IEEE transactions on medical imaging最新文献

{"title":"Mitigating Data Consistency Induced Discrepancy in Cascaded Diffusion Models for Sparse-View CT Reconstruction","authors":"Hanyu Chen;Zhixiu Hao;Lin Guo;Liying Xiao","doi":"10.1109/TMI.2025.3557243","DOIUrl":"10.1109/TMI.2025.3557243","url":null,"abstract":"Sparse-view Computed Tomography (CT) image reconstruction is a promising approach to reduce radiation exposure, but it inevitably leads to image degradation. Although diffusion model-based approaches are computationally expensive and suffer from the training-sampling discrepancy, they provide a potential solution to the problem. This study introduces a novel Cascaded Diffusion with Discrepancy Mitigation (CDDM) framework, including the low-quality image generation in latent space and the high-quality image generation in pixel space which contains data consistency and discrepancy mitigation in a one-step reconstruction process. The cascaded framework minimizes computational costs by replacing some inference steps from pixel to latent space. The discrepancy mitigation technique addresses the training-sampling gap induced by data consistency, ensuring the data distribution is close to the original diffusion manifold. A specialized Alternating Direction Method of Multipliers (ADMM) is employed to process image gradients in separate directions, offering a more targeted approach to regularization. Experimental results across several datasets demonstrate CDDM’s superior performance in high-quality image generation with clearer boundaries compared to existing methods, highlighting the framework’s computational efficiency.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 7","pages":"3012-3024"},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143775216","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":"Boundary-Guided Contrastive Learning for Semi-Supervised Medical Image Segmentation","authors":"Yang Yang;Jiaxin Zhuang;Guoying Sun;Ruixuan Wang;Jingyong Su","doi":"10.1109/TMI.2025.3556482","DOIUrl":"10.1109/TMI.2025.3556482","url":null,"abstract":"Semi-supervised learning methods, compared to fully supervised learning, offer significant potential to alleviate the burden of manual annotations on clinicians. By leveraging unlabeled data, these methods can aid in the development of medical image segmentation systems for improving efficiency. Boundary segmentation is crucial in medical image analysis. However, accurate segmentation of boundary regions is under-explored in existing methods since boundary pixels constitute only a small fraction of the overall image, resulting in suboptimal segmentation performance for boundary regions. In this paper, we introduce boundary-guided contrastive learning for semi-supervised medical image segmentation (BoCLIS). Specifically, we first propose conservative-to-radical teacher networks with an uncertainty-weighted aggregation strategy to generate higher quality pseudo-labels, enabling more efficient utilization of unlabeled data. To further improve the performance of segmentation in boundary regions, we propose a boundary-guided patch sampling strategy to guide the framework in learning discriminative representations for these regions. Lastly, the patch-based contrastive learning is proposed to simultaneously compute the (dis)similarities of the discriminative representations across intra- and inter-images. Extensive experiments on three public datasets show that our method consistently outperforms existing methods, especially in the boundary region, with DSC improvements of 20.47%, 16.75%, and 17.18%, respectively. A comprehensive analysis is further performed to demonstrate the effectiveness of our approach. Our code is released publicly at <uri>https://github.com/youngyzzZ/BoCLIS</uri>.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 7","pages":"2973-2988"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143757795","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":"CBCT Reconstruction Using Single X-Ray Projection With Cycle-Domain Geometry-Integrated Denoising Diffusion Probabilistic Models","authors":"Shaoyan Pan;Junbo Peng;Yuan Gao;Shao-Yuan Lo;Tianyu Luan;Junyuan Li;Tonghe Wang;Chih-Wei Chang;Zhen Tian;Xiaofeng Yang","doi":"10.1109/TMI.2025.3556402","DOIUrl":"10.1109/TMI.2025.3556402","url":null,"abstract":"In the sphere of Cone Beam Computed Tomography (CBCT), acquiring X-ray projections from sufficient angles is indispensable for traditional image reconstruction methods to accurately reconstruct 3D anatomical intricacies. However, this acquisition procedure for the linear accelerator-mounted CBCT systems in radiotherapy takes approximately one minute, impeding its use for ultra-fast intra-fractional motion monitoring during treatment delivery. To address this challenge, we introduce the Patient-specific Cycle-domain Geometric-integrated Denoising Diffusion Probabilistic Model (CG-DDPM). This model aims to leverage patient-specific priors from patient’s CT/4DCT images, which are acquired for treatment planning purposes, to reconstruct 3D CBCT from a single-view 2D CBCT projection of any arbitrary angle during treatment, namely single-view reconstructed CBCT (svCBCT). The CG-DDPM framework encompasses a dual DDPM structure: the Projection-DDPM for synthesizing comprehensive full-view projections and the CBCT-DDPM for creating CBCT