Ultrasonic Imaging最新文献

{"title":"Automatic Follicle Counting From Ultrasound Images of Ovaries Using MARDSE-UNET Model.","authors":"Debasmita Saha, Ardhendu Mandal, Akhil Kumar Das, Arijit Bhattacharya","doi":"10.1177/01617346251378401","DOIUrl":"10.1177/01617346251378401","url":null,"abstract":"<p><p>Detecting ovarian structures in ultrasound images is essential in gynecological and reproductive medicine. An automated detection system can serve as a valuable tool for physicians and assist in complex ultrasound interpretations. This study presents a CNN-based object detector designed to segment and count follicle regions in ovarian ultrasound images. Automated identification of ovarian follicles can aid in diagnosing conditions such as infertility, Polycystic Ovarian Syndrome (PCOS), ovarian cancer, and other reproductive health issues. The proposed model, Multi-Attention Residual Dilated UNet with Squeeze and Excitation (MARDSE-UNet), integrates residual UNet, dilated UNet, and squeeze-and-excitation blocks to enhance follicle detection performance. MARDSE-UNet achieved exceptional results, with 98.69% accuracy, 97.89% precision, 97.7% recall, an F1-score of 86.97%, and Intersection over Union (IoU) of 95.66% in follicle detection using 5-fold cross-validation. The USOVA3D dataset was used for experimentation. The model also incorporates a novel preprocessing stage to address noise and low contrast issues, as well as a post-processing stage to refine edges and extract features such as area, perimeter, and diameter of follicles for a more comprehensive performance comparison. The proposed model outperformed traditional CNN models and other state-of-the-art methods in comparative evaluations.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"83-99"},"PeriodicalIF":2.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145356609","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":"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":"13-25"},"PeriodicalIF":2.5,"publicationDate":"2026-01-01","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":"Perfusion Assessment in CEUS Imaging for Estimating Pancreatic Cancer Response to Sonoporation-Enhanced Chemotherapy.","authors":"Omri Adler, Priscilla Machado, Trang Vu, Flemming Forsberg, Odd Helge Gilja, Dan Adam","doi":"10.1177/01617346251367758","DOIUrl":"10.1177/01617346251367758","url":null,"abstract":"<p><p>Accurate assessment of perfusion in vital organs like the pancreas is crucial for monitoring various pathologies, particularly tumors and their growth. While tumor growth inhibition usually results in decreased vascularization, current techniques for non-invasive and cost-effective perfusion assessment lack sufficient vessel separability for pancreatic applications, hindering optimal treatment selection and monitoring. Ultrasound (US) imaging offers advantages like low cost, rapid acquisition, non-invasiveness, and non-ionizing radiation. However, speckle, patient-related and acquisition-related motion artifacts, and limitations in distinguishing contrast-enhanced blood vessels, particularly in single frames, pose significant challenges. This study presents a novel solution utilizing image analysis of US contrast agents (UCAs) to characterize vascularization. The approach involves data denoising, selection of static frames, spatio-temporal registration, and deconvolution. The post-processed images are analyzed based on temporal intensity changes and normalized to extract trends. Data from 13 patients undergoing chemotherapeutic treatment with FOLFIRINOX or gemcitabine/abraxane were analyzed. Though no direct effects on vascularization are expected, the results suggest a correlation between derived vascularization trends (based on B-mode and CEUS data) and observed clinical treatment outcomes. Four patients exhibited negative slope (related to vascularization regression) aligned with clinical improvement, while six showed positive slope (related to increased vascularization) coinciding with treatment deterioration. Two patients displayed negative slopes without clinical improvement, and one patient displayed positive slope but had clinical improvement. These findings indicate the potential of this method to estimate treatment efficacy and guide personalized therapy, although the sample size is small and further investigation is warranted. <b>Trial Registry Name:</b> Sonoporation and Chemotherapy for the Treatment of Pancreatic Cancer. <b>URL:</b> https://clinicaltrials.gov/study/NCT04821284. Registration Number: NCT04821284.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"36-52"},"PeriodicalIF":2.