IEEE transactions on medical imaging最新文献

{"title":"An Organ-Aware Diagnosis Framework for Radiology Report Generation","authors":"Shiyu Li;Pengchong Qiao;Lin Wang;Munan Ning;Li Yuan;Yefeng Zheng;Jie Chen","doi":"10.1109/TMI.2024.3421599","DOIUrl":"10.1109/TMI.2024.3421599","url":null,"abstract":"Radiology report generation (RRG) is crucial to save the valuable time of radiologists in drafting the report, therefore increasing their work efficiency. Compared to typical methods that directly transfer image captioning technologies to RRG, our approach incorporates organ-wise priors into the report generation. Specifically, in this paper, we propose Organ-aware Diagnosis (OaD) to generate diagnostic reports containing descriptions of each physiological organ. During training, we first develop a task distillation (TD) module to extract organ-level descriptions from reports. We then introduce an organ-aware report generation module that, for one thing, provides a specific description for each organ, and for another, simulates clinical situations to provide short descriptions for normal cases. Furthermore, we design an auto-balance mask loss to ensure balanced training for normal/abnormal descriptions and various organs simultaneously. Being intuitively reasonable and practically simple, our OaD outperforms SOTA alternatives by large margins on commonly used IU-Xray and MIMIC-CXR datasets, as evidenced by a 3.4% BLEU-1 improvement on MIMIC-CXR and 2.0% BLEU-2 improvement on IU-Xray.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"43 12","pages":"4253-4265"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141478183","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":"LCGNet: Local Sequential Feature Coupling Global Representation Learning for Functional Connectivity Network Analysis With fMRI","authors":"Jie Zhou;Biao Jie;Zhengdong Wang;Zhixiang Zhang;Tongchun Du;Weixin Bian;Yang Yang;Jun Jia","doi":"10.1109/TMI.2024.3421360","DOIUrl":"10.1109/TMI.2024.3421360","url":null,"abstract":"Analysis of functional connectivity networks (FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) has greatly advanced our understanding of brain diseases, including Alzheimer’s disease (AD) and attention deficit hyperactivity disorder (ADHD). Advanced machine learning techniques, such as convolutional neural networks (CNNs), have been used to learn high-level feature representations of FCNs for automated brain disease classification. Even though convolution operations in CNNs are good at extracting local properties of FCNs, they generally cannot well capture global temporal representations of FCNs. Recently, the transformer technique has demonstrated remarkable performance in various tasks, which is attributed to its effective self-attention mechanism in capturing the global temporal feature representations. However, it cannot effectively model the local network characteristics of FCNs. To this end, in this paper, we propose a novel network structure for Local sequential feature Coupling Global representation learning (LCGNet) to take advantage of convolutional operations and self-attention mechanisms for enhanced FCN representation learning. Specifically, we first build a dynamic FCN for each subject using an overlapped sliding window approach. We then construct three sequential components (i.e., edge-to-vertex layer, vertex-to-network layer, and network-to-temporality layer) with a dual backbone branch of CNN and transformer to extract and couple from local to global topological information of brain networks. Experimental results on two real datasets (i.e., ADNI and ADHD-200) with rs-fMRI data show the superiority of our LCGNet.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"43 12","pages":"4319-4330"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141478184","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":"Dual-Domain Collaborative Diffusion Sampling for Multi-Source Stationary Computed Tomography Reconstruction","authors":"Zirong Li;Dingyue Chang;Zhenxi Zhang;Fulin Luo;Qiegen Liu;Jianjia Zhang;Guang Yang;Weiwen Wu","doi":"10.1109/TMI.2024.3420411","DOIUrl":"10.1109/TMI.2024.3420411","url":null,"abstract":"The multi-source stationary CT, where both the detector and X-ray source are fixed, represents a novel imaging system with high temporal resolution that has garnered significant interest. Limited space within the system restricts the number of X-ray sources, leading to sparse-view CT imaging challenges. Recent diffusion models for reconstructing sparse-view CT have generally focused separately on sinogram or image domains. Sinogram-centric models effectively estimate missing projections but may introduce artifacts, lacking mechanisms to ensure image correctness. Conversely, image-domain models, while capturing detailed image features, often struggle with complex data distribution, leading to inaccuracies in projections. Addressing these issues, the Dual-domain Collaborative Diffusion Sampling (DCDS) model integrates sinogram and image domain diffusion processes for enhanced sparse-view reconstruction. This model combines the strengths of both domains in an optimized mathematical framework. A collaborative diffusion mechanism underpins this model, improving sinogram recovery and image generative capabilities. This mechanism facilitates feedback-driven image generation from the sinogram domain and uses image domain results to complete missing projections. Optimization of the DCDS model is further achieved through the alternative direction iteration method, focusing on data consistency updates. Extensive testing, including numerical simulations, real phantoms, and clinical cardiac datasets, demonstrates the DCDS model’s effectiveness. It consistently outperforms various state-of-the-art benchmarks, delivering exceptional reconstruction quality and precise sinogram.