images. A key innovation is our Cycle-Domain Geometry-Integrated (CDGI) method, incorporating a Cone Beam X-ray Geometric Transformation Module (GTM) to ensure precise, synergistic operation between the dual DDPMs, thereby enhancing reconstruction accuracy and reducing artifacts. Evaluated in a study involving 37 lung cancer patients, the method demonstrated its ability to reconstruct CBCT not only from simulated X-ray projections but also from real-world data. The CG-DDPM significantly outperforms existing V-shape convolutional neural networks (V-nets), Generative Adversarial Networks (GANs), and DDPM methods in terms of reconstruction fidelity and artifact minimization. This was confirmed through extensive voxel-level, structural, visual, and clinical assessments. The capability of CG-DDPM to generate high-quality reconstructed CBCT from a single-view projection at any arbitrary angle using a single model opens the door for ultra-fast, in-treatment volumetric imaging. This is especially beneficial for radiotherapy at motion-associated cancer sites and image-guided interventional procedures.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 7","pages":"2933-2947"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143757941","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-Supervised Knee Cartilage Segmentation With Successive Eigen Noise-Assisted Mean Teacher Knowledge Distillation","authors":"Sheheryar Khan;Muhammad Ammar Khawer;Rizwan Qureshi;Mehmood Nawaz;Muhammad Asim;Weitian Chen;Hong Yan","doi":"10.1109/TMI.2025.3556870","DOIUrl":"10.1109/TMI.2025.3556870","url":null,"abstract":"Knee cartilage segmentation for Knee Osteoarthritis (OA) diagnosis is challenging due to domain shifts from varying MRI scanning technologies. Existing cross-modality approaches often use paired order matching or style translation techniques to align features. Still, these methods can sacrifice discrimination in less prominent cartilages and overlook critical higher-order correlations and semantic information. To address this issue, we propose a novel framework called Successive Eigen Noise-assisted Mean Teacher Knowledge Distillation (SEN-MTKD) for adapting 2D knee MRI images across different modalities using partially labeled data. Our approach includes the Eigen Low-rank Subspace (ELRS) module, which employs low-rank approximations to generate meaningful pseudo-labels from domain-invariant feature representations progressively. Complementing this, the Successive Eigen Noise (SEN) module introduces advanced data perturbation to enhance discrimination and diversity in small cartilage classes. Additionally, we propose a subspace-based feature distillation loss mechanism (LRBD) to manage variance and leverage rich intermediate representations within the teacher model, ensuring robust feature representation and labeling. Our framework identifies a mutual cross-domain subspace using higher-order structures and lower energy latent features, providing reliable supervision for the student model. Extensive experiments on public and private datasets demonstrate the effectiveness of our method over state-of-the-art benchmarks. The code is available at github.com/AmmarKhawer/SEN-MTKD.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 7","pages":"3051-3063"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143757767","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":"MM-GTUNets: Unified Multi-Modal Graph Deep Learning for Brain Disorders Prediction","authors":"Luhui Cai;Weiming Zeng;Hongyu Chen;Hua Zhang;Yueyang Li;Yu Feng;Hongjie Yan;Lingbin Bian;Wai Ting Siok;Nizhuan Wang","doi":"10.1109/TMI.2025.3556420","DOIUrl":"10.1109/TMI.2025.3556420","url":null,"abstract":"Graph deep learning (GDL) has demonstrated impressive performance in predicting population-based brain disorders (BDs) through the integration of both imaging and non-imaging data. However, the effectiveness of GDL-based methods heavily depends on the quality of modeling multi-modal population graphs and tends to degrade as the graph scale increases. Moreover, these methods often limit interactions between imaging and non-imaging data to node-edge interactions within the graph, overlooking complex inter-modal correlations and resulting in suboptimal outcomes. To address these challenges, we propose MM-GTUNets, an end-to-end Graph Transformer-based multi-modal graph deep learning (MMGDL) framework designed for large-scale brain disorders prediction. To effectively utilize rich multi-modal disease-related information, we introduce <underline>M</u>odality <underline>R</u>eward <underline>R</u>epresentation <underline>L</u>earning (MRRL), which dynamically constructs population graphs using an Affinity Metric Reward System (AMRS). We also employ a variational autoencoder to reconstruct latent representations of non-imaging features aligned with imaging features. Based on this, we introduce <underline>A</u>daptive <underline>C</u>ross-<underline>M</u>odal <underline>G</u>raph <underline>L</u>earning (ACMGL), which captures critical modality-specific and modality-shared features through a unified GTUNet encoder, taking advantages of Graph UNet and Graph Transformer, along with a feature fusion module. We validated our method on two public multi-modal datasets ABIDE and ADHD-200, demonstrating its superior performance in diagnosing BDs. Our code is