Journal of Computer Assisted Tomography最新文献

{"title":"Assessment of a New CT Detector and Filtration Technology: Part 1 - X-ray Beam Characterization and Radiation Dosimetry.","authors":"Izabella Barreto, Nathalie Correa, Patricia Moser, Ibrahim Tuna, Tara Massini, Lynn Rill, Manuel Arreola","doi":"10.1097/RCT.0000000000001710","DOIUrl":"10.1097/RCT.0000000000001710","url":null,"abstract":"<p><strong>Purpose: </strong>This study evaluated beam quality and radiation dosimetry of a CT scanner equipped with a novel detector and filtration technology called PureVision Optics (PVO). PVO features miniaturized electronics, a detector cut with microblade technology, and increased filtration in order to increase x-ray detection and reduce image noise.</p><p><strong>Methods: </strong>We assessed the performance of two similar 320-detector CT scanners: one equipped with PVO and one without. Beam quality was measured by determining the half-value layer (HVL) and effective energy ( Eeff ) for both scanners using all tube voltages (80 kV, 100 kV, 120 kV, 135 kV) and bowtie filters (small, medium, large) available. Energy correction factors were identified for optically stimulated luminescent dosimeters (OSLDS) compared to a calibrated ionization chamber. Surface and internal doses were measured for an anthropomorphic CT angiography head phantom and a cadaver head scanned with CTDI vol matched as close as possible at the conventional (55.1 mGy) and PVO (55.4 mGy) CT scanners.</p><p><strong>Results: </strong>For all scan settings, the PVO scanner showed significantly higher HVL (range, 4.33-11.02 mm Al) and effective energy (range, 39.4-68.0 keV) values compared to the conventional scanner (HVL, 4.19-8.25 mm Al; effective energy, 38.4-55.2 keV). For equivalent CTDI vol values, the energy-corrected surface skin and lens doses were on average 6.7% lower with the PVO scanner than the conventional scanner ( P  < 0.01).</p><p><strong>Conclusions: </strong>PVO technology yielded higher HVL and effective energies and, for the same CTDI vol , resulted in lower surface organ doses, indicating a potential for reduced patient radiation exposure in clinical settings.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"625-630"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143059119","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":"Using Computed Tomography Coronary Angiography to Differentiate Atypical Cardiac Myxoma From Thrombus.","authors":"Nannan Zhang, Chuanqiang Lan, Zeliu Du, Guihan Lin, Yi Zhong, Jingle Fei, Kan Liu, Jiansong Ji, Chenying Lu","doi":"10.1097/RCT.0000000000001708","DOIUrl":"10.1097/RCT.0000000000001708","url":null,"abstract":"<p><strong>Objective: </strong>Atypical cardiac myxoma usually presents as an isolated mass attached to the atrial septum on imaging, with no movement and a wider attachment base. It is difficult to distinguish it from cardiac thrombus through conventional echocardiography or computed tomography (CT). The purpose of this study is to evaluate the value of CT coronary angiography imaging features in distinguishing atypical cardiac myxoma from cardiac thrombus.</p><p><strong>Materials and methods: </strong>This retrospective study included patients with atypical myxoma of the heart confirmed by histopathology (n = 18) and with thrombus disappearance after anticoagulation treatment (n = 23). All patients underwent a third-generation dual-source CT coronary angiography. We compared the clinical features and CT coronary angiography image characteristics of the 2 groups and used maximum-intensity projection and multiplanar reconstruction to show neovascularization of atypical cardiac myxoma.</p><p><strong>Results: </strong>There are significant differences in the origin, surface, and enhancement patterns between atypical cardiac myxoma and thrombus ( P < 0.05, respectively). Specifically, supplied vessels were observed in the atypical cardiac myxoma group, while no neovascularization was detected in the thrombus group (83.33% vs. 0%, P < 0.001).</p><p><strong>Conclusions: </strong>Noninvasive CT coronary angiography can help distinguish atypical cardiac myxoma and cardiac thrombus through imaging features, especially by detecting the supplying vessels. However, supplementary examinations such as cardiac magnetic resonance imaging are still needed to identify different cardiac tumors.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"595-603"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143500812","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":"Deep Learning-accelerated MRI in Body and Chest.","authors":"Naveen Rajamohan, Barun Bagga, Bhavik Bansal, Luke Ginocchio, Amit Gupta, Hersh Chandarana","doi":"10.1097/RCT.0000000000001762","DOIUrl":"10.1097/RCT.0000000000001762","url":null,"abstract":"<p><p>Deep learning reconstruction (DLR) provides an elegant solution for MR acceleration while preserving image quality. This advancement is crucial for body imaging, which is frequently marred by the increased likelihood of motion-related artifacts. Multiple vendor-specific models focusing on T2, T1, and diffusion-weighted imaging have been developed for the abdomen, pelvis, and chest, with the