Pub Date : 2024-08-01DOI: 10.1109/TRPMS.2024.3436697
Jessica B. Hopson;Anthime Flaus;Colm J. McGinnity;Radhouene Neji;Andrew J. Reader;Alexander Hammers
Pretraining deep convolutional network mappings using natural images helps with medical imaging analysis tasks; this is important given the limited number of clinically annotated medical images. Many 2-D pretrained backbone networks, however, are currently available. This work compared 18 different backbones from 5 architecture groups (pretrained on ImageNet) for the task of assessing [18F]FDG brain positron emission tomography (PET) image quality (reconstructed at seven simulated doses), based on three clinical image quality metrics (global quality rating, pattern recognition, and diagnostic confidence). Using 2-D randomly sampled patches, up to eight patients (at three dose levels each) were used for training, with three separate patient datasets used for testing. Each backbone was trained five times with the same training and validation sets, and with six cross-folds. Training only the final fully connected layer (with ~6000–20000 trainable parameters) achieved a test mean-absolute-error (MAE) of ~0.5 (which was within the intrinsic uncertainty of clinical scoring). To compare “classical” and over-parameterized regimes, the pretrained weights of the last 40% of the network layers were then unfrozen. The MAE fell below 0.5 for 14 out of the 18 backbones assessed, including two that previously failed to train. Generally, backbones with residual units (e.g., DenseNets and ResNetV2s), were suited to this task, in terms of achieving the lowest MAE at test time (~0.45–0.5). This proof-of-concept study shows that over-parameterization may also be important for automated PET image quality assessments.
{"title":"Deep Convolutional Backbone Comparison for Automated PET Image Quality Assessment","authors":"Jessica B. Hopson;Anthime Flaus;Colm J. McGinnity;Radhouene Neji;Andrew J. Reader;Alexander Hammers","doi":"10.1109/TRPMS.2024.3436697","DOIUrl":"10.1109/TRPMS.2024.3436697","url":null,"abstract":"Pretraining deep convolutional network mappings using natural images helps with medical imaging analysis tasks; this is important given the limited number of clinically annotated medical images. Many 2-D pretrained backbone networks, however, are currently available. This work compared 18 different backbones from 5 architecture groups (pretrained on ImageNet) for the task of assessing [18F]FDG brain positron emission tomography (PET) image quality (reconstructed at seven simulated doses), based on three clinical image quality metrics (global quality rating, pattern recognition, and diagnostic confidence). Using 2-D randomly sampled patches, up to eight patients (at three dose levels each) were used for training, with three separate patient datasets used for testing. Each backbone was trained five times with the same training and validation sets, and with six cross-folds. Training only the final fully connected layer (with ~6000–20000 trainable parameters) achieved a test mean-absolute-error (MAE) of ~0.5 (which was within the intrinsic uncertainty of clinical scoring). To compare “classical” and over-parameterized regimes, the pretrained weights of the last 40% of the network layers were then unfrozen. The MAE fell below 0.5 for 14 out of the 18 backbones assessed, including two that previously failed to train. Generally, backbones with residual units (e.g., DenseNets and ResNetV2s), were suited to this task, in terms of achieving the lowest MAE at test time (~0.45–0.5). This proof-of-concept study shows that over-parameterization may also be important for automated PET image quality assessments.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"893-901"},"PeriodicalIF":4.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477477","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}
Over the past decades, the development of computed tomography (CT) technologies has been largely driven by the need for cardiac imaging but the temporal resolution remains insufficient for clinical CT in difficult cases and rather challenging for preclinical CT since small animals have much higher heart rates than humans. To address this challenge, here we report a semi-stationary multisource artificial intelligence (AI)-based real-time tomography (SMART) CT system. This unique scanner is featured by 29 source-detector pairs fixed on a circular track to collect X-ray signals in parallel, enabling instantaneous tomography in principle. Given the multisource architecture, the field of view covers only a cardiac region. To solve the interior problem, an AI-empowered interior tomography approach is developed to synergize sparsity-based regularization and learning-based reconstruction. To demonstrate the performance and utilities of the SMART system, extensive results are obtained in physical phantom experiments and animal studies, including dead and live rats as well as live rabbits. The reconstructed volumetric images convincingly demonstrate the merits of the SMART system using the AI-empowered interior tomography approach, enabling cardiac CT with the unprecedented temporal resolution of 33 ms, which enjoys the highest temporal resolution than the state of the art.
