Current Medical Imaging Reviews最新文献

{"title":"Empirical Curvelet-ridgelet Wavelet Transform for Multimodal Fusion of Brain Images.","authors":"Anupama Jamwal, Shruti Jain","doi":"10.2174/0115734056269529231205101519","DOIUrl":"https://doi.org/10.2174/0115734056269529231205101519","url":null,"abstract":"<p><strong>Background: </strong>Empirical curvelet and ridgelet image fusion is an emerging technique in the field of image processing that aims to combine the benefits of both transforms.</p><p><strong>Objective: </strong>The proposed method begins by decomposing the input images into curvelet and ridgelet coefficients using respective transform algorithms for Computerized Tomography (CT) and magnetic Resonance Imaging (MR) brain images.</p><p><strong>Methods: </strong>An empirical coefficient selection strategy is then employed to identify the most significant coefficients from both domains based on their magnitude and directionality. These selected coefficients are coalesced using a fusion rule to generate a fused coefficient map. To reconstruct the image, an inverse curvelet and ridgelet transform was applied to the fused coefficient map, resulting in a high-resolution fused image that incorporates the salient features from both input images.</p><p><strong>Results: </strong>The experimental outcomes on real-world datasets show how the suggested strategy preserves crucial information, improves image quality, and outperforms more conventional fusion techniques. For CT Ridgelet-MR Curvelet and CT Curvelet-MR Ridgelet, the authors' maximum PSNRs were 58.97 dB and 55.03 dB, respectively. Other datasets are compared with the suggested methodology.</p><p><strong>Conclusion: </strong>The proposed method's ability to capture fine details, handle complex geometries, and provide an improved trade-off between spatial and spectral information makes it a valuable tool for image fusion tasks.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572125","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":"Fusion of Multimodal Medical Images Based on Fine-Grained Saliency and Anisotropic Diffusion Filter.","authors":"Harmanpreet Kaur, Renu Vig, Naresh Kumar, Apoorav Sharma, Ayush Dogra, Bhawna Goyal","doi":"10.2174/0115734056269626231201042100","DOIUrl":"https://doi.org/10.2174/0115734056269626231201042100","url":null,"abstract":"<p><strong>Background: </strong>A clinical medical image provides vital information about a person's health and bodily condition. Typically, doctors monitor and examine several types of medical images individually to gather supplementary information for illness diagnosis and treatment. As it is arduous to analyze and diagnose from a single image, multi-modality images have been shown to enhance the precision of diagnosis and evaluation of medical conditions.</p><p><strong>Objective: </strong>Several conventional image fusion techniques strengthen the consistency of the information by combining varied image observations; nevertheless, the drawback of these techniques in retaining all crucial elements of the original images can have a negative impact on the accuracy of clinical diagnoses. This research develops an improved image fusion technique based on fine-grained saliency and an anisotropic diffusion filter to preserve structural and detailed information of the individual image.</p><p><strong>Method: </strong>In contrast to prior efforts, the saliency method is not executed using a pyramidal decomposition, but rather an integral image on the original scale is used to obtain features of superior quality. Furthermore, an anisotropic diffusion filter is utilized for the decomposition of the original source images into a base layer and a detail layer. The proposed algorithm's performance is then contrasted to those of cutting-edge image fusion algorithms.</p><p><strong>Results: </strong>The proposed approach cannot only cope with the fusion of medical images well, both subjectively and objectively, according to the results obtained, but also has high computational efficiency.</p><p><strong>Conclusion: </strong>Furthermore, it provides a roadmap for the direction of future research.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572127","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":"Investigation of Medical Image Technology Based on Big Data Neuroscience in Exercise Rehabilitation.","authors":"Shuhua Zhang, Jijin Sun","doi":"10.2174/0115734056271972240111094235","DOIUrl":"https://doi.org/10.2174/0115734056271972240111094235","url":null,"abstract":"<p><strong>Purpose: </strong>The purpose of this article is to combine the functional information of CT images with the anatomical and soft tissue information of MRI through image fusion technology, providing more detailed information for rehabilitation treatment and thus providing a scientific basis for clinical applications and better training plans.</p><p><strong>Methods: </strong>In this paper, functional brain imaging technology combining CT (computed tomography) and MRI (magnetic resonance imaging) was used for image fusion, and SURF (accelerated robust feature) feature points of images were extracted. In this study, 40 patients with mild and moderate closed traumatic brain injury admitted to the rehabilitation department of a rehabilitation center from 2018 to 2022 were selected as the research objects.