Ultrasonic Imaging最新文献

{"title":"Complex-Valued Spatio-Temporal Graph Convolution Neural Network optimized With Giraffe Kicking Optimization Algorithm for Thyroid Nodule Classification in Ultrasound Images.","authors":"Kavin Kumar K, Rayavel P, Nithya M, Divyedharshini G","doi":"10.1177/01617346251362167","DOIUrl":"10.1177/01617346251362167","url":null,"abstract":"<p><p>Thyroid hormones are significant for controlling metabolism, and two common thyroid disorders, such as hypothyroidism. The hyperthyroidism are directly affect the metabolic rate of the human body. Predicting and diagnosing thyroid disease remain significant challenges in medical research due to the complexity of thyroid hormone regulation and its impact on metabolism. Therefore, this paper proposes a Complex-valued Spatio-Temporal Graph Convolution Neural Network optimized with Giraffe Kicking Optimization Algorithm for Thyroid Nodule Classification in Ultrasound Images (CSGCNN-GKOA-TNC-UI). Here, the ultrasound images are collected through DDTI (Digital Database of Thyroid ultrasound Imageries) dataset. The gathered data is given into the pre-processing stage using Bilinear Double-Order Filter (BDOF) approach to eradicate the noise and increase the input images quality. The pre-processing image is given into the Deep Adaptive Fuzzy Clustering (DAFC) for Region of Interest (RoI) segmentation. The segmented image is fed to the Multi-Objective Matched Synchro Squeezing Chirplet Transform (MMSSCT) for extracting the features, like Geometric features and Morphological features. The extracted features are fed into the CSGCNN, which classifies the Thyroid Nodule into Benign Nodules and Malign Nodules. Finally, Giraffe Kicking Optimization Algorithm (GKOA) is considered to enhance the CSGCNN classifier. The CSGCNN-GKOA-TNC-UI algorithm is implemented in MATLAB. The CSGCNN-GKOA-TNC-UI approach attains 34.9%, 21.5% and 26.8% higher f-score, 18.6%, 29.3 and 19.2% higher accuracy when compared with existing models: Thyroid diagnosis with classification utilizing DNN depending on hybrid meta-heuristic with LSTM method (LSTM-TNC-UI), innovative full-scale connected network for segmenting thyroid nodule in UI (FCG Net-TNC-UI), and Adversarial architecture dependent multi-scale fusion method for segmenting thyroid nodule (AMSeg-TNC-UI) methods respectively. The proposed model enhances thyroid nodule classification accuracy, aiding radiologists and endocrinologists. By reducing misclassification, it minimizes unnecessary biopsies and enables early malignancy detection.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"243-255"},"PeriodicalIF":2.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144976420","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":"Ultrasound Phase Aberrated Point Spread Function Estimation with Convolutional Neural Network: Simulation Study.","authors":"Wei-Hsiang Shen, Yu-An Lin, Meng-Lin Li","doi":"10.1177/01617346251352435","DOIUrl":"10.1177/01617346251352435","url":null,"abstract":"<p><p>Ultrasound imaging systems rely on accurate point spread function (PSF) estimation to support advanced image quality enhancement techniques such as deconvolution and speckle reduction. Phase aberration, caused by sound speed inhomogeneity within biological tissue, is inevitable in ultrasound imaging. It distorts the PSF by increasing sidelobe level and introducing asymmetric amplitude, making PSF estimation under phase aberration highly challenging. In this work, we propose a deep learning framework for estimating phase-aberrated PSFs using U-Net and complex U-Net architectures, operating on RF and complex k-space data, respectively, with the latter demonstrating superior performance. Synthetic phase aberration data, generated using the near-field phase screen model, is employed to train the networks. We evaluate various loss functions and find that log-compressed B-mode perceptual loss achieves the best performance, accurately predicting both the mainlobe and near sidelobe regions of the PSF. Simulation results validate the effectiveness of our approach in estimating PSFs under varying levels of phase aberration. Furthermore, we demonstrate that more accurate PSF estimation improves performance in a downstream phase aberration correction task, highlighting the broader utility of the proposed method.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"232-242"},"PeriodicalIF":2.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144838386","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":"Regularized Joint Estimator of the Nonlinearity Parameter and Attenuation Coefficient Using a Nonlinear Least-Squares Algorithm.","authors":"Sebastian Merino, Adriana Romero, Roberto Lavarello, Andres Coila","doi":"10.1177/01617346251362389","DOIUrl":"10.1177/01617346251362389","url":null,"abstract":"<p><p>The acoustic nonlinearity parameter (B/A) could enhance the diagnostic capabilities of conventional ultrasonography and quantitative ultrasound in tissues and diseases. Nonlinear acoustic propagation theory of plane waves has been used to develop a dual-energy model of the depletion of the fundamental related to the Gol'dberg number and subsequently to the B/A of media (a reference phantom is used as a baseline). The depletion method, however, needs a priori information of the attenuation coefficient (AC) of the assessed media. For this reason, recently, a work