{"title":"Semi-Stationary Multisource AI-Powered Real-Time Tomography","authors":"Weiwen Wu;Yaohui Tang;Tianling Lv;Wenxiang Cong;Chuang Niu;Cheng Wang;Yiyan Guo;Peiqian Chen;Yunheng Chang;Ge Wang;Yan Xi","doi":"10.1109/TRPMS.2024.3433575","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3433575","url":null,"abstract":"Over the past decades, the development of computed tomography (CT) technologies has been largely driven by the need for cardiac imaging but the temporal resolution remains insufficient for clinical CT in difficult cases and rather challenging for preclinical CT since small animals have much higher heart rates than humans. To address this challenge, here we report a semi-stationary multisource artificial intelligence (AI)-based real-time tomography (SMART) CT system. This unique scanner is featured by 29 source-detector pairs fixed on a circular track to collect X-ray signals in parallel, enabling instantaneous tomography in principle. Given the multisource architecture, the field of view covers only a cardiac region. To solve the interior problem, an AI-empowered interior tomography approach is developed to synergize sparsity-based regularization and learning-based reconstruction. To demonstrate the performance and utilities of the SMART system, extensive results are obtained in physical phantom experiments and animal studies, including dead and live rats as well as live rabbits. The reconstructed volumetric images convincingly demonstrate the merits of the SMART system using the AI-empowered interior tomography approach, enabling cardiac CT with the unprecedented temporal resolution of 33 ms, which enjoys the highest temporal resolution than the state of the art.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 1","pages":"118-130"},"PeriodicalIF":4.6,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912394","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 : 2024-07-23DOI: 10.1109/TRPMS.2024.3432194
Celia Valladares;John Barrio;Neus Cucarella;Marta Freire;Luis F. Vidal;José M. Benlloch;Antonio J. González
Positron emission tomography (PET) stands out as a highly specific molecular imaging technique. However, its detection sensitivity remains a challenge. The implementation of time-of-flight (TOF) PET technology enhances sensitivity by precisely measuring the time lapse between the annihilation photons. Moreover, by characterizing scattered (Compton) events, the effective sensitivity of PET imaging might significantly be enhanced. In this work, we present the scatter subsystem of a 2 layers preclinical TOF-PET scanner for mice head imaging. The scatter subsystem is composed of eight identical modules based on analog silicon photomultipliers (SiPMs) coupled to crystal arrays of $24times 24$ LYSO pixels with 0.95 mm $times 0$ .95 mm $times $ 3 mm dimensions. The system has 29-mm bore and 50.8-mm axial length. An average CTR of $192~pm ~1$ ps was obtained for the whole subsystem at the photopeak energy range after energy and timing corrections, and CTR values as good as 155 ps were found for some individual pixels. The transit time spread at the SiPM level was also studied and corrected, achieving a mean value of 41 ps of maximum time difference at the sensor corners with respect to the center. Voronoi diagrams were implemented to correct for position decoding.