available at <uri>https://github.com/NZWANG/MM-GTUNets</uri><uri>https://github.com/NZWANG/MM-GTUNets</uri>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 9","pages":"3705-3716"},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143757797","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":"Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation","authors":"Qingjie Zeng;Yutong Xie;Zilin Lu;Mengkang Lu;Yicheng Wu;Yong Xia","doi":"10.1109/TMI.2025.3556310","DOIUrl":"10.1109/TMI.2025.3556310","url":null,"abstract":"The scarcity of annotations has become a significant obstacle in training powerful deep-learning models for medical image segmentation, limiting their clinical application. To overcome this, semi-supervised learning that leverages abundant unlabeled data is highly desirable to enhance model training. However, most existing works still focus on specific medical tasks and underestimate the potential of learning across diverse tasks and datasets. In this paper, we propose a Versatile Semi-supervised framework (VerSemi) to present a new perspective that integrates various SSL tasks into a unified model with an extensive label space, exploiting more unlabeled data for semi-supervised medical image segmentation. Specifically, we introduce a dynamic task-prompted design to segment various targets from different datasets. Next, this unified model is used to identify the foreground regions from all labeled data, capturing cross-dataset semantics. Particularly, we create a synthetic task with a CutMix strategy to augment foreground targets within the expanded label space. To effectively utilize unlabeled data, we introduce a consistency constraint that aligns aggregated predictions from various tasks with those from the synthetic task, further guiding the model to accurately segment foreground regions during training. We evaluated our VerSemi framework against seven established SSL methods on four public benchmarking datasets. Our results suggest that VerSemi consistently outperforms all competing methods, beating the second-best method with a 2.69% average Dice gain on four datasets and setting a new state of the art for semi-supervised medical image segmentation. Code is available at <uri>https://github.com/maxwell0027/VerSemi</uri>","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 7","pages":"2948-2959"},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143745005","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":"PHNet: A Pulmonary Hypertension Detection Network Based on Cine Cardiac Magnetic Resonance Images Using a Hybrid Strategy of Adaptive Triplet and Binary Cross-Entropy Losses","authors":"Xinchen Yuan;Xiaojuan Guo;Yande Luo;Xiuhong Guan;Qi Li;Zhiquan Situ;Zijie Zhou;Xin Huang;Zhaowei Rong;Yunhai Lin;Mingxi Liu;Juanni Gong;Hongyan Liu;Qi Yang;Xinchun Li;Rongli Zhang;Chengwang Lei;Shumao Pang;Guoxi Xie","doi":"10.1109/TMI.2025.3555621","DOIUrl":"10.1109/TMI.2025.3555621","url":null,"abstract":"Pulmonary hypertension (PH) is a fatal pulmonary vascular disease. The standard diagnosis of PH heavily relies on an invasive technique, i.e., right heart catheterization, which leads to a delay in diagnosis and serious consequences. Noninvasive approaches are crucial for detecting PH as early as possible; however, it remains a challenge, especially in detecting mild PH patients. To address this issue, we present a new fully automated framework, hereinafter referred to as PHNet, for noninvasively detecting PH patients, especially improving the detection accuracy of mild PH patients, based on cine cardiac magnetic resonance (CMR) images. The PHNet framework employs a hybrid strategy of adaptive triplet and binary cross-entropy losses (HSATBCL) to enhance discriminative feature learning for classifying PH and non-PH. Triplet pairs in HSATBCL are created using a semi-hard negative mining strategy which maintains the stability of the training process. Experiments show that the detection error rate of PHNet for mild PH is reduced by 24.5% on average compared to state-of-the-art PH detection models. The hybrid strategy can effectively improve the model’s ability to detect PH, making PHNet achieve an average area under the curve (AUC) of 0.964, an accuracy of 0.912, and an F1-score of 0.884 in the internal validation dataset. In the external testing dataset, PHNet achieves an average AUC value of 0.828. Thus, PHNet has great potential for noninvasively detecting PH based on cine CMR images in clinical practice. Future research could explore more clinical information and refine feature extraction to further enhance the network performance.