</p><p><strong>Results: </strong>Compared with using only CT images and MRI images for brain injury diagnosis, the fusion image had a higher detection rate of abnormal brain injury diagnosis, with a detection rate of 97.5%. When using fused images for the diagnosis of abnormal brain injury, the patient's exercise rehabilitation effect was better.</p><p><strong>Conclusion: </strong>CT and MRI image fusion technology had a high diagnostic accuracy for brain injury, which could timely guide doctors in determining exercise rehabilitation plans and help improve the effectiveness of patient exercise rehabilitation.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572132","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":"Automated Diagnosis of Bone Metastasis by Classifying Bone Scintigrams Using a Self-defined Deep Learning Model.","authors":"Yubo Wang, Qiang Lin, Shaofang Zhao, Xianwu Zeng, Bowen Zheng, Yongchun Cao, Zhengxing Man","doi":"10.2174/0115734056281578231212104108","DOIUrl":"https://doi.org/10.2174/0115734056281578231212104108","url":null,"abstract":"<p><strong>Background: </strong>Patients with cancer can develop bone metastasis when a solid tumor invades the bone, which is the third most commonly affected site by metastatic cancer, after the lung and liver. The early detection of bone metastases is crucial for making appropriate treatment decisions and increasing survival rates. Deep learning, a mainstream branch of machine learning, has rapidly become an effective approach to analyzing medical images.</p><p><strong>Objective: </strong>To automatically diagnose bone metastasis with bone scintigraphy, in this work, we proposed to cast the bone metastasis diagnosis problem into automated image classification by developing a deep learning-based automated classification model.</p><p><strong>Methods: </strong>A self-defined convolutional neural network consisting of a feature extraction sub-network and feature classification sub-network was proposed to automatically detect lung cancer bone metastasis, with a feature extraction sub-network extracting hierarchal features from SPECT bone scintigrams and feature classification sub-network classifying high-level features into two categories (i.e., images with metastasis and without metastasis).</p><p><strong>Results: </strong>Using clinical data of SPECT bone scintigrams, the proposed model was evaluated to examine its detection accuracy. The best performance was achieved if the two images (i.e., anterior and posterior scans) acquired from each patient were fused using pixel-wise addition operation on the bladder-excluded images, obtaining the best scores of 0.8038, 0.8051, 0.8039, 0.8039, 0.8036, and 0.8489 for accuracy, precision, recall, specificity, F-1 score, and AUC value, respectively.</p><p><strong>Conclusion: </strong>The proposed two-class classification network can predict whether an image contains lung cancer bone metastasis with the best performance as compared to existing classical deep learning models. The high accumulation of ^99mTc MDP in the urinary bladder has a negative impact on automated diagnosis of bone metastasis. It is recommended to remove the urinary bladder before automated analysis.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139521114","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":"Machine Learning in Magnetic Resonance Images of Glioblastoma: A Review.","authors":"Georgina Waldo-Benítez, Luis Carlos Padierna, Pablo Cerón, Modesto A Sosa","doi":"10.2174/0115734056265212231122102029","DOIUrl":"https://doi.org/10.2174/0115734056265212231122102029","url":null,"abstract":"<p><strong>Background: </strong>The purpose of this work was to identify which Glioblastoma (GBM) problems can be handled by Magnetic Resonance Imaging (MRI) and Machine Learning (ML) techniques. Results, limitations, and trends through a review of the scientific literature in the last 5 years were performed. Google Scholar, PubMed, Elsevier databases, and forward and backward citations were used for searching articles applying ML techniques in GBM. The 50 most relevant papers fulfilling the selection criteria were deeply analyzed. The PRISMA statement was followed to structure our report.</p><p><strong>Methods: </strong>A partial taxonomy of the GBM problems tackled with ML methods was formulated with 15 subcategories grouped into four categories: extraction of characteristics from tumoral regions, differentiation, characterization, and problems based on genetics.</p><p><strong>Results: </strong>The dominant techniques in solving these problems are: Radiomics for feature extraction, Least Absolute Shrinkage and Selection Operator for feature selection, Support Vector Machines and Random Forest for classification, and Convolutional Neural Networks for characterization. A noticeable trend is that the application of Deep Learning on GBM problems is growing exponentially. The main limitations of ML methods are their interpretability and generalization.</p><p><strong>Conclusion: </strong>The diagnosis, treatment, and characterization of GBM have advanced with the aid of ML methods and MRI data, and this improvement is expected to continue. ML methods are effective in solving GBM-related problems with different precisions, Overall Survival being the hardest problem to solve with accuracies ranging from 57%-71%, and GBM differentiation the one with the highest accuracy ranging from 80%-97%.