{"title":"Detector Characterization of a High-Resolution Ring for PET Imaging of Mice Heads With Sub-200-ps TOF","authors":"Celia Valladares;John Barrio;Neus Cucarella;Marta Freire;Luis F. Vidal;José M. Benlloch;Antonio J. González","doi":"10.1109/TRPMS.2024.3432194","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3432194","url":null,"abstract":"Positron emission tomography (PET) stands out as a highly specific molecular imaging technique. However, its detection sensitivity remains a challenge. The implementation of time-of-flight (TOF) PET technology enhances sensitivity by precisely measuring the time lapse between the annihilation photons. Moreover, by characterizing scattered (Compton) events, the effective sensitivity of PET imaging might significantly be enhanced. In this work, we present the scatter subsystem of a 2 layers preclinical TOF-PET scanner for mice head imaging. The scatter subsystem is composed of eight identical modules based on analog silicon photomultipliers (SiPMs) coupled to crystal arrays of \u0000<inline-formula> <tex-math>$24times 24$ </tex-math></inline-formula>\u0000 LYSO pixels with 0.95 mm \u0000<inline-formula> <tex-math>$times 0$ </tex-math></inline-formula>\u0000.95 mm \u0000<inline-formula> <tex-math>$times $ </tex-math></inline-formula>\u0000 3 mm dimensions. The system has 29-mm bore and 50.8-mm axial length. An average CTR of \u0000<inline-formula> <tex-math>$192~pm ~1$ </tex-math></inline-formula>\u0000 ps was obtained for the whole subsystem at the photopeak energy range after energy and timing corrections, and CTR values as good as 155 ps were found for some individual pixels. The transit time spread at the SiPM level was also studied and corrected, achieving a mean value of 41 ps of maximum time difference at the sensor corners with respect to the center. Voronoi diagrams were implemented to correct for position decoding.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"876-885"},"PeriodicalIF":4.6,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10606942","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587631","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 : 2024-07-23DOI: 10.1109/TRPMS.2024.3430298
Hyoung Suk Park;Young Jin Jeong;Kiwan Jeon
This study presents a deep-learning-based restoration method for low-quality amyloid positron emission tomography (PET) images acquired in a short period, which can be generalized across multiple domains. Each of these domains consists of low-quality amyloid PET images acquired in the same environment. Owing to variations in image characteristics, such as contrast, across different acquisition environments, the restoration performance of the deep-learning methods can significantly degrade when applied to PET images obtained from unseen domains (i.e., not seen in training). To address the difficulty, we introduce a mapping label and condition the network on this label. This enables the network that takes a low-quality amyloid PET image and the corresponding mapping label as inputs to effectively generate the desired high-quality amyloid PET image. We assign the mapping label as a one-hot vector for each domain and use pairs of PET images from short (2 min) and standard (20 min) scanning times for training. The network, trained with the mapping label, can efficiently restore low-quality amyloid PET images in unseen domains by estimating an unknown mapping label for the unseen domain. We demonstrate the effectiveness of the proposed method through quantitative and qualitative analyses on the several datasets.
{"title":"A Robust Multidomain Network for Short-Scanning Amyloid PET Image Restoration","authors":"Hyoung Suk Park;Young Jin Jeong;Kiwan Jeon","doi":"10.1109/TRPMS.2024.3430298","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3430298","url":null,"abstract":"This study presents a deep-learning-based restoration method for low-quality amyloid positron emission tomography (PET) images acquired in a short period, which can be generalized across multiple domains. Each of these domains consists of low-quality amyloid PET images acquired in the same environment. Owing to variations in image characteristics, such as contrast, across different acquisition environments, the restoration performance of the deep-learning methods can significantly degrade when applied to PET images obtained from unseen domains (i.e., not seen in training). To address the difficulty, we introduce a mapping label and condition the network on this label. This enables the network that takes a low-quality amyloid PET image and the corresponding mapping label as inputs to effectively generate the desired high-quality amyloid PET image. We assign the mapping label as a one-hot vector for each domain and use pairs of PET images from short (2 min) and standard (20 min) scanning times for training. The network, trained with the mapping label, can efficiently restore low-quality amyloid PET images in unseen domains by estimating an unknown mapping label for the unseen domain. We demonstrate the effectiveness of the proposed method through quantitative and qualitative analyses on the several datasets.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 1","pages":"57-68"},"PeriodicalIF":4.6,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912502","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 : 2024-07-22DOI: 10.1109/TRPMS.2024.3432322
Amal Tiss;Yanis Chemli;Nicolas Guehl;Thibault Marin;Keith Johnson;Georges El Fakhri;Jinsong Ouyang
Motion is unavoidable in dynamic [18F]-MK6240 positron emission tomography (PET) imaging, especially in Alzheimer’s disease (AD) research requiring long scan duration. To understand how motion correction affects quantitative analysis, we investigated two approaches: intra- and inter- frame motion correction (II-MC), which corrects for both the interframe and intraframe motion, and interframe only motion correction (IO-MC), which only corrects for the interframe motion. These methods were applied to 83 scans from 34 subjects, and we calculated distribution volume ratios (DVRs) using the multilinear reference tissue model with the two parameters (MRTM2) in tau-rich brain regions. Most of the studies yielded similar DVR results for both II-MC and IO-MC. However, in one scan of an AD subject, the inferior temporal region showed 14% higher DVR with II-MC compared to IO-MC. This difference was reasonable given the AD diagnosis, although similar results were not observed in other regions. Although discrepancies between IO-MC and II-MC results were rare, they underscore the importance of incorporating intraframe motion correction for more accurate and dependable PET quantitation, particularly in the context of dynamic imaging. These findings suggest that while the overall impact of intraframe motion correction may be subtle, it can improve the reliability of longitudinal PET data, ultimately enhancing our understanding of tau protein distribution in AD pathology.