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"43 10","pages":"3398-3411"},"PeriodicalIF":0.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462763","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":"DCDiff: Dual-Granularity Cooperative Diffusion Models for Pathology Image Analysis","authors":"Jiansong Fan;Tianxu Lv;Pei Wang;Xiaoyan Hong;Yuan Liu;Chunjuan Jiang;Jianming Ni;Lihua Li;Xiang Pan","doi":"10.1109/TMI.2024.3420804","DOIUrl":"10.1109/TMI.2024.3420804","url":null,"abstract":"Whole Slide Images (WSIs) are paramount in the medical field, with extensive applications in disease diagnosis and treatment. Recently, many deep-learning methods have been used to classify WSIs. However, these methods are inadequate for accurately analyzing WSIs as they treat regions in WSIs as isolated entities and ignore contextual information. To address this challenge, we propose a novel Dual-Granularity Cooperative Diffusion Model (DCDiff) for the precise classification of WSIs. Specifically, we first design a cooperative forward and reverse diffusion strategy, utilizing fine-granularity and coarse-granularity to regulate each diffusion step and gradually improve context awareness. To exchange information between granularities, we propose a coupled U-Net for dual-granularity denoising, which efficiently integrates dual-granularity consistency information using the designed Fine- and Coarse-granularity Cooperative Aware (FCCA) model. Ultimately, the cooperative diffusion features extracted by DCDiff can achieve cross-sample perception from the reconstructed distribution of training samples. Experiments on three public WSI datasets show that the proposed method can achieve superior performance over state-of-the-art methods. The code is available at \u0000<uri>https://github.com/hemo0826/DCDiff</uri>\u0000.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"43 12","pages":"4393-4403"},"PeriodicalIF":0.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462671","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":"STAR-RL: Spatial-Temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-Resolution","authors":"Wenting Chen;Jie Liu;Tommy W. S. Chow;Yixuan Yuan","doi":"10.1109/TMI.2024.3419809","DOIUrl":"10.1109/TMI.2024.3419809","url":null,"abstract":"Pathology image are essential for accurately interpreting lesion cells in cytopathology screening, but acquiring high-resolution digital slides requires specialized equipment and long scanning times. Though super-resolution (SR) techniques can alleviate this problem, existing deep learning models recover pathology image in a black-box manner, which can lead to untruthful biological details and misdiagnosis. Additionally, current methods allocate the same computational resources to recover each pixel of pathology image, leading to the sub-optimal recovery issue due to the large variation of pathology image. In this paper, we propose the first hierarchical reinforcement learning framework named Spatial-Temporal hierARchical Reinforcement Learning (STAR-RL), mainly for addressing the aforementioned issues in pathology image super-resolution problem. We reformulate the SR problem as a Markov decision process of interpretable operations and adopt the hierarchical recovery mechanism in patch level, to avoid sub-optimal recovery. Specifically, the higher-level spatial manager is proposed to pick out the most corrupted patch for the lower-level patch worker. Moreover, the higher-level temporal manager is advanced to evaluate the selected patch and determine whether the optimization should be stopped earlier, thereby avoiding the over-processed problem. Under the guidance of spatial-temporal managers, the lower-level patch worker processes the selected patch with pixel-wise interpretable actions at each time step. Experimental results on medical images degraded by different kernels show the effectiveness of STAR-RL. Furthermore, STAR-RL validates the promotion in tumor diagnosis with a large margin and shows generalizability under various degradations. The source code is available at \u0000<uri>https://github.com/CUHK-AIM-Group/STAR-RL</uri>\u0000.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"43 12","pages":"4368-4379"},"PeriodicalIF":0.0,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462332","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":"Continuous 3D Myocardial Motion Tracking via Echocardiography","authors":"Chengkang Shen;Hao Zhu;You Zhou;Yu Liu;Si Yi;Lili Dong;Weipeng Zhao;David J. Brady;Xun Cao;Zhan Ma;Yi Lin","doi":"10.1109/TMI.2024.3419780","DOIUrl":"10.1109/TMI.2024.3419780","url":null,"abstract":"Myocardial motion tracking stands as an essential clinical tool in the prevention and detection of cardiovascular diseases (CVDs), the foremost cause of death globally. However, current techniques suffer from incomplete and inaccurate motion estimation of the myocardium in both spatial and temporal dimensions, hindering the early identification of myocardial dysfunction. To address these challenges, this paper