Radiology advances最新文献

{"title":"Art of imaging: intersection of aesthetics and analysis in radiology.","authors":"Susanna I Lee, Jorge A Soto","doi":"10.1093/radadv/umaf032","DOIUrl":"https://doi.org/10.1093/radadv/umaf032","url":null,"abstract":"","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"2 5","pages":"umaf032"},"PeriodicalIF":0.0,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12488230/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The growing armamentarium of image-guided tumor ablation in interventional oncology.","authors":"Jung H Yun, Arian Mansur, Elahe Abbaspour, Seyed Sina Zakavi, Callum S Newton, David C Madoff, David Woodrum, Christos Georgiades, Paul Shyn, Aria F Olumi, Raul N Uppot, Sanjeeva Kalva, Nima Kokabi, Ripal Gandhi, Peiman Habibollahi, Nariman Nezami","doi":"10.1093/radadv/umaf033","DOIUrl":"10.1093/radadv/umaf033","url":null,"abstract":"<p><p>Minimally invasive image-guided tumor ablation techniques have been established as safe, effective methods to treat a variety of unresectable soft tissue tumors. Standard thermal ablation methods include radiofrequency ablation, microwave ablation, and cryoablation. However, newer non-thermal and/or non-invasive ablation techniques are now available as alternative options to treat soft tissue tumors, particularly those that are near critical structures or otherwise susceptible to thermal energy sink effects. The 4 types of emerging ablation techniques discussed in this review are as follows: irreversible electroporation, pulsed electric field, high-intensity focused ultrasound, and histotripsy. While the clinical trials evaluating the safety and efficacy of these ablation techniques are in their early stages, initial results are promising in the treatment of various cancers at different stages. These include potential synergistic effects when combined with chemotherapy and immunotherapy.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"2 5","pages":"umaf033"},"PeriodicalIF":0.0,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12483156/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Frozen shoulder.","authors":"Woojin Kim","doi":"10.1093/radadv/umaf008","DOIUrl":"https://doi.org/10.1093/radadv/umaf008","url":null,"abstract":"","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"2 5","pages":"umaf008"},"PeriodicalIF":0.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12429248/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Liver fat quantification using deep silicon photon-counting CT: an <i>in silico</i> imaging study.","authors":"Raj Kumar Panta, Zhye Yin, Fredrik Grönberg, Benjamin Wildman-Tobriner, Mridul Bhattarai, Ehsan Abadi, Paul Segars, Ehsan Samei","doi":"10.1093/radadv/umaf031","DOIUrl":"10.1093/radadv/umaf031","url":null,"abstract":"<p><strong>Background: </strong>Accurate liver fat quantification is essential for early diagnosis and effective management of fatty liver disease.</p><p><strong>Purpose: </strong>To investigate the potential clinical utility of a deep silicon-based photon-counting CT (dSi-PCCT), currently in development, for liver fat quantification using human models in an <i>in silico</i> imaging study.</p><p><strong>Materials and methods: </strong>dSi-PCCT is a cutting-edge photon-counting CT (GE HealthCare), with several investigational systems installed globally, used under IRB approval for imaging animals and human volunteers to support FDA clearance. We developed a dSi-PCCT simulator and benchmarked its imaging performance with respect to a prototype. We imaged a computational Gammex phantom with fat fractions (FF) ranging from 0% to 100%, along with five XCAT human models with liver FF ranging from 1% to 50%, using an abdominal CT protocol. The resulting spectral sinograms were processed using a material decomposition (MD) technique. We calculated HU-based Proton Density Fat Fraction (PDFF) from single-energy images in XCAT models and compared it against the MD-derived FF. The MD-derived FF of both datasets was assessed against the digitally defined ground truth values.</p><p><strong>Results: </strong>We observed a strong correlation (<i>R</i> ² = 0.98) between MD-derived, HU-based PDFF, and ground-truth FF in a Gammex and XCAT models. There was no statistically significant difference (<i>P</i> = .52) in FF quantification accuracy between Gammex and the XCAT human models. The root mean square errors were 4.7% for Gammex and 2.7% for XCAT. Bland-Altman analysis further confirmed good agreement between the ground truth and MD-derived FF, with differences in FF ranging from -6.9% to 7% for Gammex and -3.0% to 37.6% for XCAT.</p><p><strong>Conclusion: </strong>The results indicate that dSi-PCCT could enable accurate liver fat quantification across a wide range of FFs in multiple objects. These findings suggest that the potential utility of dSi-PCCT for accurate liver fat assessment should be explored <i>in vivo</i>.