在动态[18F]-MK6240正电子发射断层扫描（PET）成像中，运动是不可避免的，尤其是在需要长时间扫描的阿尔茨海默病（AD）研究中。为了了解运动校正对定量分析的影响，我们研究了两种方法：帧内和帧间运动校正（II-MC）和仅帧间运动校正（IO-MC），前者可校正帧间和帧内运动，后者仅校正帧间运动。我们将这些方法应用于 34 名受试者的 83 次扫描，并使用带两个参数的多线性参考组织模型（MRTM2）计算了富含 tau 的脑区的分布容积比（DVRs）。大多数研究得出的 II-MC 和 IO-MC 的分布容积比结果相似。不过，在对一名注意力缺失症患者的扫描中，与 IO-MC 相比，II-MC 下颞区的 DVR 高出 14%。虽然在其他区域没有观察到类似的结果，但考虑到注意力缺失症的诊断，这种差异是合理的。虽然 IO-MC 和 II-MC 结果之间的差异很少见，但它们强调了结合帧内运动校正以实现更准确可靠的 PET 定量的重要性，尤其是在动态成像的情况下。这些研究结果表明，虽然帧内运动校正的总体影响可能是微妙的，但它可以提高纵向 PET 数据的可靠性，最终增强我们对 tau 蛋白在 AD 病理学中分布情况的了解。
{"title":"Effects of List-Mode-Based Intraframe Motion Correction in Dynamic Brain PET Imaging","authors":"Amal Tiss;Yanis Chemli;Nicolas Guehl;Thibault Marin;Keith Johnson;Georges El Fakhri;Jinsong Ouyang","doi":"10.1109/TRPMS.2024.3432322","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3432322","url":null,"abstract":"Motion is unavoidable in dynamic [18F]-MK6240 positron emission tomography (PET) imaging, especially in Alzheimer’s disease (AD) research requiring long scan duration. To understand how motion correction affects quantitative analysis, we investigated two approaches: intra- and inter- frame motion correction (II-MC), which corrects for both the interframe and intraframe motion, and interframe only motion correction (IO-MC), which only corrects for the interframe motion. These methods were applied to 83 scans from 34 subjects, and we calculated distribution volume ratios (DVRs) using the multilinear reference tissue model with the two parameters (MRTM2) in tau-rich brain regions. Most of the studies yielded similar DVR results for both II-MC and IO-MC. However, in one scan of an AD subject, the inferior temporal region showed 14% higher DVR with II-MC compared to IO-MC. This difference was reasonable given the AD diagnosis, although similar results were not observed in other regions. Although discrepancies between IO-MC and II-MC results were rare, they underscore the importance of incorporating intraframe motion correction for more accurate and dependable PET quantitation, particularly in the context of dynamic imaging. These findings suggest that while the overall impact of intraframe motion correction may be subtle, it can improve the reliability of longitudinal PET data, ultimately enhancing our understanding of tau protein distribution in AD pathology.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"950-958"},"PeriodicalIF":4.6,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587634","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 : 2024-07-18DOI: 10.1109/TRPMS.2024.3430827
Ryuto Miura;Mitsuhiro Nakamura;Megumi Nakao
2-D/3-D image registration is a problem that solves the deformation and alignment of a pretreatment 3-D volume to a 2-D projection image, which is available for treatment support and biomedical analysis. 2-D/3-D image registration for abdominal organs is a complicated task because the abdominal organs deform significantly and their contours are not detected in 2-D X-ray images. In this study, we propose a supervised deep learning framework that achieves 2-D/3-D deformable image registration between the 3-D volume and a single-viewpoint 2-D projection image. The proposed method uses latent image features of the 2-D projection images to learn a transformation from the input image, which is a concatenation of the 2-D projection images and the 3-D volume, to a dense displacement vector field (DVF) that represents nonlinear and local organ displacements. The target DVFs are generated by registration between 3-D volumes, and the registration error with the estimated DVF is introduced as a loss function during training. We register 3D-computed tomography (CT) volumes to the digitally reconstructed radiographs generated from abdominal 4D-CT volumes of 35 cases. The experimental results show that the proposed method can reconstruct 3D-CT with a mean voxel-to-voxel error of 29.4 Hounsfield unit and a dice similarity coefficient of 89.2 % on average for the body, liver, stomach, duodenum, and kidney regions, which is a clinically acceptable accuracy. In addition, the average computation time for the registration process by the proposed framework is 0.181 s, demonstrating real-time registration performance.