introduces the Neural Cardiac Motion Field (NeuralCMF). NeuralCMF leverages implicit neural representation (INR) to model the 3D structure and the comprehensive 6D forward/backward motion of the heart. This method surpasses pixel-wise limitations by offering the capability to continuously query the precise shape and motion of the myocardium at any specific point throughout the cardiac cycle, enhancing the detailed analysis of cardiac dynamics beyond traditional speckle tracking. Notably, NeuralCMF operates without the need for paired datasets, and its optimization is self-supervised through the physics knowledge priors in both space and time dimensions, ensuring compatibility with both 2D and 3D echocardiogram video inputs. Experimental validations across three representative datasets support the robustness and innovative nature of the NeuralCMF, marking significant advantages over existing state-of-the-art methods in cardiac imaging and motion tracking. Code is available at: \u0000<uri>https://njuvision.github.io/NeuralCMF</uri>\u0000.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"43 12","pages":"4236-4252"},"PeriodicalIF":0.0,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141462351","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":"Toward Human-Scale Magnetic Particle Imaging: Development of the First System With Superconductor- Based Selection Coils","authors":"Tuan-Anh Le;Minh Phu Bui;Yaser Hadadian;Khaled Mohamed Gadelmowla;Seungjun Oh;Chaemin Im;Seungyong Hahn;Jungwon Yoon","doi":"10.1109/TMI.2024.3419427","DOIUrl":"10.1109/TMI.2024.3419427","url":null,"abstract":"Magnetic Particle Imaging (MPI) is an emerging tomographic modality that allows for precise three-dimensional (3D) mapping of magnetic nanoparticles (MNPs) concentration and distribution. Although significant progress has been made towards improving MPI since its introduction, scaling it up for human applications has proven challenging. High-quality images have been obtained in animal-scale MPI scanners with gradients up to 7 T/m/\u0000<inline-formula> <tex-math>$mu _{{0}}$ </tex-math></inline-formula>\u0000, however, for MPI systems with bore diameters around 200 mm the gradients generated by electromagnets drop significantly to below 0.5 T/m/\u0000<inline-formula> <tex-math>$mu _{{0}}$ </tex-math></inline-formula>\u0000. Given the current technological limitations in image reconstruction and the properties of available MNPs, these low gradients inherently impose limitations on improving MPI resolution for higher precision medical imaging. Utilizing superconductors stands out as a promising approach for developing a human-scale MPI system. In this study, we introduce, for the first time, a human-scale amplitude modulation (AM) MPI system with superconductor-based selection coils. The system achieves an unprecedented magnetic field gradient of up to 2.5 T/m/\u0000<inline-formula> <tex-math>$mu _{{0}}$ </tex-math></inline-formula>\u0000 within a 200 mm bore diameter, enabling large fields of view of \u0000<inline-formula> <tex-math>$100times 130times 98$ </tex-math></inline-formula>\u0000 mm3 at 2.5 T/m/\u0000<inline-formula> <tex-math>$mu _{{0}}$ </tex-math></inline-formula>\u0000 for 3D imaging. While obtained spatial resolution is in the order of previous animal-scale AM MPIs, incorporating superconductors for achieving such high gradients in a 200 mm bore diameter marks a major step toward clinical MPI.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"43 12","pages":"4266-4280"},"PeriodicalIF":0.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141461400","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":"HiCervix: An Extensive Hierarchical Dataset and Benchmark for Cervical Cytology Classification","authors":"De Cai;Jie Chen;Junhan Zhao;Yuan Xue;Sen Yang;Wei Yuan;Min Feng;Haiyan Weng;Shuguang Liu;Yulong Peng;Junyou Zhu;Kanran Wang;Christopher Jackson;Hongping Tang;Junzhou Huang;Xiyue Wang","doi":"10.1109/TMI.2024.3419697","DOIUrl":"10.1109/TMI.2024.3419697","url":null,"abstract":"Cervical cytology is a critical screening strategy for early detection of pre-cancerous and cancerous cervical lesions. The challenge lies in accurately classifying various cervical cytology cell types. Existing automated cervical cytology methods are primarily trained on databases covering a narrow range of coarse-grained cell types, which fail to provide a comprehensive and detailed performance analysis that accurately represents real-world cytopathology conditions. To overcome these limitations, we introduce HiCervix, the most extensive, multi-center cervical cytology dataset currently available to the public. HiCervix includes 40,229 cervical cells from 4,496 whole slide images, categorized into 29 annotated classes. These classes are organized within a three-level hierarchical tree to capture fine-grained subtype information. To exploit the semantic correlation inherent in this hierarchical tree, we propose HierSwin, a hierarchical vision transformer-based classification network. HierSwin serves as a benchmark for detailed feature learning in both coarse-level and fine-level cervical cancer classification tasks. In our comprehensive experiments, HierSwin demonstrated remarkable performance, achieving 92.08% accuracy for coarse-level classification and 82.93% accuracy averaged across all three levels. When compared to board-certified cytopathologists, HierSwin achieved high classification performance (0.8293 versus 0.7359 averaged accuracy), highlighting its potential for clinical applications. This newly released HiCervix dataset, along with our benchmark HierSwin method, is poised to make a substantial impact on the advancement of deep learning algorithms for rapid cervical cancer screening and greatly improve cancer prevention and patient outcomes in real-world clinical settings.