating detector with high spatial resolution for transmission imaging. Through this system, linear attenuation coefficient, high-Z element concentration, and electron density could be reconstructed simultaneously. We also propose a new algorithm to simultaneously realize the virtual monoenergetic imaging at different energies and the accurate attenuation correction of XFCT without extra prior information. The system’s feasibility and the algorithm’s accuracy are verified through both numerical simulations and experiments.
{"title":"Full-Field Imaging System of X-Ray Transmission, Scattering, and Fluorescence Tomography With Polychromatic Source","authors":"Jiadan Song;Shaozhou Pu;Liang Li","doi":"10.1109/TRPMS.2025.3570314","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3570314","url":null,"abstract":"X-ray transmission computed tomography (XCT) is a method to image the structure of objects. X-ray fluorescence computed tomography (XFCT) is a way for quantitative imaging of high-Z element concentrations. The dual-modality imaging system integrating XCT and XFCT has been designed and extensively studied. In the existing dual-modality imaging system, the detector of XFCT not only collects fluorescence photons but also scattering photons, and the scattering photons are always regarded as noise signals. But in fact, the scattering photons contain electron density information, which is complementary to XCT. In this article, we design a new three-mode imaging system for transmission, fluorescence, and scattering tomography, which consists of a conventional polychromatic X-ray tube, a pinhole collimator, a photon-counting detector with high energy resolution for scattering and fluorescence imaging, and an energy-integrating detector with high spatial resolution for transmission imaging. Through this system, linear attenuation coefficient, high-Z element concentration, and electron density could be reconstructed simultaneously. We also propose a new algorithm to simultaneously realize the virtual monoenergetic imaging at different energies and the accurate attenuation correction of XFCT without extra prior information. The system’s feasibility and the algorithm’s accuracy are verified through both numerical simulations and experiments.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 1","pages":"99-111"},"PeriodicalIF":3.5,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145859867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-14DOI: 10.1109/TRPMS.2025.3546120
Fabiana M. Ribeiro;Pedro M. C. C. Encarnação;Ana L. M. Silva;Pedro M. M. Correia;Afonso X. Pinto;Ismael F. Castro;Ana C. Santos;João F. C. A. Veloso
EasyPET.3D is a preclinical positron emission tomography (PET) scanner using a unique scanning method based on two face-to-face detector modules with two axes of motion. The sensitivity and spatial resolution were optimized for mouse imaging by studying the operating parameters related to motor motion (speed and step angle), following the NEMA NU 4-2008 Standards. Moreover, the impact of the energy window and positron range on the images was assessed. The fan motor should operate at a speed of 20 full steps/s, while the fan (${F}=0.014^{circ }$ –0.113°) and axial (${A}=0.9^{circ }$ –9.0°) step angles are chosen depending on the study’s purpose. The image quality experiment demonstrated the high-resolution capability of easyPET.3D. A 200–750 keV energy window maximized the sensitivity (+200%) without significantly increasing scatter fraction (SF) (+35%). In contrast, the acquisition protocol made it difficult to conclude about the positron range effect. The feature with the most impact on the scanner’s performance is the fan motor speed. A lower fan motor speed of 20 steps/s enhanced sensitivity and spatial resolution by +122% and +60%, respectively, increased noise equivalent count rate by 155%, decreased SF by 7%, and improved recovery coefficient by +35%.