{"title":"Image-to-Volume Deformable Registration by Learning Displacement Vector Fields","authors":"Ryuto Miura;Mitsuhiro Nakamura;Megumi Nakao","doi":"10.1109/TRPMS.2024.3430827","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3430827","url":null,"abstract":"2-D/3-D image registration is a problem that solves the deformation and alignment of a pretreatment 3-D volume to a 2-D projection image, which is available for treatment support and biomedical analysis. 2-D/3-D image registration for abdominal organs is a complicated task because the abdominal organs deform significantly and their contours are not detected in 2-D X-ray images. In this study, we propose a supervised deep learning framework that achieves 2-D/3-D deformable image registration between the 3-D volume and a single-viewpoint 2-D projection image. The proposed method uses latent image features of the 2-D projection images to learn a transformation from the input image, which is a concatenation of the 2-D projection images and the 3-D volume, to a dense displacement vector field (DVF) that represents nonlinear and local organ displacements. The target DVFs are generated by registration between 3-D volumes, and the registration error with the estimated DVF is introduced as a loss function during training. We register 3D-computed tomography (CT) volumes to the digitally reconstructed radiographs generated from abdominal 4D-CT volumes of 35 cases. The experimental results show that the proposed method can reconstruct 3D-CT with a mean voxel-to-voxel error of 29.4 Hounsfield unit and a dice similarity coefficient of 89.2 % on average for the body, liver, stomach, duodenum, and kidney regions, which is a clinically acceptable accuracy. In addition, the average computation time for the registration process by the proposed framework is 0.181 s, demonstrating real-time registration performance.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 1","pages":"69-82"},"PeriodicalIF":4.6,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912508","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 : 2024-07-10DOI: 10.1109/TRPMS.2024.3422149
Sahar M. Gebril;Fakhr El-din M. Lashein;Mohamed Khalaf;Eslam El-Sabry AbuAmra;F. M. El-Hossary
Diabetic hyperglycemia is a metabolic scenario that disturbs immunity and promotes inflammatory reactions. On the other hand, many biomedical applications benefit from cold atmospheric plasma (CAP). In this study, the effect of CAP treatment on a diabetic mice model was evaluated by examining splenic immune cells and inflammatory parameters that modulate diabetes-induced immune dysfunction. Twenty-four adult male BALB/c mice (25–30 g) were randomly divided into four groups: 1) negative control; 2) control treated by CAP; 3) streptozotocin (STZ)-injected diabetics (60 mg/kg animal weight); and 4) STZ-injected diabetics treated with direct CAP for 10 s daily for two months. Fasting blood glucose levels, antioxidant enzymes (catalase and glutathione reductase), spleen tissue histopathology, and Immunohistochemistry (active caspase 3, proliferating cell nuclear antigen, cluster of differentiation 68 for macrophages (CD68), and tumor necrosis factor $alpha $ ) were examined. Diabetic mice treated with CAP had improved spleen histological morphology, and significantly increased in antioxidant enzymes, white pulp diameter, lymphocyte density, and immune cell proliferation. Moreover, Mallory-stained collagen fibrosis, TNF $alpha $ , CD68 positive macrophages and caspase 3 activated immune cells were significantly decreased. The antioxidant effect of RONS, produced by CAP, reduces hyperglycemia, reconstitutes splenic immune cells, and regulates inflammatory cells, Cytokines, and programmed cell death.
