Ultrasonic Imaging最新文献

{"title":"RFDNet: Robust Frequency-Based Denoising Network for 3D Ultrasound Vascular Imaging Using a Row-Column Addressed Array.","authors":"Dongkyu Jung, Nizar Guezzi, Sangheon Lee, Noman Muhammad, Sua Bae, Jaesok Yu","doi":"10.1177/01617346251398442","DOIUrl":"10.1177/01617346251398442","url":null,"abstract":"<p><p>Three-dimensional (3D) ultrasound vascular imaging (UVI) is essential for visualizing complex vascular structures. Row-column addressed (RCA) arrays, widely used for 3D UVI due to their hardware efficiency, suffer from point spread function (PSF) anisotropy, resulting in ramp-shaped noise that degrades image quality. Although existing denoising methods, including deep learning-based approaches, have shown promise, they are often limited by domain shift bias and the need for condition-specific data collection. Moreover, as full-volume 3D training is often impractical, many studies rely on 2D slice-wise training with 3D reconstruction, which can yield inter-slice intensity inconsistencies when slices are normalized independently. To overcome these limitations, we propose Robust Frequency-based Denoising Network (RFDNet), which integrates a Deep Frequency Filtering (DFF) module into a standard denoising model. The DFF module adaptively filters frequency components within the encoder, suppressing ramp-shaped noise while dynamically balancing spectral content to reduce sensitivity to domain shifts and inter-slice intensity inconsistencies. This adaptive filtering preserves vascular details and improves overall imaging consistency. Experiments on Doppler phantom, carotid artery, and abdominal datasets show that RFDNet significantly outperforms conventional methods in peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and root mean squared error (RMSE). Further validation through 2D frequency spectrum analysis confirmed that the DFF module dynamically adjusts frequency components to maintain spectral balance. In addition, spectral KL divergence analysis demonstrated its robustness against inter-slice intensity inconsistencies introduced by slice-wise normalization. This approach improves domain generalization, reduces noise artifacts, and enhances clinical applicability by improving imaging reliability. Future work will explore 3D training and architectural refinements for better computational efficiency.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"276-293"},"PeriodicalIF":2.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145858857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing Optimized Mixed-Order Relation-Aware Recurrent Neural Network for Metacarpophalangeal Rheumatoid Arthritis Grading via Ultrasound Images.","authors":"G Sudha, M Mohammadha Hussaini, T Dharma Raj, Veeresh R K","doi":"10.1177/01617346251389620","DOIUrl":"10.1177/01617346251389620","url":null,"abstract":"<p><p>The diagnostic problem of grading evaluation of ultrasonic images of Metacarpophalangeal rheumatoid arthritis (RA) is mostly dependent on the skills of sonographers with training. A grading system is used to identify and evaluate the geometric and textural features of bone deterioration and synovium thickening. In this manuscript, utilizing optimized mixed-order relation-aware recurrent neural network for metacarpophalangeal rheumatoid arthritis grading via ultrasound images (MRAG-UI-MORARNN-BWKA) is proposed. First, Tianjin University of Traditional Chinese Medicine's First Teaching Hospital provides the input ultrasound images. The pre-processing step uses confidence partitioning sampling filtering (CPSF) to resize the input images and eliminate background noise. Afterward, the pre-processed images were given to unpaired multi-view graph clustering (UMGC) for segmenting the region of interest (ROI). The holistic dynamic frequency transformer (HDFT) was used for extracting the geometric features like area, thickness, and shape. The Black winged kite algorithm (BWKA) was then employed to optimize the mixed-order relation-aware recurrent neural network (MORARNN) for precise grading of rheumatoid arthritis detection, with grades 0 (no synovium thickening), 1, 2, and 3 (mild, moderate, and severe, respectively). Python is used in the implementation of the proposed MRAG-UI-MORARNN-BWKA method. The proposed strategy achieves significant improvements over existing methods in grading rheumatoid arthritis via ultrasound images. The proposed model attains an accuracy of 97.02%, precision of 97.5% and sensitivity of 97.25%, respectively. These results clearly indicate the better performance and robustness of the proposed method analyzed to existing methods.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"219-235"},"PeriodicalIF":2.