IEEE Transactions on Radiation and Plasma Medical Sciences最新文献

{"title":"Imaging and Sensing of pH and Chemical State With Cascade Photons of ¹¹¹In Using Ring-Type Compton Camera","authors":"Donghwan Kim;Mizuki Uenomachi;Kenji Shimazoe;Hiroyuki Takahashi;Kei Kamada;Hideki Tomita","doi":"10.1109/TRPMS.2024.3437354","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3437354","url":null,"abstract":"Molecular imaging techniques, such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT), provide the activity distribution of radioisotopes. 111In is a radioisotope commonly used in SPECT, which consecutively emits two cascade gamma rays: 171 keV, followed by 245 keV. The anisotropy of the emission directions of cascade gamma rays can provide more information in addition to activity distribution. It contains information regarding the chemical state of the molecule surrounding the 111In atom. A previous mechanical collimator SPECT study indicated the possibility of imaging and obtaining information on the local chemical state through anisotropy measurements. This study utilized a ring-structured Compton camera for anisotropy measurement of 111In cascade gamma rays with potential use in imaging PET and SPECT tracers. Two point sources with different pH values (1.6 and 13.9) were measured separately and simultaneously. The efficiency of events was in the order of \u0000<inline-formula> <tex-math>$mathbf {1}mathbf {0}^{mathbf {- 6}}$ </tex-math></inline-formula>\u0000, which was two orders higher than the collimated detector system used in the previous study. The spatial resolution on the image at the center of the field of view was obtained as \u0000<inline-formula> <tex-math>$13.7mathbf {pm }0$ </tex-math></inline-formula>\u0000.16 mm, which is worse than collimated SPECT in previous studies. In the experiment, the measured anisotropy coefficients (ACs) of pH 1.6 and 13.9 were \u0000<inline-formula> <tex-math>$- 0.18mathbf {pm }0.05$ </tex-math></inline-formula>\u0000 and \u0000<inline-formula> <tex-math>$- 0.01mathbf {pm }0.05$ </tex-math></inline-formula>\u0000, respectively, when measured separately. Furthermore, those of pH 1.6 and 13.9 were \u0000<inline-formula> <tex-math>$- 0.16mathbf {pm }0.03$ </tex-math></inline-formula>\u0000 and \u0000<inline-formula> <tex-math>$0.01mathbf {pm }0.04$ </tex-math></inline-formula>\u0000, respectively, when measured simultaneously. The anisotropy measurements conducted at source positions derived from reconstructed images exhibited differences of more than two standard errors at different pHs. This proof of concept demonstrates the feasibility of AC measurement and discusses the limitations and the areas for improvement for further work.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 1","pages":"107-117"},"PeriodicalIF":4.6,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912396","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}

{"title":"130+ ps Coincident Time Resolution With 20-mm Crystal Length Using 28-nm Xilinx FPGA","authors":"Fei Wang;Jiawen Zhou;Ziyi Weng;Chao Cai;Qingguo Xie","doi":"10.1109/TRPMS.2024.3437178","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3437178","url":null,"abstract":"The coincidence time resolution (CTR) is of paramount importance in positron emission tomography (PET) as it can directly determine the imaging resolution. In this article, a 130+ ps CTR with 20-mm crystal length is achieved using AMD FPGA platform. Three steps are proposed to achieve a high CTR. First, a low-noise amplifier (LNA) is used on fast output signals that are used for time sampling. This can equivalently lower the configured threshold for leading edge discriminator and therefore further mitigate the time walk effect. Second, a new time-to-digital converter (TDC) architecture that achieves less than 1-LSB integral nonlinearity (INL) and differential nonlinearity (DNL) without any calibration tricks are introduced. This TDC can yield salient INL performance, which can deliver consistent performance in time sampling and hence better CTR. Last but not least, a resource-efficient energy characterization method is proposed. This approach utilizes only one TDC chain to sample all trigger times for pulse reconstruction. This not only saves up to 75% chain resources but also minimizes sampling errors due to heterogeneity properties when involving multiple TDC chains. A prototype using 28-nm Kintex 7 FPGA is implemented and 130+ ps CTR is achieved.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 1","pages":"1-10"},"PeriodicalIF":4.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912507","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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}