liver and prostate being the most well-studied organ systems. Variational networks with supervised DL models, including data consistency layers and regularizers, are the most common DLR methods. The common theme for all single-center studies on this subject has been noninferior or superior image quality metrics and lesion conspicuity to conventional sequences despite significant acquisition time reduction. DLR also provides a potential for denoising, artifact reduction, increased resolution, and increased signal-noise ratio (SNR) and contrast-to-noise ratio (CNR) that can be balanced with acceleration benefits depending on the imaged organ system. Some specific challenges faced by DLR include slightly reduced lesion detection, cardiac motion-related signal loss, regional SNR variations, and variabilities in ADC measurements as reported in different organ systems. Continued investigations with large-scale multicenter prospective clinical validation of DLR to document generalizability and demonstrate noninferior diagnostic accuracy with histopathologic correlation are the need of the hour. The creation of vendor-neutral solutions, open data sharing, and diversifying training data sets are also critical to strengthening model robustness.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"531-544"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144064127","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":"Effect of Saline Sealing After CT-Guided Lung Biopsy on Pneumothorax and Hemoptysis.","authors":"Xiaoxia Zhang, Jianli An, Jingpeng Wu, Xiuxiu Jing, Hongzhi Lu, Ye Tian","doi":"10.1097/RCT.0000000000001725","DOIUrl":"10.1097/RCT.0000000000001725","url":null,"abstract":"<p><strong>Objective: </strong>To confirm that saline sealing of the needle trace after computed tomography (CT)-guided lung biopsy reduces the incidence of pneumothorax and chest tube insertion, and to observe its effects on pulmonary hemorrhage and hemoptysis.</p><p><strong>Materials and methods: </strong>Patients who underwent CT-guided lung biopsy at our hospital between January 2018 and January 2024 were included in the study. Patients were divided into 2 groups according to whether the needle trace was sealed with saline after tissue sampling. Patient baseline characteristics, lung lesion factors, procedural factors, pneumothorax rates, chest tube insertion rates, pulmonary hemorrhage rates, and hemoptysis rates were recorded.</p><p><strong>Results: </strong>The incidence of pneumothorax was 28.9% (38/132) and 15.8% (15/95) in groups A (control) and B (with sealed traces), respectively ( P =0.002). The incidence of pneumothorax requiring chest tube insertion was significantly lower in group B than in group A (1.1% vs. 6.8%; P =0.048). The incidence of pulmonary hemorrhage was similar between the 2 groups (38.6% vs. 42.1%; P =0.599). No significant difference was observed in the hemoptysis of patients in groups A and B (6.8% vs. 10.5%; P =0.320). In the binary logistic regression analysis, significant risk factors for pneumothorax included lack of saline sealing, smaller lesion size, multiple passes through the pleura, and the lateral decubitus position. Smaller lesions and longer biopsy trace lengths were independent risk factors for hemoptysis.</p><p><strong>Conclusions: </strong>Sealing the needle trace with saline significantly reduced the incidences of pneumothorax and chest tube insertion due to pneumothorax. Moreover, it did not significantly increase the incidence of pulmonary hemorrhage or hemoptysis. This technique is recommended for use in CT-guided lung biopsies.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"640-645"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143059098","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":"Resident Education in the Age of AI.","authors":"Erin Gomez, Cheng Ting Lin","doi":"10.1097/RCT.0000000000001697","DOIUrl":"10.1097/RCT.0000000000001697","url":null,"abstract":"<p><strong>Abstract: </strong>Artificial intelligence (AI) is a rapidly expanding field of interest to radiologists for its utility as an adjunct in detecting and reporting disease and its potential influence on the role of radiologists and their practices. As radiology educators, we are responsible for developing and providing access to curricular elements that will prepare residents to be good stewards of artificial intelligence resources while also acquiring fundamental knowledge and skills that are essential to daily practice. Residency programs should consider collaborative approaches as well as solicit support from national societies in the development and curation of their AI curricula.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"556-558"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780226","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":"Combining Low-energy Images in Dual-energy Spectral CT With Deep Learning Image Reconstruction Algorithm to Improve Inferior Vena Cava Image Quality.","authors":"Wei Wei, Yongjun Jia, Ming Li, Nan Yu, Shan Dang, Jian Geng, Dong Han, Yong Yu, Yunsong Zheng, Lihua Fan","doi":"10.1097/RCT.0000000000001713","DOIUrl":"10.1097/RCT.0000000000001713","url":null,"abstract":"<p><strong>Objective: </strong>To explore the application of low-energy image in dual-energy spectral CT (DEsCT) combined with deep learning image reconstruction (DLIR) to improve inferior vena cava imaging.