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145662594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CDP-KDNet: Curriculum-Guided Dynamic Pruning and Knowledge Distillation for Resource-Efficient Ultrasound Elastography.","authors":"Yan Li, Tianqiang Xiang, Jiachen Dang, Han Yang, Jingfeng Jiang, Bo Peng","doi":"10.1177/01617346251399875","DOIUrl":"10.1177/01617346251399875","url":null,"abstract":"<p><p>In recent years, convolutional neural network (CNN)-based optical flow models for motion estimation have been applied to radio-frequency (RF) ultrasound and B-mode (BM) data, demonstrating excellent performance. However, their architectures result in intricate network structures with a large number of parameters, posing challenges for deployment on resource-constrained devices. This paper proposes a novel approach that integrates dynamic pruning, knowledge distillation, and curriculum learning for model compression. The proposed method substantially reduces the complexity of deep learning models (i.e., memory demands and computational costs) while minimizing performance degradation. The teacher network was initially developed based on the Unsupervised Motion Estimation CNN (UMEN-Net). Subsequently, we developed a sub-network to reduce the number of parameters, referred to as DP-Net, and applied the proposed training techniques to obtain the final model, CDP-KDNet. The CDP-KDNet model was evaluated on simulated, phantom, and in vivo ultrasound data. Compared to DP-Net and other lightweight CNNs, CDP-KDNet achieves superior Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR) for axial strain estimation across all tested datasets. Its performance closely matches that of the teacher network while utilizing only 45.3% of the parameters and 67.8% of the floating-point operations. Additionally, as an unsupervised model, CDP-KDNet does not require ground-truth labels during training, rendering it a promising approach for ultrasound motion estimation.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"247-260"},"PeriodicalIF":2.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145858901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced Fetal Cardiac Disease Detection Using Optimized Gegenbauer Graph Neural Networks on Ultrasound Images to Facilitate Early Diagnosis and Clinical Assessment.","authors":"Subasree Santhanaraman, NalliyaGounder Kuppuswamy Sakthivel, Amit Kumar Tyagi, Tharani Balamurugan","doi":"10.1177/01617346251390849","DOIUrl":"10.1177/01617346251390849","url":null,"abstract":"<p><p>Early detection of fetal cardiac diseases can dramatically improve neonatal outcomes by enabling timely intervention and informed clinical management. However, accurate diagnosis remains challenging due to the complexity of fetal heart structures in ultrasound images and the subtlety of congenital anomalies. To address these challenges, this work introduces Fetal Cardiac Disease Detection Using Ultrasound Imaging with Gegenbauer Graph Neural Networks (FCD-UI-GGNN). Ultrasound images are collected from the Fetal Phantom Ultrasound Dataset 23 (FPUS23) and pre-processed using Broad Collaborative Filtering (BCF) to resize images while preserving critical anatomical details. Fast Continual Multi-View Clustering (FCMVC) segments target vessel structures, and Gegenbauer Graph Neural Networks (GGNN) detects cardiac anomalies by modeling both local and global vessel relationships. The network weights are optimized using the Bitterling Fish Optimization Algorithm (BFOA) to improve accuracy. The framework is evaluated utilizing Accuracy, Precision, <i>F</i>1-Score, Recall, and ROC analysis, achieving 98.78% Accuracy, 99.01% Recall, 98.36% Precision, and 99.12% <i>F</i>1-Score. Validation on additional datasets, including FPUS23 and 4D Fetal Cardiac Ultrasound images, confirms robust generalization. These results demonstrate highly reliable and precise detection, supporting early clinical intervention for fetal cardiac anomalies.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"294-306"},"PeriodicalIF":2.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145835163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Ultrasound Radiomics Model for the Early Diagnosis of Cervical Cancer.","authors":"Shuang Dong, Ya-Nan Feng, Xiao-Ying Li, Yan-Qing Peng, Xiao-Shan Du, Li-Tao Sun","doi":"10.1177/01617346251403960","DOIUrl":"10.1177/01617346251403960","url":null,"abstract":"<p><p>We evaluated whether image-radiomics features extracted from ultrasound with integrated genomic data of single nucleotide polymorphisms (SNPs) associated with CIN susceptibility and clinical features could improve the differential diagnosis of high-grade intraepithelial disease (HSIL) and stage IA CC. Models were developed from ultrasound-derived radiomic features, SNPs data and clinical variables. After random 7:3 allocation into training and validation sets, clinical and SNPs datasets were each screened by univariable then multivariable logistic regression to build separate predictors. Ultrasound radiomics features were reduced with Max-relevance and min-redundancy (mRMR) and least absolute contraction and selection operator (LASSO) to generate an ultrasound radiomic score, which was subsequently used with clinical and SNPs data to establish the combined model. For the differentiation of HSIL and early CC models, only the ultrasound radiomics model showed higher classification efficiency, which the performance in the validation cohort (AUC: 0.885 [95%CI: 0.751-1.000]) than the method combining ultrasound radiomics score, clinical data and SNPs data with an AUC value of 0.850[95%CI: 0.713-0.987]. The model developed and constructed in this study, based on ultrasound radiomics, demonstrates potential for differentiating HSIL from Stage IA CC and exhibits significant clinical application value.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"236-246"},"PeriodicalIF":2.