introduced a simultaneous estimator of the B/A and AC based on fitting depletion method measurements to a nonlinear model using the iterative algorithm Gauss-Newton Levenberg-Marquardt (GNLM). However, the GNLM method presented high sensitivity to the initial guess values of the algorithm which limits the robustness of the approach. In the present work, the Gauss-Newton method is combined with a total variation regularization approach (GNTV), which is achievable by expanding the nonlinear model of the GNLM method for joint estimation of the B/A and AC of all pixels of the parametric images instead of a block-wise approach. In addition, the GNTV used compounding data from several tone-burst transmissions at different center frequencies rather than only one narrowband tone-burst. The results suggest that incorporating regularization and increasing the number of frequencies improves the robustness of the GNTV compared to the GNLM method by accurately estimating B/A values in uniform and nonuniform experimental phantoms (mean relative error less than 18%). The best performance of B/A reconstruction was observed when the sample medium exhibited a constant Gol'dberg number.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"270-282"},"PeriodicalIF":2.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145030775","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":"Differentiation Between Fibro-Adipose Vascular Anomaly and Intramuscular Venous Malformation Using Grey-Scale Ultrasound-Based Radiomics and Machine Learning.","authors":"Wen-Jia Hu, Gang Wu, Jian-Jun Yuan, Bing-Xin Ma, Yu-Han Liu, Xiao-Nan Guo, Chang-Xian Dong, Hong Kang, Xiao Yang, Jian-Chu Li","doi":"10.1177/01617346251342608","DOIUrl":"10.1177/01617346251342608","url":null,"abstract":"<p><p>To establish an ultrasound-based radiomics model to differentiate fibro adipose vascular anomaly (FAVA) and intramuscular venous malformation (VM). The clinical data of 65 patients with VM and 31 patients with FAVA who were treated and pathologically confirmed were retrospectively analyzed. Dimensionality reduction was performed on these features using the least absolute shrinkage and selection operator (LASSO). An ultrasound-based radiomics model was established using support vector machine (SVM) and random forest (RF) models. The diagnostic efficiency of this model was evaluated using the receiver operating characteristic. A total of 851 features were obtained by feature extraction, and 311 features were screened out using the <i>t</i>-test and Mann-Whitney <i>U</i> test. The dimensionality reduction was performed on the remaining features using LASSO. Finally, seven features were included to establish the diagnostic prediction model. In the testing group, the AUC, accuracy and specificity of the SVM model were higher than those of the RF model (0.841 [0.815-0.867] vs. 0.791 [0.759-0.824], 96.6% vs. 93.1%, and 100.0% vs. 90.5%, respectively). However, the sensitivity of the SVM model was lower than that of the RF model (88.9% vs. 100.0%). In this study, a prediction model based on ultrasound radiomics was developed to distinguish FAVA from VM. The study achieved high classification accuracy, sensitivity, and specificity. SVM model is superior to RF model and provides a new perspective and tool for clinical diagnosis.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"223-231"},"PeriodicalIF":2.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144838385","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":"Experimental Assessment of Conventional Features, CNN-Based Features and Ensemble Schemes for Discriminating Benign Versus Malignant Lesions on Breast Ultrasound Images.","authors":"Francesco Bianconi, Muhammad Usama Khan, Hongbo Du, Sabah Jassim","doi":"10.1177/01617346251362168","DOIUrl":"10.1177/01617346251362168","url":null,"abstract":"<p><p>Breast ultrasound images play a pivotal role in assessing the nature of suspicious breast lesions, particularly in patients with dense tissue. Computerized analysis of breast ultrasound images has the potential to assist the physician in the clinical decision-making and improve subjective interpretation. We assess the performance of conventional features, deep learning features and ensemble schemes for discriminating benign versus malignant breast lesions on ultrasound images. A total of 19 individual feature sets (1 morphological, 2 first-order, 10 texture-based, and 6 CNN-based) were included in the analysis. Furthermore, four combined feature sets (Best by class; Top 3, 5, and 7) and four fusion schemes (feature concatenation, majority voting, sum and product rule) were considered to generate ensemble models. The experiments were carried out on three independent open-access datasets respectively containing 252 (154 benign, 98 malignant), 232 (109 benign, 123 malignant), and 281 (187 benign, 94 malignant) lesions. CNN-based features outperformed the other individual descriptors achieving levels of accuracy between 77.4% and 83.6%, followed by morphological features (71.6%-80.8%) and histograms of oriented gradients (71.4%-77.6%). Ensemble models further improved the accuracy to 80.2% to 87.5%. Fusion schemes based on product and sum rule were generally superior to feature concatenation and majority voting. Combining individual feature sets by ensemble schemes demonstrates advantages for discriminating benign versus malignant breast lesions on ultrasound images.