</p><p><strong>Materials and methods: </strong>Thirty patients with inferior vena cava syndrome underwent contrast-enhanced upper abdominal CT with routine dose, and the 40, 50, 60, 70, and 80 keV images in the delayed phase were first reconstructed with the ASiR-V40% algorithm. Image quality was evaluated both quantitatively [CT value, SD, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) for inferior vena cava] and qualitatively to select an optimal energy level with the best image quality. Then, the optimal-energy images were reconstructed again using deep learning image reconstruction medium strength (DLIR-M) and DLIR-H (high strength) algorithms and compared with that of ASiR-V40%.</p><p><strong>Results: </strong>The objective CT value, SD, SNR, and CNR increased with the decrease in energy level, with statistically significant differences (all P <0.05). The 40 keV images had the highest CT values, SNR, and CNR and good diagnostic acceptability, and 40 keV was selected as the best energy level. Compared with ASiR-V40% and DLIR-M, DLIR-H had the lowest SD, highest SNR and CNR, and subjective score (all P <0.001) with good consistencies between the 2 physicians (all k ≥0.75). The 40 keV images with DLIR-H had the highest overall image quality, showing sharper edges of inferior vena cava vessels and clearer lumen in patients with Budd-Chiari syndrome.</p><p><strong>Conclusions: </strong>Compared with the ASiR-V algorithm, DLIR-H significantly reduces image noise and provides the highest CNR and best diagnostic image quality for the 40 keV DEsCT images in imaging inferior vena cava.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"604-610"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12237118/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143059123","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":"Validation of a Deep Learning Tool for Detection of Incidental Vertebral Compression Fractures.","authors":"Michelle Dai, Bryan-Clement Tiu, Jacob Schlossman, Angela Ayobi, Charlotte Castineira, Julie Kiewsky, Christophe Avare, Yasmina Chaibi, Peter Chang, Daniel Chow, Jennifer E Soun","doi":"10.1097/RCT.0000000000001726","DOIUrl":"10.1097/RCT.0000000000001726","url":null,"abstract":"<p><strong>Objective: </strong>This study evaluated the performance of a deep learning-based vertebral compression fracture (VCF) detection tool in patients with incidental VCF. The purpose of this study was to validate this tool across multiple sites and multiple vendors.</p><p><strong>Methods: </strong>This was a retrospective, multicenter, multinational blinded study using anonymized chest and abdominal CT scans performed for indications other than VCF in patients ≥50 years old. Images were obtained from 2 teleradiology companies in France and United States and were processed by CINA-VCF v1.0, a deep learning algorithm designed for VCF detection. Ground truth was established by majority consensus across 3 board-certified radiologists. Overall performance of CINA-VCF was evaluated, as well as subset analyses based on imaging acquisition parameters, baseline patient characteristics, and VCF severity. A subgroup was also analyzed and compared with available clinical radiology reports.</p><p><strong>Results: </strong>Four hundred seventy-four CT scans were included in this study, comprising 166 (35.0%) positive and 308 (65.0%) negative VCF cases. CINA-VCF demonstrated an area under the curve (AUC) of 0.97 (95% CI: 0.96-0.99), accuracy of 93.7% (95% CI: 91.1%-95.7%), sensitivity of 95.2% (95% CI: 90.7%-97.9%), and specificity of 92.9% (95% CI: 89.4%-96.5%). Subset analysis based on VCF severity resulted in a specificity of 94.2% (95% CI: 90.9%-96.6%) for grade 0 negative cases and a specificity of 64.3% (95% CI: 35.1%-87.2%) for grade 1 negative cases. For grades 2 and 3 positive cases, sensitivity was 89.7% (95% CI: 79.9%-95.8%) and 99.0% (95% CI: 94.4%-100.0%), respectively.</p><p><strong>Conclusions: </strong>CINA-VCF successfully detected incidental VCF and even outperformed clinical reports. The performance was consistent among all subgroups analyzed. Limitations of the tool included various confounding pathologies such as Schmorl's nodes and borderline cases. Despite these limitations, this study validates the applicability and generalizability of the tool in the clinical setting.