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"2 5","pages":"umaf031"},"PeriodicalIF":0.0,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12483155/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting adverse perinatal outcomes in small-for-gestational-age fetuses using MRI, ultrasound, and clinical data.","authors":"Aviad Rabinowich, Aaron Olender, Netanell Avisdris, Yair Wexler, Bossmat Yehuda, Sharon Vanetik, Jayan Khawaja, Tamir Graziani, Bar Neeman, Ayala Zilberman, Maya Yanko, Dana Schonberger, Or Rachel Sadan, Miriam Misochnik, Bella Specktor-Fadida, Daphna Link-Sourani, Jacky Herzlich, Karina Krajden Haratz, Liran Hiersch, Liat Ben Sira, Leo Joskowicz, Dafna Ben Bashat","doi":"10.1093/radadv/umaf030","DOIUrl":"10.1093/radadv/umaf030","url":null,"abstract":"<p><strong>Background: </strong>Fetal growth restriction (FGR) is associated with adverse perinatal outcomes. Existing sonographic approaches offer limited predictive accuracy. Combining fetal MRI, ultrasound and clinical data may improve perinatal prognostication.</p><p><strong>Purpose: </strong>To evaluate whether integrating prenatal MRI, ultrasound, and clinical features using machine learning (ML) improves prediction of adverse perinatal outcomes in FGR or small-for-gestational-age (SGA) pregnancies.</p><p><strong>Materials and methods: </strong>This single-center study included prospectively enrolled FGR/SGA and retrospectively included appropriate-for-gestational-age cases, with follow-up through neonatal discharge. Twenty-seven features from MRI, ultrasound, and clinical data were used in the final analysis. Seven ML classifiers were trained using stratified 5-fold cross-validation to predict composite adverse neonatal outcomes (CANO) and non-reassuring fetal status (NRFS). Sensitivity and specificity of the top-performing model (based on area under the curve [AUC]) were compared to standard biometric thresholds (estimated fetal weight and/or abdominal circumference <10th/<3rd centiles). Multiparametric, MRI-only, and ultrasound-only models were compared, along with reduced models using 4 features for CANO and 2 for NRFS.</p><p><strong>Results: </strong>One hundred thirty-one participants were included (60 FGR/SGA, 71 appropriate-for-gestational-age). The random forest method achieved the highest AUC for predicting CANO (0.912; 95% confidence interval [CI], 0.83-0.99) and NRFS (0.834; 95% CI, 0.76-0.91). For CANO, the multiparametric model demonstrated a 25% higher sensitivity (<i>P </i>= 0.005) and 17% higher specificity (<i>P </i>< 0.001) compared with the 3rd centile threshold, and improved specificity over the 10th centile threshold by 29% (<i>P </i>< 0.001). Sensitivity did not differ significantly from the 10th centile threshold (<i>P </i>= 0.366). For NRFS, specificity increased by 26% and 40% over the 3rd and 10th centile thresholds, respectively (<i>P </i>< 0.001), without significant differences in sensitivity (<i>P </i>= 1). No statistically significant differences were observed between the multiparametric, ultrasound-only, and MRI-only models (<i>P </i>≥ 0.826), or between full and reduced models (<i>P </i>≥ 0.313).</p><p><strong>Conclusions: </strong>ML-based models integrating multimodal data may improve risk stratification for predicting adverse perinatal outcomes in FGR/SGA pregnancies.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"2 5","pages":"umaf030"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12483154/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning MRI halves scan time and preserves image quality across routine neuroradiologic examinations.","authors":"Shawn K Lyo, Suyash Mohan, Michael J Hoch, Vivek P Patel, Robert M Kurtz, Alvand Hassankhani","doi":"10.1093/radadv/umaf029","DOIUrl":"10.1093/radadv/umaf029","url":null,"abstract":"<p><strong>Background: </strong>Magnetic resonance imaging (MRI) is a cornerstone of neuroimaging but is limited by lengthy acquisition times, which can lead to motion artifacts, patient discomfort, and delayed care. Deep learning reconstruction is an emerging technology that can offer image acquisition acceleration while maintaining image quality.</p><p><strong>Purpose: </strong>To compare image quality and acquisition efficiency between deep learning-accelerated vs conventional MRI (C-MRI) across a spectrum of routine neuroradiologic examinations.