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"256-269"},"PeriodicalIF":2.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144976391","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":"Predicting Central Lymph Node Metastasis in Papillary Thyroid Carcinoma: Integration of Two-Dimensional Ultrasound Radiomics with Clinical Features.","authors":"Jihe Fu, Zhan Wang, Heng Zhang, Xiaoqin Li, Xinye Ni, Chao Zhang, Tong Zhao","doi":"10.1177/01617346251377985","DOIUrl":"https://doi.org/10.1177/01617346251377985","url":null,"abstract":"<p><p>To evaluate the ability of two-dimensional ultrasound radiomics, integrated with clinical features, to predict central lymph node metastasis (CLNM) in papillary thyroid carcinoma (PTC). We conducted a retrospective study of PTC patients treated at the Second People's Hospital of Changzhou from January 2018 to February 2023. A total of 725 eligible patients were randomly allocated to training and test cohorts in a 7:3 ratio. Radiomic features were extracted from the PTC primary nodal region region on two-dimensional ultrasound images. Dimensionality reduction was performed using Mann-Whitney <i>U</i> tests, Spearman correlation analysis, and least absolute shrinkage and selection operator regression, yielding a radiomics signature (Rad-score). Seven machine-learning algorithms-logistic regression, support vector machine, k-nearest neighbors, decision tree, random forest, light gradient boosting machine, and gaussian naïve bayes-were compared to identify the optimal classifier. A joint predictive model was then constructed by integrating the Rad-score with clinically significant variables identified by univariate and multivariate logistic regression, and implemented using the optimal machine-learning classifier. Model performance was comprehensively evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. Among the seven algorithms, gaussian naïve bayes achieved the highest predictive performance. Univariate and multivariate logistic regression revealed that sex, age, and tumor aspect ratio were independent predictors of CLNM. These variables were integrated with the Rad-score to yield a joint model that achieved AUCs of 0.840 (95% CI, 0.806-0.873) and 0.811 (95% CI, 0.746-0.866) in the training and test cohorts, respectively. Calibration curves and decision curve analysis indicated that the joint model was well-calibrated and afforded favorable clinical utility. The joint model integrating two-dimensional ultrasound radiomics with clinical features enables effective preoperative prediction of CLNM in PTC.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"1617346251377985"},"PeriodicalIF":2.5,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145226220","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":"Time Domain Measure of Transient Shear Wave Attenuation.","authors":"Hamidreza Asemani, Zaegyoo Hah, Kyungsook Shin, Jeongeun Lee, Kevin J Parker","doi":"10.1177/01617346251367763","DOIUrl":"https://doi.org/10.1177/01617346251367763","url":null,"abstract":"<p><p>Transient shear waves from push pulses can be used in elastography to estimate shear wave speed and attenuation, as a step towards the viscoelastic characterization of tissue. While many implementations are in use, less attention has been paid to practical issues of the strong influence of the inevitable background motions of tissue and transducer, the limited time and sampling available, and the deleterious effects of these on spectral estimates. To mitigate these issues, we propose several physics-based steps, first to correct for baseline drift and second to eliminate the need for Fourier transforms by completing all estimations on time domain energy. We target the estimation of shear wave attenuation, and preliminary results are shown for two phantoms and two <i>in vivo</i> livers to demonstrate the potential of this approach, which can serve as an alternative pathway towards shear wave attenuation of tissues for clinical assessment of tissue elastography.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"1617346251367763"},"PeriodicalIF":2.5,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145041726","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":"Elastic Modulus Imaging for Breast Application Using a Virtual Fields Based-Method in Quasi-Static Ultrasound Elastography.","authors":"Anne-Lise Duroy, Olivier Basset, Elisabeth Brusseau","doi":"10.1177/01617346251342609","DOIUrl":"10.1177/01617346251342609","url":null,"abstract":"<p><p>Nowadays, detection and characterization of breast pathologies is an essential issue. Quasi-static ultrasound elastography have been proposed to provide information about the mechanical properties of tissues during the patient examination. However, reconstructing tissue properties is a challenging task as it requires to solve an ill-posed inverse problem, with generally no available boundary information and solely 2D estimated displacements, whereas the problem is inherently three-dimensional. In this paper, a Virtual fields based-method is investigated to reconstruct Young's modulus maps from the knowledge of internal displacements and the force applied. The media examined are assumed to be linear elastic and isotropic, and to overcome the lack of 3D data, the plane stress conditions are considered. The developed method is assessed with