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"669-674"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143059146","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":"Beyond Human Limits: The Promise and Pitfalls of Large Language Models in Radiology Research.","authors":"Chloe Reyes, Evie Nguyen, Lauren F Alexander, Rajesh Bhayana, Zoe Deahl, Ashish Khandelwal, Connor Mayes, Maria Zulfiqar, Nelly Tan","doi":"10.1097/RCT.0000000000001709","DOIUrl":"10.1097/RCT.0000000000001709","url":null,"abstract":"<p><p>This review examines the applications and challenges of large language models (LLMs), like OpenAI's ChatGPT, in radiology research. ChatGPT can assist radiology researchers in generating new ideas, finding and summarizing research papers, designing studies, analyzing data, and facilitating manuscript writing. LLMs are powerful tools with numerous applications in radiology research. However, users should be mindful of potential pitfalls, such as producing incorrect or biased outputs and inconsistent responses, along with ethical and privacy concerns. We discuss approaches to optimize models and address these issues, including prompting techniques like chain-of-thought prompting, retrieval-augmented generation, and fine-tuning. For researchers, prompt engineering can be particularly effective. This review seeks to demonstrate how researchers can utilize ChatGPT for radiology research while offering strategies to mitigate associated risks. We aim to help researchers harness these potent tools to safely boost their productivity.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"545-553"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143968765","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":"Pilot Study Examining the Use of DCE MRI With Pharmacokinetic Analysis to Evaluate Hypoxia in Prostate Cancer.","authors":"Eduardo Miguel Febronio, André de Freitas Secaf, Fernando Chahud, Jorge Elias, Rodolfo B Reis, Valdair F Muglia","doi":"10.1097/RCT.0000000000001707","DOIUrl":"10.1097/RCT.0000000000001707","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to investigate the association between tumor hypoxia, assessed through anti-HIF (hypoxia-inducible factor) staining, and aggressiveness in prostate cancer using a pharmacokinetic model, particularly those derived from the Tofts model, in predicting tumor aggressiveness.</p><p><strong>Material and methods: </strong>From January 2019 to April 2021, we conducted a retrospective search of patients with confirmed prostate cancer and a previous magnetic resonance imaging (MRI) examination. After exclusions, a total of 57 consecutive patients were enrolled. Patient data, including demographic, laboratory, and pathologic variables, were collected. MRI acquisition followed PI-RADS guidelines, encompassing T2-weighted, diffusion-weighted imaging, and dynamic contrast-enhanced imaging. An experienced abdominal radiologist conducted both morphologic and quantitative MRI analyses, evaluating parameters such as lesion size, apparent diffusion coefficient values, and the Tofts pharmacokinetics (TF) model. The histopathologic analysis included the International Society of Uropathology (ISUP) score and hypoxia marker immunohistochemistry.</p><p><strong>Results: </strong>There were no significant demographic and imaging differences between hypoxic and nonhypoxic tumors, except for elevated prostate-specific antigen levels in the latter and decreased normalized apparent diffusion coefficient in the former. Morphologic assessments revealed larger lesions in the hypoxia group. While Ktrans showed a positive association with hypoxia, it did not independently predict high-risk lesions.</p><p><strong>Conclusions: </strong>Our results suggest that pharmacokinetic analysis by the Tofts model was associated with tumors with hypoxia. However, this parameter was not an independent predictor of more aggressive tumors. Further studies, with a larger number of patients, multi-institutional and prospective, are needed to verify this possible association.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":" ","pages":"571-576"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143995910","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":"Leveraging Virtual Containers for High-Powered, Collaborative AI Research in Radiology.","authors":"Lucas Aronson, John Garrett, Andrew L Wentland","doi":"10.1097/RCT.0000000000001687","DOIUrl":"10.1097/RCT.0000000000001687","url":null,"abstract":"<p><strong>Abstract: </strong>Numerous obstacles confront radiologists interested in the use of artificial intelligence (AI) models within the field of radiology. For example, discrepancies between the radiologist's and an AI developer's hardware and software specifications pose a substantial hindrance to using AI models. Additionally, accessing and using GPU computers can lead to compatibility issues and add to these challenges. Finally, the dissemination of AI models and the ability to download preexisting AI models are not simple tasks due to the size and complexity of most programs. Virtual containers offer a solution to such compatibility issues and provide a simplified way for radiologists to use AI models. Virtual containers are software tools that bundle code, required programs, and necessary software packages to ensure that a program runs identically for all users, regardless of their computing environment. This article outlines the features of virtual containers (compatibility, versatility, and portability) and highlights an applied use case for virtual containers in the development of an AI model.</p>","PeriodicalId":15402,"journal":{"name":"Journal of Computer Assisted Tomography","volume":"49 4","pages":"559-562"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144608475","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}