IEEE Transactions on Radiation and Plasma Medical Sciences最新文献

{"title":"Improved 2-D Chest CT Image Enhancement With Multi-Level VGG Loss","authors":"Ayush Chaturvedi;Ritvik Prabhu;Mukund Yadav;Wu-Chun Feng;Guohua Cao","doi":"10.1109/TRPMS.2024.3439010","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3439010","url":null,"abstract":"Chest CT scans play an important role in diagnosing abnormalities associated with the lungs, such as tuberculosis, sarcoidosis, pneumonia, and, more recently, COVID-19. However, because conventional normal-dose chest CT scans require a much larger amount of radiation than x-rays, practitioners seek to replace conventional CT with low-dose CT (LDCT). LDCT often generates a low-quality CT image that poses noise and, in turn, negatively affects the accuracy of diagnosis. Therefore, in the context of COVID-19, due to the large number of affected populations, efficient image-denoising techniques are needed for LDCT images. Here, we present a deep learning (DL) model that combines two neural networks to enhance the quality of low-dose chest CT images. The DL model leverages a previously developed densenet and deconvolution-based network (DDNet) for feature extraction and extends it with a pretrained VGG network inside the loss function to suppress noise. Outputs from selected multiple levels in the VGG network (ML-VGG) are leveraged for the loss calculation. We tested our DDNet with ML-VGG loss using several sources of CT images and compared its performance to DDNet without VGG loss as well as DDNet with an empirically selected single-level VGG loss (DDNet-SL-VGG) and other state-of-the-art DL models. Our results show that DDNet combined with ML-VGG (DDNet-ML-VGG) achieves state-of-the-art denoising capabilities and improves the perceptual and quantitative image quality of chest CT images. Thus, DDNet with multilevel VGG loss could potentially be used as a post-acquisition image enhancement tool for medical professionals to diagnose and monitor chest diseases with higher accuracy.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 3","pages":"304-312"},"PeriodicalIF":4.6,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143553300","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":"HYPR4D Kernel Method With an Unsupervised 2.5SD+0.5TD Deep Learning Assisted Kernel Matrix","authors":"Ju-Chieh Kevin Cheng;Erik Reimers;Vesna Sossi","doi":"10.1109/TRPMS.2024.3442690","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3442690","url":null,"abstract":"We describe a deep learning (DL) assisted HYPR4D kernelized reconstruction which produces low-noise voxel-level time-activity-curves (TACs) while preserving quantification within small structures as well as consistent spatiotemporal patterns/features within measured data. The proposed method consists of the following advantages over other methods: 1) unsupervised single subject network training scheme independent of positron emission tomography (PET) tracers; 2) training data generated on-the-fly during reconstruction; 3) intrinsic spatiotemporal consistency provided by minimizing the \u0000<inline-formula> <tex-math>$L_{2}$ </tex-math></inline-formula>\u0000 loss using pseudo 4-D (i.e., 2.5 Spatial Dimension + 0.5 Temporal Dimension or 2.5SD+0.5TD) patches between kernelized OSEM subset estimates; and 4) a final tuning step which minimizes over-smoothing from the network output within the kernel matrix. Contrast phantom, human [18F]FDG and [11C]RAC data acquired on GE SIGNA PET/MR were used for evaluations. The proposed DL HYPR4D kernel method outperformed the standard HYPR4D kernel method as well as TOF-OSEM and TOF-BSREM (Q.Clear) in terms contrast recovery versus noise. The proposed final tuning reduced the underestimation bias due to over-smoothing within a 4-mm target structure from ~15% to ~2% while maintaining low-noise voxel-level TACs. In addition, the proposed unsupervised DL assisted reconstruction also outperformed the supervised DL version in terms of bias reduction along the TACs and kinetic model fittings.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 1","pages":"20-28"},"PeriodicalIF":4.6,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912554","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":"List-Mode PET Image Reconstruction Using Dykstra-Like Splitting","authors":"Kibo Ote;Fumio Hashimoto;Yuya Onishi;Yasuomi Ouchi","doi":"10.1109/TRPMS.2024.3441526","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3441526","url":null,"abstract":"Convergence of the block iterative method in image reconstruction for positron emission tomography (PET) requires careful control of relaxation parameters, which is a challenging task. The automatic determination of relaxation parameters for list-mode reconstructions also remains challenging. Therefore, a different approach would be desirable. In this study, we propose a list-mode maximum-likelihood Dykstra-like splitting PET reconstruction (LM-MLDS) that reduces the limit-cycle amplitude by adding the distance from an initial image as a penalty term into an objective function. LM-MLDS uses a two-step approach because its performance depends on the quality of the initial image. The first step uses a uniform image as the initial image, whereas the second step uses a reconstructed image after one main iteration as the initial image. In a simulation study, LM-MLDS provided a better tradeoff curve between noise and contrast than the other methods. In a clinical study, LM-MLDS removed the false hotspots at the edge of the axial field of view and improved the image quality of slices covering the top of the head to the cerebellum. List-mode proximal splitting reconstruction is useful not only for optimizing nondifferential functions but also for mitigating the limit-cycle phenomenon in block iterative methods.