</p><p><strong>Materials and methods: </strong>In this single-center retrospective study, 26 patients underwent imaging with a commercially available, FDA-cleared deep learning-accelerated MRI reconstruction algorithm (Deep Resolve, Siemens Healthineers), and C-MRI on a Siemens 3 T MAGNETOM Vida scanner between October 24 and November 14, 2023. A total of 113 sequence pairs were acquired across multiple body parts (brain [<i>n</i> = 28], cervical spine [<i>n</i> = 24], thoracic spine [<i>n </i>= 16], lumbar spine [<i>n</i> = 14], internal auditory canals [<i>n</i> = 5], sella [<i>n</i> = 5], neck [<i>n</i> = 5], temporomandibular joints [<i>n</i> = 6], brachial plexus [<i>n</i> = 4], and orbits [<i>n</i> = 6]) and sequences (T2 [<i>n</i> = 38], T1 [<i>n</i> = 30], short tau inversion recovery [<i>n</i> = 21], T1 post-contrast [<i>n</i> = 17], T2 fluid attenuated inversion recovery [<i>n</i> = 5], and proton density [<i>n </i>= 2]) and evaluated by 4 neuroradiologists blinded to the acquisition method for image quality using a 5-point Likert scale. Acquisition parameters were extracted from Digital Imaging and Communications in Medicine (DICOM) metadata and statistically compared. Rater preferences and interrater reliability were assessed using nonparametric tests and intraclass correlation coefficients.</p><p><strong>Results: </strong>Deep learning reduced mean scan time by 51.6% (95% CI: 45.7%-57.7%; from 110.8 seconds to 53.7 seconds; <i>P</i> < .001). Image quality assessments using a Likert scale showed scores slightly above neutral for signal-to-noise ratio (mean 3.51; 95% CI: 3.44-3.58), structural delineation (mean 3.51, 95% CI: 3.44-3.56), and overall image quality (mean 3.56, 95% CI: 3.49-3.63). However, poor interrater reliability (intraclass correlation [ICC] range: 0.06-0.33) showed that the observed differences were not consistent, indicating functional equivalence between conventional and deep learning images.</p><p><strong>Conclusion: </strong>Deep learning MRI enabled substantial scan time reductions while maintaining image quality.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"2 5","pages":"umaf029"},"PeriodicalIF":0.0,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12483150/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The inaugural trainee editorial board of <i>Radiology Advances</i>.","authors":"Susanna I Lee","doi":"10.1093/radadv/umaf026","DOIUrl":"https://doi.org/10.1093/radadv/umaf026","url":null,"abstract":"","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"2 4","pages":"umaf026"},"PeriodicalIF":0.0,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12429200/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visual-language artificial intelligence system for knee radiograph diagnosis and interpretation: a collaborative system with humans.","authors":"Xingxin He, Zachary E Stewart, Nikitha Crasta, Varun Nukala, Albert Jang, Zhaoye Zhou, Richard Kijowski, Li Feng, Wei Peng, Rianne A van der Heijden, Kenneth S Lee, Shasha Li, Miho J Tanaka, Fang Liu","doi":"10.1093/radadv/umaf027","DOIUrl":"10.1093/radadv/umaf027","url":null,"abstract":"<p><strong>Background: </strong>Large language models (LLMs) have shown promising abilities in text-based clinical tasks but they do not inherently interpret medical images such as knee radiographs.</p><p><strong>Purpose: </strong>To develop a human-artificial intelligence interactive diagnostic approach, named radiology generative pretrained transformer (RadGPT), aimed at assisting and synergizing with human users for the interpretation of knee radiological images.</p><p><strong>Materials and methods: </strong>A total of 22 512 knee roentgen ray images and reports were retrieved from Massachusetts General Hospital; 80% of these were used for model training and 10% were used for model testing and validation, respectively. Fifteen diagnostic imaging features (eg, osteoarthritis, effusion, joint space narrowing, osteophyte) were selected to label images based on their high frequency and clinical relevance in the retrieved official reports. Area under the curve scores were calculated for each feature to assess the diagnostic performance. To evaluate the quality of the generated medical text, historical clinical reports were used as the reference text. Several metrics for text generation tasks are applied, including BiLingual Evaluation Understudy, Recall-Oriented Understudy for Gisting Evaluation, Metric for Evaluation of Translation with Explicit Ordering, and Semantic Propositional Image Caption Evaluation.</p><p><strong>Results: </strong>RadGPT, in collaboration with human users, achieved area under the curve scores ranging from 0.76 for osteonecrosis to 0.91 for arthroplasty across 15 diagnostic categories for knee conditions. Compared with the baseline LLM method, RadGPT achieved higher scores, specifically 0.18 in BiLingual Evaluation Understudy score, 0.30 in Recall-Oriented Understudy for Gisting Evaluation-L, 0.10 in Metric for Evaluation of Translation with Explicit Ordering, and 0.15 in Semantic Propositional Image Caption Evaluation, which is significantly higher than the baseline LLM method, demonstrating good linguistic overlap and clinical consistency with the reference reports.</p><p><strong>Conclusion: </strong>RadGPT has achieved advanced results in knee roentgen ray image feature recognition, illustrating the potential of LLMs in medical image interpretation. The study establishes a training protocol for developing artificial intelligence-assisted tools specifically focusing on the diagnosis and interpretation of knee radiological images.