Pub Date : 2025-03-22DOI: 10.1109/TRPMS.2025.3572863
P. Shali;N. Caz;J. Van den Bosch;R. Ghobeira;S. Aliakbarshirazi;M. Narimisa;R. Morent;E. Wolfs;N. De Geyter
Cancer remains a leading cause of mortality, emphasizing the need for innovative therapies. Plasma-treated liquids, containing reactive oxygen and nitrogen species, have demonstrated therapeutic potential. This study investigates the physicochemical properties and anti-cancer efficacy of phosphate-buffered saline (PBS) treated using a novel liquid-submerged plasma jet, which enhances interactions between plasma species and the liquid for a more uniform treatment. Operational parameters, including voltage, gas flow, and treatment time, were optimized concurrently. Notably, the submerged configuration produced significantly higher H2O2 concentrations in PBS (up to $2000~mu $ M) compared to the above-liquid plasma set-ups reported in literature. However, ${NO}{2}^{-}$ concentrations remained low (6–$18~mu $ M). Voltage variations influenced H2O2 production but had a minimal effect on ${NO}{2}^{-}$ , while gas flow rates did not impact their concentrations. PBS maintained a stable pH, demonstrating its effective buffering capacity. Stability tests showed H2O2 remained stable at $21~^{circ }$ C, slightly increased at $4~^{circ }$ C, and decreased at $37~^{circ }$ C; nitrites were stable below $21~^{circ }$ C but slightly decreased at $37~^{circ }$ C. Plasma-treated PBS selectively reduced oral squamous cell carcinoma (OSCC) cell viability while sparing healthy keratinocytes (HaCaT), with H2O2 identified as the primary anti-cancer agent. These findings suggest that PBS plasma-treated using a new liquid-submerged set-up shows potential as selective OSCC therapy.
{"title":"Investigation of the Physicochemical Properties and Selective Anti-Cancer Efficacy of In-Plasma Treated PBS Using an Exclusive Liquid-Submerged Plasma Jet","authors":"P. Shali;N. Caz;J. Van den Bosch;R. Ghobeira;S. Aliakbarshirazi;M. Narimisa;R. Morent;E. Wolfs;N. De Geyter","doi":"10.1109/TRPMS.2025.3572863","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3572863","url":null,"abstract":"Cancer remains a leading cause of mortality, emphasizing the need for innovative therapies. Plasma-treated liquids, containing reactive oxygen and nitrogen species, have demonstrated therapeutic potential. This study investigates the physicochemical properties and anti-cancer efficacy of phosphate-buffered saline (PBS) treated using a novel liquid-submerged plasma jet, which enhances interactions between plasma species and the liquid for a more uniform treatment. Operational parameters, including voltage, gas flow, and treatment time, were optimized concurrently. Notably, the submerged configuration produced significantly higher H2O2 concentrations in PBS (up to <inline-formula> <tex-math>$2000~mu $ </tex-math></inline-formula>M) compared to the above-liquid plasma set-ups reported in literature. However, <inline-formula> <tex-math>${NO}{2}^{-}$ </tex-math></inline-formula> concentrations remained low (6–<inline-formula> <tex-math>$18~mu $ </tex-math></inline-formula>M). Voltage variations influenced H2O2 production but had a minimal effect on <inline-formula> <tex-math>${NO}{2}^{-}$ </tex-math></inline-formula>, while gas flow rates did not impact their concentrations. PBS maintained a stable pH, demonstrating its effective buffering capacity. Stability tests showed H2O2 remained stable at <inline-formula> <tex-math>$21~^{circ }$ </tex-math></inline-formula>C, slightly increased at <inline-formula> <tex-math>$4~^{circ }$ </tex-math></inline-formula>C, and decreased at <inline-formula> <tex-math>$37~^{circ }$ </tex-math></inline-formula>C; nitrites were stable below <inline-formula> <tex-math>$21~^{circ }$ </tex-math></inline-formula>C but slightly decreased at <inline-formula> <tex-math>$37~^{circ }$ </tex-math></inline-formula>C. Plasma-treated PBS selectively reduced oral squamous cell carcinoma (OSCC) cell viability while sparing healthy keratinocytes (HaCaT), with H2O2 identified as the primary anti-cancer agent. These findings suggest that PBS plasma-treated using a new liquid-submerged set-up shows potential as selective OSCC therapy.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"10 2","pages":"317-332"},"PeriodicalIF":3.5,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116905","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 : 2025-03-20DOI: 10.1109/TRPMS.2025.3553409
M. Mehdi Khalighi;Christina B. Young;Matthew G. Spangler-Bickell;Timothy W. Deller;Floris Jansen;Dawn Holley;Hillary Vossler;Moss Y. Zhao;Feliks Kogan;Gary Steinberg;Elizabeth Mormino;Michael Moseley;Greg Zaharchuk
The current spatial resolution of positron emission tomography (PET) images is 3–4 mm for whole body PET/MR. Anatomical MR images with higher resolution and superior image quality have been used in PET reconstruction to improve the image quality and spatial resolution; however, mismatches between MR priors and actual tracer distribution can hinder accuracy. A novel PET reconstruction with MR priors, magnetic resonance-guided block sequential regularized expectation maximum (MRgBSREM), that is robust to mismatches between anatomical priors and true activity distribution is proposed. This method is evaluated in diverse clinical settings using various tracers: 18F-florbetaben (FBB) in 373 subjects from a dementia study, 18F-FDG in a patient with chronic ischemic stroke, 18F-NaF in a knee study, and 15O-water in a patient with Moyamoya disease. Reconstruction using MRgBSREM visually improved both spatial resolution and image quality in all studies. In the 18FBB study, it mitigated white-matter spill-in into gray-matter as well as gray-matter spill over to the adjacent tissues, potentially leading to more accurate measurement of FBB uptake in the gray-matter. Visual assessment suggests that the proposed PET reconstruction enhances spatial resolution, which may contribute to improved diagnostic accuracy, while it displays robustness to mismatches between MR priors and true activity distribution.