{"title":"Effect of Cold Atmospheric Plasma on Hyperglycemia and Immunity in the Spleen of STZ Diabetic Mice","authors":"Sahar M. Gebril;Fakhr El-din M. Lashein;Mohamed Khalaf;Eslam El-Sabry AbuAmra;F. M. El-Hossary","doi":"10.1109/TRPMS.2024.3422149","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3422149","url":null,"abstract":"Diabetic hyperglycemia is a metabolic scenario that disturbs immunity and promotes inflammatory reactions. On the other hand, many biomedical applications benefit from cold atmospheric plasma (CAP). In this study, the effect of CAP treatment on a diabetic mice model was evaluated by examining splenic immune cells and inflammatory parameters that modulate diabetes-induced immune dysfunction. Twenty-four adult male BALB/c mice (25–30 g) were randomly divided into four groups: 1) negative control; 2) control treated by CAP; 3) streptozotocin (STZ)-injected diabetics (60 mg/kg animal weight); and 4) STZ-injected diabetics treated with direct CAP for 10 s daily for two months. Fasting blood glucose levels, antioxidant enzymes (catalase and glutathione reductase), spleen tissue histopathology, and Immunohistochemistry (active caspase 3, proliferating cell nuclear antigen, cluster of differentiation 68 for macrophages (CD68), and tumor necrosis factor \u0000<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>\u0000) were examined. Diabetic mice treated with CAP had improved spleen histological morphology, and significantly increased in antioxidant enzymes, white pulp diameter, lymphocyte density, and immune cell proliferation. Moreover, Mallory-stained collagen fibrosis, TNF\u0000<inline-formula> <tex-math>$alpha $ </tex-math></inline-formula>\u0000, CD68 positive macrophages and caspase 3 activated immune cells were significantly decreased. The antioxidant effect of RONS, produced by CAP, reduces hyperglycemia, reconstitutes splenic immune cells, and regulates inflammatory cells, Cytokines, and programmed cell death.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 1","pages":"131-140"},"PeriodicalIF":4.6,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912510","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 : 2024-07-08DOI: 10.1109/TRPMS.2024.3424941
Huamin Wang;Shuo Yang;Xiao Bai;Zhe Wang;Jiayi Wu;Yang Lv;Guohua Cao
The metal artifacts in computed tomography (CT) images not only affect diagnosis and treatment but also present a classic nonlinear inverse problem in CT reconstruction. In this study, we propose an iterative relation-based dual-domain network (IRDNet) that utilizes metal artifact feature guidance to reduce such artifacts in CT images. To the best of our knowledge, IRDNet leverages metal artifact features as guidance of the dual-domain network for the first time to reduce metal artifacts. Our framework incorporates artifact-corrupted and precorrected images (linear-interpolated images) as well as metal artifact features to effectively reduce metal artifacts for a high-quality prior CT image and corresponding prior sinogram. The prior image and prior sinogram are then iteratively recovered sinogram using the residual learning strategy and mitigate the artifacts of CT image with a metal-location guidance framework. We construct IRDNet in an unrolling manner to accurately optimize anatomical structures. Compared to the state-of-the-art algorithms, IRDNet consistently produces reasonable CT images with reduced metal artifacts, as evaluated both quantitatively and qualitatively across different-sized metal implant samples and different metal materials. It generalized different artifacts caused by metals of various sizes and materials and successfully recovered surrounding tissues. The experimental results demonstrate the potential of incorporating metal inherent features as priors in the dual-domain network for reducing metal artifacts.