plane-stress and 3D simulations, as well as phantoms and patient data. For all the media examined, the reconstructed Young's modulus maps clearly reveal regions with different stiffnesses. The stiffness contrast between regions is accurately estimated for the different plane stress simulations, but underestimated for the 3D simulations. These results can be expected as plane stress conditions are no longer satisfied in the 3D simulations. On the other hand, for all these cases, the size and the position of the different regions are correctly estimated when the region is larger than a pixel. Finally, similar comments can be made for the experimental results. More especially for the in vivo results, the inclusion-to-background Young's modulus ratio is estimated in average around 6.61 for the carcinoma and 4.57 for the fibroedenoma, which is consistent with the literature.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"189-201"},"PeriodicalIF":2.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700207","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":"MVKD-Trans: A Multi-View Knowledge Distillation Vision Transformer Architecture for Breast Cancer Classification Based on Ultrasound Images.","authors":"Dongchen Ling, Xiong Jiao","doi":"10.1177/01617346251346060","DOIUrl":"10.1177/01617346251346060","url":null,"abstract":"<p><p>Breast cancer is the leading cancer threatening women's health. In recent years, deep neural networks have outperformed traditional methods in terms of both accuracy and efficiency for breast cancer classification. However, most ultrasound-based breast cancer classification methods rely on single-perspective information, which may lead to higher misdiagnosis rates. In this study, we propose a multi-view knowledge distillation vision transformer architecture (MVKD-Trans) for the classification of benign and malignant breast tumors. We utilize multi-view ultrasound images of the same tumor to capture diverse features. Additionally, we employ a shuffle module for feature fusion, extracting channel and spatial dual-attention information to improve the model's representational capability. Given the limited computational capacity of ultrasound devices, we also utilize knowledge distillation (KD) techniques to compress the multi-view network into a single-view network. The results show that the accuracy, area under the ROC curve (AUC), sensitivity, specificity, precision, and F1 score of the model are 88.15%, 91.23%, 81.41%, 90.73%, 78.29%, and 79.69%, respectively. The superior performance of our approach, compared to several existing models, highlights its potential to significantly enhance the understanding and classification of breast cancer.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"171-181"},"PeriodicalIF":2.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144334278","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":"Contrast-Enhanced B-Flow Ultrasound: A Novel Approach to Liver Trauma Imaging.","authors":"Sriharsha Gummadi, Amr Mohammed, Mostafa Alnoury, Fari Fall, Tania Siu Xiao, Kaizer Contreras, Adam Maxwell, Eli Vlaisavljevich, Ji-Bin Liu, Corinne E Wessner, Flemming Forsberg, Allison Goldberg, George Koenig, John R Eisenbrey","doi":"10.1177/01617346251346922","DOIUrl":"10.1177/01617346251346922","url":null,"abstract":"<p><p>Contrast-enhanced ultrasound (CEUS) shows promise in solid organ trauma by identifying areas of disrupted perfusion. In contrast, B-Flow ultrasound offers high fidelity imaging of larger vessels. We hypothesize that contrast-enhanced B-Flow (CEB-Flow) will improve accuracy of hepatic vessel injury delineation, as an adjunct tool to CEUS and future ultrasound-guided therapies. Imaging data was collected using our IACUC approved swine model for traumatic liver injury. All procedures were approved within this IACUC protocol. Sonography was performed using a Logiq E10 scanner with C1-6 probe (GE HealthCare). After ultrasound guided liver trauma, we performed open-abdomen B-Mode ultrasound, CEUS, and CEB-Flow of the injury during infusion of Definity (Lantheus Medical Imaging, N. Billerica, MA). CEUS was performed using coded harmonic imaging and CEB-Flow using a commercial package (GE Healthcare). Twelve swine were used for analysis. Three blinded interpreters were asked to identify injured liver parenchyma and lacerated vessels. Identification rates were compared using ultrasound-guided laceration images and pathology confirmation as a reference standard. Liver injury identification ranged from 88.3% to 100% on CEUS and 50% to 66.7% on CEB-Flow. Consensus identification rates in identifying parenchymal injury were not significantly different (91.7% CEUS vs. 66.7% CEB-Flow, <i>p</i> = .25). Lacerated vessel identification ranged from 41.7% to 58.3% for CEUS and 75.0% to 91.7% for CEB-Flow. Specifically, CEB-Flow demonstrated improved consensus in identifying lacerated vasculature (41.7% CEUS vs. 91.7% CEB-Flow, <i>p</i> = .041). In this swine model study, the combination of CEUS and CEB-Flow could accurately identify and localize traumatic hepatic injury. CEB-Flow may better characterize vessel injury, which in turn may direct and improve bedside management.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"182-188"},"PeriodicalIF":2.5,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555534","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}