EasyPET。3D是一种临床前正电子发射断层扫描（PET）扫描仪，采用独特的扫描方法，基于两个具有两个运动轴的面对面检测器模块。根据NEMA NU 4-2008标准，通过研究与运动相关的操作参数（速度和步进角），优化小鼠成像的灵敏度和空间分辨率。此外，还评估了能量窗和正电子范围对图像的影响。风机电机应以20整步/秒的速度运行，风机（${F}=0.014^{circ}$ -0.113°）和轴向（${a}=0.9^{circ}$ -9.0°）步进角根据研究目的选择。图像质量实验验证了easyPET.3D的高分辨率能力。200-750 keV的能量窗使灵敏度达到最大值（+200%），而散射分数（SF）没有显著增加（+35%）。相比之下，获取协议使得正电子距离效应难以得出结论。对扫描仪性能影响最大的特性是风扇电机的转速。当风扇电机转速为20步/秒时，灵敏度和空间分辨率分别提高+122%和+60%，噪声等效计数率提高155%，SF降低7%，恢复系数提高+35%。
{"title":"Sensitivity and Spatial Resolution Optimization of a High-Resolution Preclinical PET With a Unique Acquisition Method","authors":"Fabiana M. Ribeiro;Pedro M. C. C. Encarnação;Ana L. M. Silva;Pedro M. M. Correia;Afonso X. Pinto;Ismael F. Castro;Ana C. Santos;João F. C. A. Veloso","doi":"10.1109/TRPMS.2025.3546120","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3546120","url":null,"abstract":"EasyPET.3D is a preclinical positron emission tomography (PET) scanner using a unique scanning method based on two face-to-face detector modules with two axes of motion. The sensitivity and spatial resolution were optimized for mouse imaging by studying the operating parameters related to motor motion (speed and step angle), following the NEMA NU 4-2008 Standards. Moreover, the impact of the energy window and positron range on the images was assessed. The fan motor should operate at a speed of 20 full steps/s, while the fan (<inline-formula> <tex-math>${F}=0.014^{circ }$ </tex-math></inline-formula>–0.113°) and axial (<inline-formula> <tex-math>${A}=0.9^{circ }$ </tex-math></inline-formula>–9.0°) step angles are chosen depending on the study’s purpose. The image quality experiment demonstrated the high-resolution capability of easyPET.3D. A 200–750 keV energy window maximized the sensitivity (+200%) without significantly increasing scatter fraction (SF) (+35%). In contrast, the acquisition protocol made it difficult to conclude about the positron range effect. The feature with the most impact on the scanner’s performance is the fan motor speed. A lower fan motor speed of 20 steps/s enhanced sensitivity and spatial resolution by +122% and +60%, respectively, increased noise equivalent count rate by 155%, decreased SF by 7%, and improved recovery coefficient by +35%.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 7","pages":"959-969"},"PeriodicalIF":3.5,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-14DOI: 10.1109/TRPMS.2025.3551520
Eiji Yoshida;Fujino Obata;Taiga Yamaya
We have developed a crosshair light-sharing (CLS) detector to obtain time-of-flight and depth-of-interaction (DOI) information; the detector consists of a 2-D crystal array with three layers of reflective material, and has a loop structure within a pair of crystal bars. In this work, we modified the detector structure by removing optical glue between the crystals forming the loop structure for the purpose of simplifying the assembly process. The modified CLS was made of fast lutetium-gadolinium oxyorthosilicate (LGSO) crystals with dimensions of $1.45times 1.45times 15$ mm3 that were optically coupled to the multipixel photon counter (MPPC) array. Most optical windows of the top and bottom layers of the new Air-CLS were so-called air gaps. Only the optical windows that contribute to maintaining the 3-D structure of the reflective material were optically bonded, and a grid of reflective material was formed within the MPPC protective cover. This approach also improved the coincidence resolving time (CRT). The Air-CLSs and previous room temperature vulcanized (RTV)-CLSs were read out by TOFPET2 application-specific integrated circuits, respectively. For Air-CLS (RTV-CLS), we obtained CRT of 188 ps (197 ps), energy resolution of 14.3% (13.1%), and DOI resolution of 3.6 mm (2.9 mm). The Air-CLS significantly simplifies the assembly process while achieving the CRT of less than 190 ps.