{"title":"IRDNet: Iterative Relation-Based Dual-Domain Network via Metal Artifact Feature Guidance for CT Metal Artifact Reduction","authors":"Huamin Wang;Shuo Yang;Xiao Bai;Zhe Wang;Jiayi Wu;Yang Lv;Guohua Cao","doi":"10.1109/TRPMS.2024.3424941","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3424941","url":null,"abstract":"The metal artifacts in computed tomography (CT) images not only affect diagnosis and treatment but also present a classic nonlinear inverse problem in CT reconstruction. In this study, we propose an iterative relation-based dual-domain network (IRDNet) that utilizes metal artifact feature guidance to reduce such artifacts in CT images. To the best of our knowledge, IRDNet leverages metal artifact features as guidance of the dual-domain network for the first time to reduce metal artifacts. Our framework incorporates artifact-corrupted and precorrected images (linear-interpolated images) as well as metal artifact features to effectively reduce metal artifacts for a high-quality prior CT image and corresponding prior sinogram. The prior image and prior sinogram are then iteratively recovered sinogram using the residual learning strategy and mitigate the artifacts of CT image with a metal-location guidance framework. We construct IRDNet in an unrolling manner to accurately optimize anatomical structures. Compared to the state-of-the-art algorithms, IRDNet consistently produces reasonable CT images with reduced metal artifacts, as evaluated both quantitatively and qualitatively across different-sized metal implant samples and different metal materials. It generalized different artifacts caused by metals of various sizes and materials and successfully recovered surrounding tissues. The experimental results demonstrate the potential of incorporating metal inherent features as priors in the dual-domain network for reducing metal artifacts.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"959-972"},"PeriodicalIF":4.6,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10589441","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587517","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 : 2024-07-08DOI: 10.1109/TRPMS.2024.3421601
Eva Becker;Christina B. Walter;Juliane Scheid;Sara Y. Brucker;André Koch;Martin Weiss
Endometrial cancer (EC) is the most common tumor of the female reproductive organs in industrialized nations and an increasingly frequent disease in premenopausal women, which necessitates the development of fertility-preserving alternative treatment modalities without radical hysterectomy. In this study, we progressively elucidate the cancer specific the impact of plasma activated media (PAM) on EC, transitioning from conventional single cell models to more clinically relevant patient-derived 3-D organoid systems of different tumor gradings compared to healthy endometrial tissue, emphasizing a novel experimental approach. Significantly, we demonstrate an increasing impact of PAM on patient-derived high-grade EC organoids accompanied with a dose-dependent rise in oxidative stress levels, contrasting with no alterations in healthy endometrial tissue. These findings collectively suggest that the application of plasma-activated liquid holds promise for expanding fertility-preserving therapies for endometrial carcinoma and contributing to future disease control. In conclusion, this research pioneers a patient-specific and stepwise investigation into the therapeutic potential of PAM on EC and contributes to the evolving landscape of personalized cancer therapies, offering promising avenues for future clinical applications.
{"title":"Exploring Cell Type-Specific Efficacy of Plasma-Activated Medium (PAM) on Endometrial Cancer Using Patient-Specific 2-D and 3-D cell Culture Systems (2024)","authors":"Eva Becker;Christina B. Walter;Juliane Scheid;Sara Y. Brucker;André Koch;Martin Weiss","doi":"10.1109/TRPMS.2024.3421601","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3421601","url":null,"abstract":"Endometrial cancer (EC) is the most common tumor of the female reproductive organs in industrialized nations and an increasingly frequent disease in premenopausal women, which necessitates the development of fertility-preserving alternative treatment modalities without radical hysterectomy. In this study, we progressively elucidate the cancer specific the impact of plasma activated media (PAM) on EC, transitioning from conventional single cell models to more clinically relevant patient-derived 3-D organoid systems of different tumor gradings compared to healthy endometrial tissue, emphasizing a novel experimental approach. Significantly, we demonstrate an increasing impact of PAM on patient-derived high-grade EC organoids accompanied with a dose-dependent rise in oxidative stress levels, contrasting with no alterations in healthy endometrial tissue. These findings collectively suggest that the application of plasma-activated liquid holds promise for expanding fertility-preserving therapies for endometrial carcinoma and contributing to future disease control. In conclusion, this research pioneers a patient-specific and stepwise investigation into the therapeutic potential of PAM on EC and contributes to the evolving landscape of personalized cancer therapies, offering promising avenues for future clinical applications.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 2","pages":"259-268"},"PeriodicalIF":4.6,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10589343","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106265","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 : 2024-07-03DOI: 10.1109/TRPMS.2024.3405098
{"title":"IEEE Transactions on Radiation and Plasma Medical Sciences Information for Authors","authors":"","doi":"10.1109/TRPMS.2024.3405098","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3405098","url":null,"abstract":"","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 6","pages":"C3-C3"},"PeriodicalIF":4.6,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10584412","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141500379","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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