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 7","pages":"2960-2972"},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143745007","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":"MR Spatiospectral Reconstruction Integrating Subspace Modeling and Self-Supervised Spatiotemporal Denoising","authors":"Ruiyang Zhao;Zepeng Wang;Aaron Anderson;Graham Huesmann;Fan Lam","doi":"10.1109/TMI.2025.3555928","DOIUrl":"10.1109/TMI.2025.3555928","url":null,"abstract":"We present a new method that integrates subspace modeling and a pre-learned spatiotemporal denoiser for reconstruction from highly noisy magnetic resonance spectroscopic imaging (MRSI) data. The subspace model imposes an explicit low-dimensional representation of the high-dimensional spatiospectral functions of interest for noise reduction, while the denoiser serves as a complementary spatiotemporal prior to constrain the subspace reconstruction. A self-supervised learning strategy was proposed to train a denoiser that can distinguish the spatiotemporally correlated signals from uncorrelated noise. An iterative reconstruction formalism was developed based on the Plug-and-Play (PnP)-ADMM framework to synergize the subspace constraint, plug-in denoiser and spatiospectral encoding model. We evaluated the proposed method using numerical simulations and in vivo data, demonstrating improved performance over state-of-the-art subspace-based methods. We also provided theoretical analysis on the utility of combining subspace projection and iterative denoising in terms of both algorithm convergence and performance. Our work demonstrated the potential of integrating self-supervised denoising priors and low-dimensional representations for high-dimensional imaging problems.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 7","pages":"3002-3011"},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10945499","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734386","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":"AVP-AP: Self-Supervised Automatic View Positioning in 3D Cardiac CT via Atlas Prompting","authors":"Xiaolin Fan;Yan Wang;Yingying Zhang;Mingkun Bao;Bosen Jia;Dong Lu;Yifan Gu;Jian Cheng;Haogang Zhu","doi":"10.1109/TMI.2025.3554785","DOIUrl":"10.1109/TMI.2025.3554785","url":null,"abstract":"Automatic view positioning is crucial for cardiac computed tomography (CT) examinations, including disease diagnosis and surgical planning. However, it is highly challenging due to individual variability and large 3D search space. Existing work needs labor-intensive and time-consuming manual annotations to train view-specific models, which are limited to predicting only a fixed set of planes. However, in real clinical scenarios, the challenge of positioning semantic 2D slices with any orientation into varying coordinate space in arbitrary 3D volume remains unsolved. We thus introduce a novel framework, AVP-AP, the first to use Atlas Prompting for self-supervised Automatic View Positioning in the 3D CT volume. Specifically, this paper first proposes an atlas prompting method, which generates a 3D canonical atlas and trains a network to map slices into their corresponding positions in the atlas space via a self-supervised manner. Then, guided by atlas prompts corresponding to the given query images in a reference CT, we identify the coarse positions of slices in the target CT volume using rigid transformation between the 3D atlas and target CT volume, effectively reducing the search space. Finally, we refine the coarse positions by maximizing the similarity between the predicted slices and the query images in the feature space of a given foundation model. Our framework is flexible and efficient compared to other methods, outperforming other methods by 19.8% average structural similarity (SSIM) in arbitrary view positioning and achieving 9% SSIM in two-chamber view compared to four radiologists. Meanwhile, experiments on a public dataset validate our framework’s generalizability.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 7","pages":"2921-2932"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143713037","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":"Phase-Locked Time-Stretch Optical Coherence Tomography for Contrast-Enhanced Retinal Microangiography","authors":"Gyeong Hun Kim;Seongjin Bak;Hyung-Hoi Kim;Jun Geun Shin;Tae Joong Eom;Chang-Seok Kim;Hwidon Lee","doi":"10.1109/TMI.2025.3555112","DOIUrl":"10.1109/TMI.2025.3555112","url":null,"abstract":"Optical coherence tomography angiography has transformed retinal vascular imaging by providing non-invasive, high-resolution visualization. However, achieving an optimal balance between field of view, resolution, and three-dimensional microvasculature contrast, particularly in deeper retinal layers, remains challenging. A phase-locked time-stretch optical coherence tomography microangiography system is developed to address these limitations with a 5-MHz A-line rate and sub-nm phase sensitivity. Utilizing a dual chirped fiber Bragg grating architecture, the swept-source laser achieves an extended coherence length of ~10 mm and a 102-nm bandwidth. A time-stretch analog-to-digital converter overcomes the limitations of conventional multi-MHz optical coherence tomography systems, ensuring a 2-mm imaging depth in the air with high spatial resolution. The proposed system enables high-contrast, depth-encoded mapping of key retinal structures, including the superficial and deep capillary plexuses and the choriocapillaris. Compared to a state-of-the-art system, the proposed approach demonstrates enhanced resolution, improved contrast, and faster imaging speeds, enhancing its potential for diagnosing and monitoring retinal and systemic diseases like age-related macular degeneration, diabetic retinopathy, and Alzheimer’s disease.","PeriodicalId":94033,"journal":{"name":"IEEE transactions on medical imaging","volume":"44 7","pages":"2906-2920"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10942462","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143713038","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}