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145858883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid Physics-Driven Deep Learning for Enhanced Ultrasound Image Quality and Speckle Noise Suppression.","authors":"Taher Slimi, Nahla Majdoub Bhiri, Hajer Chtioui, Anouar Ben Khalifa","doi":"10.1177/01617346261452679","DOIUrl":"https://doi.org/10.1177/01617346261452679","url":null,"abstract":"<p><p>Ultrasound imaging is widely used in clinical practice due to its non-invasive, real-time, and cost-effective nature. However, speckle noise often degrades image quality, obscuring fine anatomical structures and reducing diagnostic confidence. Existing denoising methods struggle to remove noise effectively while preserving critical details, limiting their clinical utility. Although recent deep learning architectures excel at capturing both local details and global structure, they remain inherently limited in handling speckle noise, as its physical characteristics are not explicitly incorporated. To address this limitation, a Physics-Regularized Self-Supervised Denoising U-Net (PR-SSD-Net) is introduced to reinforce the U-Net's capability for high-quality image restoration. The physics-based constraint guides the network to produce residual noise patterns that align with expected statistical behavior, enhancing image clarity and preserving critical structures. Comprehensive evaluations were conducted on six diverse ultrasound datasets. Significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) were observed, accompanied by reduced variability, as reflected in their standard deviations (SD). An ablation study confirmed the pivotal role of physics-guided regularization, and expert assessments demonstrated high inter-rater agreement (Fleiss), supporting the clinical relevance of the approach. These results highlight the proposed PR-SSD-Net approach as a robust, physically grounded solution for speckle noise reduction, enhancing both the reliability and clinical utility of ultrasound imaging.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"1617346261452679"},"PeriodicalIF":1.7,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148309233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Task Segmentation and Classification Network Based on Ultrasound Images for Predicting the Grading of Ascites in the Abdominal Cavity.","authors":"Feng Xie, Le Tao, Hejing Huang, Chengcheng Liu, Dean Ta","doi":"10.1177/01617346261450223","DOIUrl":"https://doi.org/10.1177/01617346261450223","url":null,"abstract":"<p><p>Abdominal trauma with bleeding is a leading cause of post-traumatic death, and detecting free fluid in the abdomen or hemoperitoneum can provide critical guidance for clinical management. Rapid and accurate diagnosis of abdominal bleeding using ultrasound is significant for making decisions regarding the need for surgical intervention. This study introduces a multi-task network for the segmentation and classification of ascites in ultrasound images. The network utilizes a U-Net backbone with a ResNext encoder as the basic architecture for the segmentation and classification models. The segmentation network includes a Frequency Channel Attention (FCA) attention module, which effectively broadens the range of captured information and enhances the robustness of channel representation. Furthermore, an Enhanced Channel Attention Multi Feature Fusion (EMFF) was used to extract the interdependencies between feature channels by combining high-order and low-order feature mappings, thereby improving segmentation accuracy. Lastly, a classification branch was created to classify ascites by sharing encoder features. Experiments on the collected ascites ultrasound dataset demonstrated that the proposed method achieved a segmentation Dice of 85.28% and a classification accuracy of 86.18%. It outperformed the leading multi-task SOTA method by 0.7% in Dice and 2.03% in accuracy, establishing a new benchmark for simultaneous ascites assessment. This study showed that the proposed network is valuable for the preliminary diagnosis of ascites in ultrasound and can serve as a potential auxiliary tool for clinical ascites examination in emergency situations.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"1617346261450223"},"PeriodicalIF":1.7,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148309228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Super-Resolution Ultrasound Contrast Imaging in Differentiating Benign From Malignant Breast Tumors.","authors":"Tong Zhao, Zhan Wang, Ye Yao, Heng Zhang, Xiaoqin Li, Xinye Ni, Chao Zhang, Bobo Wu","doi":"10.1177/01617346261454313","DOIUrl":"https://doi.org/10.1177/01617346261454313","url":null,"abstract":"<p><p>This study evaluated the utility of super-resolution ultrasound contrast imaging for differentiating benign from malignant breast tumors. In this retrospective study, 60 patients underwent contrast-enhanced ultrasound acquisition before biopsy or surgery excision at The Third Affiliated Hospital of Nanjing Medical University between March and November 2025. Super-resolution contrast-enhanced ultrasound (SRUS) reconstruction and quantitative analysis were then performed from contrast-enhanced ultrasound cine loops using microbubble localization and tracking software. Using histopathology as the reference standard, patients were classified into malignant and benign groups. The