{"title":"Air-CLS Detector: A Modified Crosshair Light-Sharing PET Detector With Air Gaps in the U-Shape Light Path","authors":"Eiji Yoshida;Fujino Obata;Taiga Yamaya","doi":"10.1109/TRPMS.2025.3551520","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3551520","url":null,"abstract":"We have developed a crosshair light-sharing (CLS) detector to obtain time-of-flight and depth-of-interaction (DOI) information; the detector consists of a 2-D crystal array with three layers of reflective material, and has a loop structure within a pair of crystal bars. In this work, we modified the detector structure by removing optical glue between the crystals forming the loop structure for the purpose of simplifying the assembly process. The modified CLS was made of fast lutetium-gadolinium oxyorthosilicate (LGSO) crystals with dimensions of <inline-formula> <tex-math>$1.45times 1.45times 15$ </tex-math></inline-formula> mm3 that were optically coupled to the multipixel photon counter (MPPC) array. Most optical windows of the top and bottom layers of the new Air-CLS were so-called air gaps. Only the optical windows that contribute to maintaining the 3-D structure of the reflective material were optically bonded, and a grid of reflective material was formed within the MPPC protective cover. This approach also improved the coincidence resolving time (CRT). The Air-CLSs and previous room temperature vulcanized (RTV)-CLSs were read out by TOFPET2 application-specific integrated circuits, respectively. For Air-CLS (RTV-CLS), we obtained CRT of 188 ps (197 ps), energy resolution of 14.3% (13.1%), and DOI resolution of 3.6 mm (2.9 mm). The Air-CLS significantly simplifies the assembly process while achieving the CRT of less than 190 ps.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 7","pages":"872-878"},"PeriodicalIF":3.5,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144996103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-13DOI: 10.1109/TRPMS.2025.3551208
Adrienne L. Lehnert;Marissa E. Kranz;Donald Q. DeWitt;David C. Argento;Robert D. Stewart;Robert S. Miyaoka
The University of Washington Medical Center has clinically implemented intensity modulated neutron therapy (IMNT) as a novel, high linear energy transfer modality for palliative and curative treatments of certain cancers. Because of the destructive nature of fast neutrons to electronics, this required development of a novel patient specific quality assurance (QA) system. Therefore, we developed an in-house 2-D positron emission tomography (PET) system that images patient-specific QA fields by measuring induced 11C positron activity in polyethylene plates. The scanner is built around two parallel imaging panels of $2times 16$ repurposed clinical PET detector modules. Images are reconstructed using focal plane tomography in a $14times 16$ cm2 field of view. Standard metrics (gamma analysis) are used to compare images with simulated (MCNP6) fluence maps. Studies demonstrated a linear dose-response relationship and full system [x, y] spatial resolution of [$5.2~pm ~0.30$ , $5.3~pm ~0.34$ ] mm2 with 1 mm-diameter point source. Final image spatial resolution is approximately 8.5 mm FWHM due to the geometry of the polyethylene plates. Energy resolution (FWHM) in the center crystals is $28~pm ~3$ %. Assembly, characterization, and quantitative calibration of the neutron Positron Emission Portal Imaging (nPEPI) system was completed in 2022, and more than 100 patients have since completed QA.
{"title":"An Imaging System to Support Fast Neutron Therapy Quality Assurance (QA) of Intensity Modulated Neutron Therapy (IMNT)","authors":"Adrienne L. Lehnert;Marissa E. Kranz;Donald Q. DeWitt;David C. Argento;Robert D. Stewart;Robert S. Miyaoka","doi":"10.1109/TRPMS.2025.3551208","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3551208","url":null,"abstract":"The University of Washington Medical Center has clinically implemented intensity modulated neutron therapy (IMNT) as a novel, high linear energy transfer modality for palliative and curative treatments of certain cancers. Because of the destructive nature of fast neutrons to electronics, this required development of a novel patient specific quality assurance (QA) system. Therefore, we developed an in-house 2-D positron emission tomography (PET) system that images patient-specific QA fields by measuring induced 11C positron activity in polyethylene plates. The scanner is built around two parallel imaging panels of <inline-formula> <tex-math>$2times 16$ </tex-math></inline-formula> repurposed clinical PET detector modules. Images are reconstructed using focal plane tomography in a <inline-formula> <tex-math>$14times 16$ </tex-math></inline-formula> cm2 field of view. Standard metrics (gamma analysis) are used to compare images with simulated (MCNP6) fluence maps. Studies demonstrated a linear dose-response relationship and full system [x, y] spatial resolution of [<inline-formula> <tex-math>$5.2~pm ~0.30$ </tex-math></inline-formula>, <inline-formula> <tex-math>$5.3~pm ~0.34$ </tex-math></inline-formula>] mm2 with 1 mm-diameter point source. Final image spatial resolution is approximately 8.5 mm FWHM due to the geometry of the polyethylene plates. Energy resolution (FWHM) in the center crystals is <inline-formula> <tex-math>$28~pm ~3$ </tex-math></inline-formula>%. Assembly, characterization, and quantitative calibration of the neutron Positron Emission Portal Imaging (nPEPI) system was completed in 2022, and more than 100 patients have since completed QA.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 7","pages":"970-977"},"PeriodicalIF":3.5,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144998036","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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