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12618712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145356620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Nomogram for Predicting Benign and Malignant Skin Tumors Using Multimodal Ultrasound.","authors":"Weijie Liu, Xiaomeng Qu, Yumei Yan, Xiaoyu Li, Zhirou Zhang, Yanli Huang, Xiaohang Wu","doi":"10.1177/01617346251374629","DOIUrl":"https://doi.org/10.1177/01617346251374629","url":null,"abstract":"<p><p>This study aimed to investigate the diagnostic and differential value of high frequency ultrasound (HFUS), shear wave elastography (SWE), and superb microvascular imaging (SMI) for benign and malignant skin tumors, both individually and in combination. A total of 155 patients diagnosed with skin tumors via surgical treatment or puncture biopsy at the First Affiliated Hospital of Dalian Medical University were included in the study. The findings from HFUS, SWE, and SMI were recorded for each case. Pathological results served as the gold standard for comparing the differential diagnostic value of these parameters in benign and malignant skin tumors. Independent risk factors were further screened to construct a nomogram model, which was evaluated using receiver operating characteristic curves, decision curves, and calibration curves. Among the 155 patients with skin tumors, 107 were benign, and 48 were malignant. HFUS revealed significant differences in maximum diameter, internal echo, basal boundary, morphology, and blood flow grading between benign and malignant skin tumors (<i>p</i> < .05). In SWE, the maximum shear elastic modulus (E-max) of malignant skin tumors was significantly higher than that of benign tumors (<i>p</i> < .05). In SMI, the vascular index was significantly higher in the malignant group compared to the benign group (<i>p</i> < .001). Multivariate logistic regression identified boundary, maximum diameter, vascular index, and age as independent risk factors, leading to the development of a nomogram model. This model demonstrated an AUC of 0.935 (95% CI: 0.893-0.978), with a sensitivity of 85.4% and a specificity of 90.7%, indicating strong diagnostic value. HFUS, SWE, and SMI possess certain differential diagnostic capabilities for benign and malignant skin tumors. The nomogram model enhances the discrimination between benign and malignant tumors, providing precise diagnostic criteria and significant clinical relevance for the diagnosis of skin tumors.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":"48 1","pages":"26-35"},"PeriodicalIF":2.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145901446","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":"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":"3-12"},"PeriodicalIF":2.5,"publicationDate":"2026-01-01","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":"Microfabrication of a 16 MHz 1D-pMUT-Array for Photoacoustic and Ultrasound Imaging.","authors":"Atheeth Shivalingaprasad, Lakshmi Narayana Chandrashekar, Isha Munjal, Swathi Padmanabhan, Jaya Prakash, Manish Arora","doi":"10.1177/01617346251380297","DOIUrl":"10.1177/01617346251380297","url":null,"abstract":"<p><p>In this paper, we describe the complete fabrication process of a 1D piezoelectric Micromachined Ultrasound Transducer (pMUT) array operating at 16 MHz underwater. We demonstrate the applicability of this pMUT Array in medical imaging using photoacoustic imaging (PAI) and ultrasound imaging (USI) experiments. There are 16 individual pMUT devices in the array, the radius of each device is 25 microns with a pitch of 100 microns (center-to-center). A 1-micron thick AlN (Aluminum Nitride) thin film is the piezoelectric material of choice for our pMUT array. This thin film was achieved by improving upon the control parameters in RF magnetron sputtering process. The working of this pMUT was validated by performing optical, electrical, and acoustic characterization. The 1D pMUT array was characterized optically using Laser Doppler Vibrometer (LDV) wherein the pMUT membrane showcased displacement of 6.2 pm/V for the in-air measurements at resonance of 20 MHz, the resonance frequency underwater was 16.2 MHz. Electrical characteristics were obtained through lock-in amplifier measurements, these were in close match to LDV results. Acoustical characteristics of the array was obtained through imaging experiments.</p>","PeriodicalId":49401,"journal":{"name":"Ultrasonic Imaging","volume":" ","pages":"53-62"},"PeriodicalIF":2.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145402233","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":"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}