{"title":"A Novel Method in PET Image Reconstruction Using MRI Anatomical Priors","authors":"M. Mehdi Khalighi;Christina B. Young;Matthew G. Spangler-Bickell;Timothy W. Deller;Floris Jansen;Dawn Holley;Hillary Vossler;Moss Y. Zhao;Feliks Kogan;Gary Steinberg;Elizabeth Mormino;Michael Moseley;Greg Zaharchuk","doi":"10.1109/TRPMS.2025.3553409","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3553409","url":null,"abstract":"The current spatial resolution of positron emission tomography (PET) images is 3–4 mm for whole body PET/MR. Anatomical MR images with higher resolution and superior image quality have been used in PET reconstruction to improve the image quality and spatial resolution; however, mismatches between MR priors and actual tracer distribution can hinder accuracy. A novel PET reconstruction with MR priors, magnetic resonance-guided block sequential regularized expectation maximum (MRgBSREM), that is robust to mismatches between anatomical priors and true activity distribution is proposed. This method is evaluated in diverse clinical settings using various tracers: 18F-florbetaben (FBB) in 373 subjects from a dementia study, 18F-FDG in a patient with chronic ischemic stroke, 18F-NaF in a knee study, and 15O-water in a patient with Moyamoya disease. Reconstruction using MRgBSREM visually improved both spatial resolution and image quality in all studies. In the 18FBB study, it mitigated white-matter spill-in into gray-matter as well as gray-matter spill over to the adjacent tissues, potentially leading to more accurate measurement of FBB uptake in the gray-matter. Visual assessment suggests that the proposed PET reconstruction enhances spatial resolution, which may contribute to improved diagnostic accuracy, while it displays robustness to mismatches between MR priors and true activity distribution.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 8","pages":"1074-1082"},"PeriodicalIF":3.5,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145435700","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}
Although atmospheric pressure plasma (APP) treatment has exhibited promising antitumor efficacy across various cancer types, no studies have analyzed the effects of APP on pituitary adenoma (PA). In this study, APP generation and treatment conditions were optimized and investigated. Four pituitary tumor cell lines (GH3, AtT-20, GT1-1, and MMQ) were used to assess the inhibitory effect of APP treatment and were compared with two glioblastoma (GBM) cell lines (U87MG and LN229) and a neuronal cell line (SH-SY5Y). Results showed that the APP treatment has a better inhibitory effect on pituitary tumor cells with minimal neurotoxicity. The best inhibitory effect was observed in GH3, which had an IC50 value of only 32.33 s. APP treatment elevated both intra- and extra-cellular reactive oxygen/nitrogen species (ROS/RNS) in GH3 cells, which induced significantly GH3 cell apoptosis. Noninvasive micro-test technology (NMT) experiment revealed substantial ${mathrm { Ca}}^{2+}$ influx following APP treatment in GH3 cells. Moreover, validation on primary pituitary tumor cells from patients corroborated these findings. Overall, our results highlight that APP treatment exerts substantial antitumor effects on PA cells compared to GBM cell lines, suggesting its potential as a complementary therapy in clinical neurosurgical treatment of PA.
{"title":"Inhibitory Effect of Atmospheric Pressure Plasma on GH3 Pituitary Adenoma Cell Line and Primary Pituitary Tumor Cells From Patients","authors":"Qiuyue Fang;Yixiao Liu;Yanan Xing;Xi Zhang;Yuqing Liu;Yuxuan Liu;Zhiyan Sun;Yuqi Guo;Yulou Liu;Gaosheng He;Lixin Xu;Xiaojin Xu;Jiting Ouyang;Chuzhong Li;Xu Yan;Zilan Xiong","doi":"10.1109/TRPMS.2025.3552789","DOIUrl":"https://doi.org/10.1109/TRPMS.2025.3552789","url":null,"abstract":"Although atmospheric pressure plasma (APP) treatment has exhibited promising antitumor efficacy across various cancer types, no studies have analyzed the effects of APP on pituitary adenoma (PA). In this study, APP generation and treatment conditions were optimized and investigated. Four pituitary tumor cell lines (GH3, AtT-20, GT1-1, and MMQ) were used to assess the inhibitory effect of APP treatment and were compared with two glioblastoma (GBM) cell lines (U87MG and LN229) and a neuronal cell line (SH-SY5Y). Results showed that the APP treatment has a better inhibitory effect on pituitary tumor cells with minimal neurotoxicity. The best inhibitory effect was observed in GH3, which had an IC50 value of only 32.33 s. APP treatment elevated both intra- and extra-cellular reactive oxygen/nitrogen species (ROS/RNS) in GH3 cells, which induced significantly GH3 cell apoptosis. Noninvasive micro-test technology (NMT) experiment revealed substantial <inline-formula> <tex-math>${mathrm { Ca}}^{2+}$ </tex-math></inline-formula> influx following APP treatment in GH3 cells. Moreover, validation on primary pituitary tumor cells from patients corroborated these findings. Overall, our results highlight that APP treatment exerts substantial antitumor effects on PA cells compared to GBM cell lines, suggesting its potential as a complementary therapy in clinical neurosurgical treatment of PA.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 8","pages":"1135-1146"},"PeriodicalIF":3.5,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145435707","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 : 2025-03-20DOI: 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}
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