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139521271","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":"Structured Reporting of Computed Tomography Enterography in Crohn's Disease.","authors":"Hui Zhu, Suying Chen, Jinghao Chen, Jushun Yang, Ruochen Cong, Jinjie Sun, Yachun Xu, Bosheng He","doi":"10.2174/0115734056258848240101055747","DOIUrl":"https://doi.org/10.2174/0115734056258848240101055747","url":null,"abstract":"<p><strong>Background: </strong>To compare the integrity, clarity, conciseness, etc., of the structured report (SR) versus free-text report (FTR) for computed tomography enterography of Crohn's disease (CD).</p><p><strong>Methods: </strong>FTRs and SRs were generated for 30 patients with CD. The integrity, clarity, conciseness etc., of SRs versus FTRs, were compared. In this study, an evidence-based medicine practice model was utilized on 92 CD patients based on SR in order to evaluate its clinical value. Then, the life quality of the patients in two groups was evaluated before and after three months of intervention using an Inflammatory Bowel Disease Questionnaire (IBDQ).</p><p><strong>Results: </strong>SRs received higher ratings for satisfaction with integrity (median rating 4.27 vs. 3.75, P=0.008), clarity (median rating 4.20 vs. 3.43, P=0.003), conciseness (median rating 4.23 vs. 3.20, P=0.003), the possibility of contacting a radiologist to interpret (median rating 4.17 vs. 3.20, P<0.001), and overall clinical impact (median rating 4.23 vs. 3.27, P<0.001) than FTRs. Besides, research group had higher score of IBDQ intestinal symptom dimension (median score 61.13 vs. 58.02, P=0.003), IBDQ systemic symptom dimension (median score 24.48 vs. 20.67, P<0.001), IBDQ emotional capacity dimension (median score 65.65 vs. 61.74, P<0.001), IBDQ social ability dimension (median score 26.80 vs. 22.37, P<0.001), and total IBDQ score (median score 178.07 vs. 162.80, P<0.001) than control group.</p><p><strong>Conclusion: </strong>The SR of CTE in CD patients was conducive to improving the quality and readability of the report, and CD patients' life quality could significantly improve after the intervention of an evidence-based medicine model based on SR.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139681936","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 Retrospective Analysis of the Computed Tomography Findings and Diagnosis of 53 Cases of Elastofibroma in the Infrascapular Region.","authors":"Jian-Wu Wang, Ru-Chen Peng","doi":"10.2174/0115734056252284231210190622","DOIUrl":"https://doi.org/10.2174/0115734056252284231210190622","url":null,"abstract":"<p><strong>Objective: </strong>In this work, we have used histopathology as the gold standard for the diagnosis, calculated the sensitivity and positive predictive value (PPV) of computed tomography (CT), and analyzed the CT and clinical characteristics of pathologically proven elastofibromas.</p><p><strong>Methods: </strong>A systematic retrospective analysis was performed on all patients with infrascapular lesions who were treated in the hospital from 2006 to 2018. CT and histopathological examinations were performed for all cases, and the CT sensitivity and PPV for the diagnosis of elastofibroma were calculated. 12 of 53 cases (20 lesions) underwent enhanced CT scan after CT plain scan, and the related clinical and CT features of elastofibromas have been discussed.</p><p><strong>Results: </strong>Of the 54 patients treated during the study, CT diagnosis was consistent with histopathology in 53 cases. One was a false-positive patient. The PPV and sensitivity of the CT in the diagnosis of elastofibroma were 93.3% (95% CI 68.0%-99.8%) and 100%, respectively. The CT values of 12 patients with 20 lesions on plain and enhanced scans were statistically significant (P=0.001). The prevalence of elastofibromas in males and females was statistically significant (P=.000). There was no statistically significant difference in the incidence of left and right elastofibromas (P=0.752). There was no significant difference in the volume of left and right lesions (P=0.209) and the volume of elastofibromas between males and females (P=.474).</p><p><strong>Conclusion: </strong>CT is the most practical tool for the evaluation of elastofibromas in the infrascapular region.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139418538","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":"Clinical Implementation of Dual-Energy CT Technical for Hepatobiliary Imaging.","authors":"Tian Li, Hao Xiong, Guang-Hai Ji, Xiao-Han Zhang, Jie Peng, Bo Li","doi":"10.2174/0115734056275595231208075930","DOIUrl":"https://doi.org/10.2174/0115734056275595231208075930","url":null,"abstract":"<p><p>Dual-energy computed tomography (DECT) applies two energy spectra distributions to collect raw data based on traditional CT imaging. The application of hepatobiliary imaging, has the advantages of optimizing the scanning scheme, improving the imaging quality, highlighting the disease characterization, and increasing the detection rate of lesions. In order to summarize the clinical application value of DECT in hepatobiliary diseases, we searched the technical principles of DECT and its existing studies, case reports, and clinical guidelines in hepatobiliary imaging from 2010 to 2023 (especially in the past 5 years) through PubMed and CNKI, focusing on the clinical application of DECT in hepatobiliary diseases, including liver tumors, diffuse liver lesions, and biliary system lesions. The first part of this article briefly describes the basic concept and technical advantages of DECT. The following will be reviewed:the detection of lesions, diagnosis and differential diagnosis of lesions, hepatic steatosis, quantitative analysis of liver iron, and analyze the advantages and disadvantages of DECT in hepatobiliary imaging. Finally, the contents of this paper are summarized and the development prospect of DECT in hepatobiliary imaging is prospected.