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"2 5","pages":"umaf027"},"PeriodicalIF":0.0,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12483153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contrast-enhanced CT as a non-invasive alternative for lung shunt fraction estimation in hepatic transarterial radioembolization.","authors":"Brahim Mehadji, Talia Marx, Adrianna Carter, Roger Eric Goldman, Catherine Tram Vu, Emilie Roncali","doi":"10.1093/radadv/umaf025","DOIUrl":"10.1093/radadv/umaf025","url":null,"abstract":"<p><strong>Background: </strong>Estimation of the lung shunt fraction (LSF) is an integral part of liver radioembolization treatment planning to prevent excessive lung irradiation from arterio-venous shunting in the liver. ^99mTc macro-aggregated albumin (^99mTc-MAA) nuclear imaging is the standard method. Recent literature suggests that ^99mTc-MAA nuclear imaging may be omitted in selected patient populations.</p><p><strong>Purpose: </strong>This study investigates the potential of contrast-enhanced computed tomography (CECT) as a non-invasive method for estimating LSF as an alternative for ^99mTc-MAA nuclear imaging.</p><p><strong>Materials and methods: </strong>This single-center retrospective study included 30 consecutive patients who underwent ⁹⁰Y radioembolization between January 2015 and December 2024, where both four-phase CECT and ^99mTc-MAA planar imaging were performed within one month of each other. Hypervascular tumor enhancement was identified on the CECT by subtracting the portal venous phase from the arterial phase and applying an intensity threshold. Additional perfusion characteristics were captured. Statistical analysis assessed the agreement between the CECT-derived volume ratios and the LSF values derived from ^99mTc-MAA imaging.</p><p><strong>Results: </strong>The cohort consisted of 23 male and 7 female patients with a median age of 66 years (interquartile range: 58-71), diagnosed with hepatocellular carcinoma (<i>n</i> = 24), intrahepatic cholangiocarcinoma (<i>n</i> = 2), pancreatic neuroendocrine tumors (<i>n</i> = 2), metastatic colorectal cancer (<i>n</i> = 1), and lymphocyte carcinoma (<i>n</i> = 1). Regression of the hypervascular-tumor-to-perfused volume ratio on CECT against LSF from ^99mTc-MAA imaging showed <i>R</i> ² = 0.95 (<i>P</i> < .001). In contrast, the correlation between tumor volume and LSF was <i>R</i> ² = 0.38 (<i>P</i> = .001). The root mean square error between the LSF estimated from CECT and that measured using ^99mTc-MAA planar imaging was 3%.</p><p><strong>Conclusion: </strong>Hypervascular-tumor-to-perfused volume ratio computed from CECT may offer a suitable alternative to ^99mTc-MAA nuclear imaging for LSF estimation in patients undergoing transarterial radioembolization.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"2 4","pages":"umaf025"},"PeriodicalIF":0.0,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12429231/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145246307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding large language models for radiology: strategies for fine-tuning and prompt engineering.","authors":"Sanaz Vahdati, Elham Mahmoudi, Ali Ganjizadeh, Chiehju Chao, Bradley J Erickson","doi":"10.1093/radadv/umaf024","DOIUrl":"10.1093/radadv/umaf024","url":null,"abstract":"<p><p>The advances in large language models (LLMs) have demonstrated sophisticated potential for automating complex tasks within the radiology workflow. From radiology report generation and report summarization to data collection for research trials, these models have proven to be powerful tools. However, optimal implementation of these models requires careful adaptation to the specialized medical domain. In addition, these models tend to generate information that is not truthful or factual, which can adversely affect patient care and clinical decisions. Strategies such as fine-tuning and prompt optimization have been shown to be impactful in eliminating these errors. Although these models undergo rapid updates and improvements, understanding the principles of prompt engineering and fine-tuning provides a foundation for evaluating and maintaining the performance of any LLM deployment. The current article aims to review the recent advancements in radiology using fine-tuning and prompt optimization to leverage LLMs' capabilities. It delves into various techniques within each strategy, their advantages and limitations, and presents a framework to facilitate the practical integration of LLMs into radiology settings.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"2 4","pages":"umaf024"},"PeriodicalIF":0.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12429228/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145245920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}