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"43 12","pages":"4344-4355"},"PeriodicalIF":0.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141461398","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":"PtbNet: Based on Local Few-Shot Classes and Small Objects to Accurately Detect PTB","authors":"Wenhui Yang;Shuo Gao;Hao Zhang;Hong Yu;Menglei Xu;Puimun Chong;Weijie Zhang;Hong Wang;Wenjuan Zhang;Airong Qian","doi":"10.1109/TMI.2024.3419134","DOIUrl":"10.1109/TMI.2024.3419134","url":null,"abstract":"Pulmonary Tuberculosis (PTB) is one of the world’s most infectious illnesses, and its early detection is critical for preventing PTB. Digital Radiography (DR) has been the most common and effective technique to examine PTB. However, due to the variety and weak specificity of phenotypes on DR chest X-ray (DCR), it is difficult to make reliable diagnoses for radiologists. Although artificial intelligence technology has made considerable gains in assisting the diagnosis of PTB, it lacks methods to identify the lesions of PTB with few-shot classes and small objects. To solve these problems, geometric data augmentation was used to increase the size of the DCRs. For this purpose, a diffusion probability model was implemented for six few-shot classes. Importantly, we propose a new multi-lesion detector PtbNet based on RetinaNet, which was constructed to detect small objects of PTB lesions. The results showed that by two data augmentations, the number of DCRs increased by 80% from 570 to 2,859. In the pre-evaluation experiments with the baseline, RetinaNet, the AP improved by 9.9 for six few-shot classes. Our extensive empirical evaluation showed that the AP of PtbNet achieved 28.2, outperforming the other 9 state-of-the-art methods. In the ablation study, combined with BiFPN+ and PSPD-Conv, the AP increased by 2.1, APs increased by 5.0, and grew by an average of 9.8 in APm and APl. In summary, PtbNet not only improves the detection of small-object lesions but also enhances the ability to detect different types of PTB uniformly, which helps physicians diagnose PTB lesions accurately. The code is available at \u0000<uri>https://github.com/Wenhui-person/PtbNet/tree/master</uri>\u0000.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"43 12","pages":"4331-4343"},"PeriodicalIF":0.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141461399","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":"Accurate Airway Tree Segmentation in CT Scans via Anatomy-Aware Multi-Class Segmentation and Topology-Guided Iterative Learning","authors":"Puyang Wang;Dazhou Guo;Dandan Zheng;Minghui Zhang;Haogang Yu;Xin Sun;Jia Ge;Yun Gu;Le Lu;Xianghua Ye;Dakai Jin","doi":"10.1109/TMI.2024.3419707","DOIUrl":"10.1109/TMI.2024.3419707","url":null,"abstract":"Intrathoracic airway segmentation in computed tomography is a prerequisite for various respiratory disease analyses such as chronic obstructive pulmonary disease, asthma and lung cancer. Due to the low imaging contrast and noises execrated at peripheral branches, the topological-complexity and the intra-class imbalance of airway tree, it remains challenging for deep learning-based methods to segment the complete airway tree (on extracting deeper branches). Unlike other organs with simpler shapes or topology, the airway’s complex tree structure imposes an unbearable burden to generate the “ground truth” label (up to 7 or 3 hours of manual or semi-automatic annotation per case). Most of the existing airway datasets are incompletely labeled/annotated, thus limiting the completeness of computer-segmented airway. In this paper, we propose a new anatomy-aware multi-class airway segmentation method enhanced by topology-guided iterative self-learning. Based on the natural airway anatomy, we formulate a simple yet highly effective anatomy-aware multi-class segmentation task to intuitively handle the severe intra-class imbalance of the airway. To solve the incomplete labeling issue, we propose a tailored iterative self-learning scheme to segment toward the complete airway tree. For generating pseudo-labels to achieve higher sensitivity (while retaining similar specificity), we introduce a novel breakage attention map and design a topology-guided pseudo-label refinement method by iteratively connecting breaking branches commonly existed from initial pseudo-labels. Extensive experiments have been conducted on four datasets including two public challenges. The proposed method achieves the top performance in both EXACT’09 challenge using average score and ATM’22 challenge on weighted average score. In a public BAS dataset and a private lung cancer dataset, our method significantly improves previous leading approaches by extracting at least (absolute) 6.1% more detected tree length and 5.2% more tree branches, while maintaining comparable precision.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"43 12","pages":"4294-4306"},"PeriodicalIF":0.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141461397","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}