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139099229","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":"Modular Edge Analysis Reveals Chemotherapy-induced Brain Network Changes in Lung Cancer Patients.","authors":"Jia You, Zhengqian Wang, Lanyue Hu, Yujie Zhang, Feifei Chen, Xindao Yin, Yu-Chen Chen, Xiaomin Yong","doi":"10.2174/0115734056277364231226081249","DOIUrl":"https://doi.org/10.2174/0115734056277364231226081249","url":null,"abstract":"<p><strong>Background: </strong>Lung cancer patients with post-chemotherapy may have disconnected or weakened function connections within brain networks.</p><p><strong>Objective: </strong>This study aimed to explore the abnormality of brain functional networks in lung cancer patients with post-chemotherapy by modular edge analysis.</p><p><strong>Methods: </strong>Resting-state functional magnetic resonance imaging (rs-fMRI) scans were performed on 40 patients after chemotherapy, 40 patients before chemotherapy and 40 normal controls. Patients in all three groups were age and sex well-matched. Then, modular edge analysis was applied to assess brain functional network alterations.</p><p><strong>Results: </strong>Post-chemotherapy patients had the worst MoCA scores among the three groups (p < 0.001). In intra-modular connections, compared with normal controls, the patients after chemotherapy had decreased connection strengths in the occipital lobe module (p < 0.05). Compared with the nonchemotherapy group, the patients after chemotherapy had decreased connection strengths in the subcortical module (p < 0.05). In inter-modular connections, compared with normal controls, the patients after chemotherapy had decreased connection strength in the frontal-temporal lobe modules (p < 0.05). Compared with the non-chemotherapy group, the patients after chemotherapy had decreased connection strength in the subcortical-temporal lobe modules (p < 0.05).</p><p><strong>Conclusion: </strong>The results reveal that chemotherapy can disrupt connections in brain functional networks. As far as we know, the use of modular edge analysis to report changes in brain functional brain networks associated with chemotherapy was rarely reported. Modular edge analysis may play a crucial part in predicting the clinical outcome for the patients after chemotherapy.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139681935","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":"Super-resolution based Nodule Localization in Thyroid Ultrasound Images through Deep Learning.","authors":"Jing Li, Qiang Guo, Shiyi Peng, Xingli Tan","doi":"10.2174/0115734056269264240408080443","DOIUrl":"10.2174/0115734056269264240408080443","url":null,"abstract":"<p><strong>Background: </strong>Currently, it is difficult to find a solution to the inverse inappropriate problem, which involves restoring a high-resolution image from a lowresolution image contained within a single image. In nature photography, one can capture a wide variety of objects and textures, each with its own characteristics, most notably the high-frequency component. These qualities can be distinguished from each other by looking at the pictures.</p><p><strong>Objective: </strong>The goal is to develop an automated approach to identify thyroid nodules on ultrasound images. The aim of this research is to accurately differentiate thyroid nodules using Deep Learning Technique and to evaluate the effectiveness of different localization techniques.</p><p><strong>Methods: </strong>The method used in this research is to reconstruct a single super-resolution image based on segmentation and classification. The poor-quality ultrasound image is divided into several parts, and the best applicable classification is chosen for each component. Pairs of high- and lowresolution images belonging to the same class are found and used to figure out which image is high-resolution for each segment. Deep learning technology, specifically the Adam classifier, is used to identify carcinoid tumors within thyroid nodules. Measures, such as localization accuracy, sensitivity, specificity, dice loss, ROC, and area under the curve (AUC), are used to evaluate the effectiveness of the techniques.</p><p><strong>Results: </strong>The results of the proposed method are superior, both statistically and qualitatively, compared to other methods that are considered one of the latest and best technologies. The developed automated approach shows promising results in accurately identifying thyroid nodules on ultrasound images.</p><p><strong>Conclusion: </strong>The research demonstrates the development of an automated approach to identify thyroid nodules within ultrasound images using super-resolution single-image reconstruction and deep learning technology. The results indicate that the proposed method is superior to the latest and best techniques in terms of accuracy and quality. This research contributes to the advancement of medical imaging and holds the potential to improve the diagnosis and treatment of thyroid nodules.</p>.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141064764","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}