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 1","pages":"29-39"},"PeriodicalIF":4.6,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912497","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":"Improving Reconstruction Speed of Positron Emission Particle Tracking by Efficient Gradient Calculation","authors":"Eunsik Choi;Yeseul Kim;Wonmo Sung","doi":"10.1109/TRPMS.2024.3440344","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3440344","url":null,"abstract":"The development of molecular imaging algorithms and tools has advanced our understanding of the molecular dynamics in complex systems, such as tracking cells in vivo. One of these advancements, the positron emission particle tracking (PEPT) algorithm, allows particles to be tracked through a positron emission tomography (PET) scanner. The spatiotemporal B-spline reconstruction (SBSR) method of the PEPT algorithm is capable of tracking a single particle, such as a cell using PET with high accuracy. However, its slow computational speed, particularly with large data, results in time-intensive hyperparameter tuning, which is a limitation in real-world applications. This study introduces a novel approach, employing the backpropagation algorithm, commonly used in deep learning, to enhance the efficiency of gradient computation during particle trajectory reconstruction. Comparisons of the computational speed of the previous and current algorithms on a PEPT benchmark dataset show that the novel approach significantly increased the computational speed without compromising the tracking accuracy. Notably, we found that the difference in computation time between the current and previous algorithms increased as the size of the data increased. In conclusion, we have improved the SBSR method by efficiently computing the gradients, making it faster and more efficient. Even with bigger data, our approach keeps up, showing an improvement in computational speed.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"9 1","pages":"40-46"},"PeriodicalIF":4.6,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912506","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 Learning-Based Fast Volumetric Image Generation for Image-Guided Proton Radiotherapy","authors":"Chih-Wei Chang;Yang Lei;Tonghe Wang;Sibo Tian;Justin Roper;Liyong Lin;Jeffrey Bradley;Tian Liu;Jun Zhou;Xiaofeng Yang","doi":"10.1109/TRPMS.2024.3439585","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3439585","url":null,"abstract":"Very fast imaging techniques can enhance the precision of image-guided radiation therapy, which can be useful for external beam radiation therapy. This work aims to develop a deep learning (DL)-based image-guide framework to enable fast volumetric image reconstruction for accurate target localization for treating lung cancer patients with gating, and it is presented in the context of FLASH which leverages ultrahigh dose-rate radiation to enhance the sparing of organs at risk without compromising tumor control probability. The proposed framework comprises four modules, including orthogonal kV X-ray projection acquisition, DL-based volumetric image generation, image quality analyses, and proton water equivalent thickness (WET) evaluation. We investigated volumetric image reconstruction using kV projection pairs with four different source angles. Thirty patients with lung targets were identified from an institutional database, each patient having a 4-D computed tomography (CT) dataset with ten respiratory phases. Considering all evaluation metrics, the kV projections with source angles of 135° and 225° yielded the optimal volumetric images. The patient-averaged mean absolute error, peak signal-to-noise ratio, structural similarity index measure, and WET error were \u0000<inline-formula> <tex-math>$75pm 22$ </tex-math></inline-formula>\u0000 hounsfield unit, \u0000<inline-formula> <tex-math>$19pm 3$ </tex-math></inline-formula>\u0000.7 dB, \u0000<inline-formula> <tex-math>$0.938pm 0.044$ </tex-math></inline-formula>\u0000, and −1.3%±4.1%. The proposed framework can rapidly deliver volumetric images to potentially guide proton FLASH treatment delivery systems.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"973-983"},"PeriodicalIF":4.6,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587625","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":"Experimental Measurement of Secondary Particle Count for Real-Time Proton Range Verification","authors":"Chuan Huang;Zhengguo Hu;Wei Lv;Yucong Chen;Xiuling Zhang;Zhiguo Xu;Faming Luo;Xinle Lang;Zulong Zhao;Ruishi Mao;Yongzhi Yin;Zhongming Wang;Di Wang;Guoqing Xiao","doi":"10.1109/TRPMS.2024.3439517","DOIUrl":"https://doi.org/10.1109/TRPMS.2024.3439517","url":null,"abstract":"The real-time positioning of the particle beam range during treatment is a critical technology for improving the quality of the patient treatment. This article presents a scheme for the real-time proton range verification, and an experimental prototype is built at the Xi’an proton application facility (XiPAF) terminal. The experiment utilized a 150 MeV passive proton beam delivery mode to bombard the polymethyl methacrylate (PMMA) target for the real-time proton range verification. This scheme utilizes the secondary particle counts generated per monitor unit (MU) of primary particles and does not require identification of the secondary particle species, only its deposition energy in the cerium bromide (CeBr3) scintillator module exceeding 73.24 keV. The accuracy of range verification was evaluated at various acquisition periods by establishing the relationship between the secondary particle counts generated per MU of primary particles and the proton range. The range verification accuracy after one spill (\u0000<inline-formula> <tex-math>$sim ~1.67times 10$ </tex-math></inline-formula>\u00009 protons) delivery was measured at \u0000<inline-formula> <tex-math>$0.01~pm ~0$ </tex-math></inline-formula>\u0000.29 mm. The accuracy of range verification within milliseconds is mainly affected by the statistical fluctuations in the secondary particle counts caused by the accumulation of activation products. Under constrained conditions, the range verification accuracy was measured at \u0000<inline-formula> <tex-math>$0.16~pm ~0$ </tex-math></inline-formula>\u0000.69 mm within 110 ms acquisition time and \u0000<inline-formula> <tex-math>$0.16~pm ~0$ </tex-math></inline-formula>\u0000.94 mm within 55 ms acquisition time. The experimental results confirm the feasibility of the scheme for the real-time range verification practice. The study hopes to provide a new reference scheme for reducing the impact of range uncertainty on the patient treatment quality.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":"8 8","pages":"984-989"},"PeriodicalIF":4.6,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142587539","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":"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}