discriminatory value of each parameter for benign versus malignant breast tumors was assessed. Diagnostic performance was quantified using the area under the receiver operating characteristic curve (AUC). The diagnostic performance of each SRUS-derived parameter was evaluated for differentiating benign from malignant breast tumors. On final pathology, SRUS-derived parameters showed good performance for differentiating benign from malignant breast tumors. Compared with benign lesions, malignant tumors exhibited significantly higher FD, number of branching points per area maximum curvature, mean curvature, maximum vessel velocity, mean vessel velocity, maximum diameter, mean diameter, and microvasculature density (all <i>p</i> < .050). Among all parameters, maximum curvature achieved the highest AUC (0.933), with 88.0% sensitivity and 80.0% specificity. Intra- and inter-operator reliability were high, with intraclass correlation coefficients >.85. SRUS-derived quantitative parameters showed diagnostic value for differentiating benign from malignant breast masses and enable clear visualization of hemodynamic alterations, suggesting potential value for preoperative malignant-risk assessment.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"1617346261454313"},"PeriodicalIF":2.5,"publicationDate":"2026-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148101973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Preprocessing Method for Common Carotid Artery Ultrasound Images Based on Phase Asymmetry Metric and Non-subsampled Shearlet Transform.","authors":"Kun Wang, Wenlu Lou, Keyan Wu, Chunping Pang, Jinpeng Zhang","doi":"10.1177/01617346261441786","DOIUrl":"https://doi.org/10.1177/01617346261441786","url":null,"abstract":"<p><p>The intima media thickness (IMT) of the common carotid artery (CCA) is helpful for the diagnosis of atherosclerosis. The CCA ultrasound image segmentation methods can be used for the IMT measurements. However, the speckle noise and the blurred edges can reduce the accuracy of the IMT measurements. To address this issue, a preprocessing method based on non-subsampled shearlet transform (NSST) and Phase Asymmetry Metric is proposed for the CCA ultrasound images. First, the CCA ultrasound image is decomposed into the NSST domain with a low-frequency sub-band and several high-frequency sub-bands. Next, the high frequency sub-band image is despeckling using an adaptive threshold method. Then, the processed image is reconstructed through NSST. Finally, an edge enhancement method based on the phase asymmetry (PAS) metric is used for enhancing the edges in the reconstructed CCA ultrasound images. In the simulated CCA ultrasound image experiments, the CNR value of proposed method is 1.8191 dB, which is the highest among four state-of-the-art methods. In the clinical CCA ultrasound image experiments, the average CNR values of proposed method is 1.5457 ± 0.6595 (mean ± std in dB), which is the best among four advanced methods. In the real application of IMT measurements, the average absolute error of intima-media thickness measurements of proposed method is 0.0790 ± 0.0588 (mean ± std in mm), which is the lowest among four advanced methods. In conclusion, the proposed approach has improved performance in suppressing the speckle noise and enhancing the intima media complex (IMC) regions, and it is a potential method for preprocessing of the CCA ultrasound images in clinical application.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"1617346261441786"},"PeriodicalIF":2.5,"publicationDate":"2026-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147934941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generalized Null Subtraction Factor: A Post-Filtering Framework for Contrast Enhancement in Ultrafast Ultrasound Imaging.","authors":"Xiaodong Yang, Xin Yan, Lingling Jing, Yingqi Wang, Xinwei Su","doi":"10.1177/01617346261445936","DOIUrl":"https://doi.org/10.1177/01617346261445936","url":null,"abstract":"<p><p>Poor spatial resolution and contrast remain major challenges in ultrafast ultrasound imaging. Null subtraction imaging (NSI) improves lateral resolution but often degrades speckle quality and contrast. Its extension, dynamic DC-biased NSI (dDC-NSI), mitigates this trade-off by introducing a dynamic DC bias; however, slight speckle suppression may still occur in homogeneous regions, leading to dark-region artifacts. In this work, a generalized null subtraction factor (gNSF) is proposed as a post-processing framework. gNSF applies multiple apodizations, followed by a mirror-flipping and symmetric summation operation, and defines a weighting factor based on the energy ratio between a bias term and a zero-mean sequence. By incorporating the dynamic DC bias, coherent echoes are enhanced while incoherent noise is suppressed. Phantom experiments show that gNSF achieves a contrast performance (gCNR close to 1) comparable to GCF and dDC-NSI, and superior to DAS and NSI. In addition, gNSF improves CR and sSNR by 20% and 21% compared with dDC-NSI, indicating reduced speckle over-suppression and a better balance between contrast and speckle preservation.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"1617346261445936"},"PeriodicalIF":2.5,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147845109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}