Journal of Medical Imaging最新文献

{"title":"2024 List of Reviewers.","authors":"","doi":"10.1117/1.JMI.12.1.010102","DOIUrl":"https://doi.org/10.1117/1.JMI.12.1.010102","url":null,"abstract":"<p><p>Thanks to reviewers who served the Journal of Medical Imaging in 2024.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 1","pages":"010102"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11753298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143029986","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":"Evaluation of the flying focal spot technology in a wide-angle digital breast tomosynthesis system.","authors":"Katrien Houbrechts, Nicholas Marshall, Lesley Cockmartin, Hilde Bosmans","doi":"10.1117/1.JMI.12.S1.S13009","DOIUrl":"10.1117/1.JMI.12.S1.S13009","url":null,"abstract":"<p><strong>Purpose: </strong>We characterize the flying focal spot (FFS) technology in digital breast tomosynthesis (DBT), designed to overcome source motion blurring.</p><p><strong>Approach: </strong>A wide-angle DBT system with continuous gantry and focus motion (\"uncompensated focus\") and a system with FFS were compared for image sharpness and lesion detectability. The modulation transfer function (MTF) was assessed as a function of height in the projections and reconstructed images, along with lesion detectability using the contrast detail phantom for mammography (CDMAM) and the L1 phantom.</p><p><strong>Results: </strong>For the uncompensated focus system, the spatial frequency for 25% MTF value ( <math> <mrow><msub><mi>f</mi> <mrow><mn>25</mn> <mo>%</mo></mrow> </msub> </mrow> </math> ) measured at 2, 4, and 6 cm in DBT projections fell by 35%, 49%, and 59%, respectively in the tube-travel direction compared with the FFS system. There was no significant difference in <math> <mrow><msub><mi>f</mi> <mrow><mn>25</mn> <mo>%</mo></mrow> </msub> </mrow> </math> for the front-back and tube-travel directions for the FFS unit. The in-plane MTF in the tube-travel direction also improved with the FFS technology.The threshold gold thickness ( <math> <mrow><msub><mi>T</mi> <mi>t</mi></msub> </mrow> </math> ) for the 0.16-mm diameter discs of contrast detail phantom for mammography (CDMAM) improved for the FFS system in DBT mode, especially at greater heights above the table; <math> <mrow><msub><mi>T</mi> <mi>t</mi></msub> </mrow> </math> at 45 and 65 mm improved by 16% and 24%, respectively, compared with the uncompensated focus system. In addition, improvements in calcification and mass detection in a structured background were observed for DBT and synthetic mammography. The FFS system demonstrated faster scan times (4.8 s versus 21.7 s), potentially reducing patient motion artifacts.</p><p><strong>Conclusions: </strong>The FFS technology offers isotropic resolution, improved small detail detectability, and faster scan times in DBT mode compared with the traditional continuous gantry and focus motion approach.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 1","pages":"S13009"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11616485/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142786586","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 CT image restoration using system blur and noise models.","authors":"Yijie Yuan, Grace J Gang, J Webster Stayman","doi":"10.1117/1.JMI.12.1.014003","DOIUrl":"10.1117/1.JMI.12.1.014003","url":null,"abstract":"<p><strong>Purpose: </strong>The restoration of images affected by blur and noise has been widely studied and has broad potential for applications including in medical imaging modalities such as computed tomography. Recently, deep learning approaches have demonstrated the potential to enhance image quality beyond classic limits; however, most deep learning models attempt a blind restoration problem and base their restoration on image inputs alone without direct knowledge of the image noise and blur properties. We present a method that leverages both degraded image inputs and a characterization of the system's blur and noise to combine modeling and deep learning approaches.</p><p><strong>Approach: </strong>Different methods to integrate these auxiliary inputs are presented, namely, an input-variant and a weight-variant approach wherein the auxiliary inputs are incorporated as a parameter vector before and after the convolutional block, respectively, allowing easy integration into any convolutional neural network architecture.</p><p><strong>Results: </strong>The proposed model shows superior performance compared with baseline models lacking auxiliary inputs. Evaluations are based on the average peak signal-to-noise ratio and structural similarity index measure, selected examples of top and bottom 10% performance for varying approaches, and an input space analysis to assess the effect of different noise and blur on performance. In addition, the proposed model exhibits a degree of robustness when the blur and noise parameters deviate from their true values.</p><p><strong>Conclusion: </strong>Results demonstrate the efficacy of providing a deep learning model with auxiliary inputs, representing system blur and noise characteristics, to enhance the performance of the model in image restoration tasks.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 1","pages":"014003"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11788843/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143190925","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":"Introduction to the Special Issue: Celebrating Digital Tomosynthesis-Past, Present, and Future.","authors":"Stephen J Glick, Ingrid S Reiser, Mitchell M Goodsitt, Andrew D A Maidment, John M Sabol","doi":"10.1117/1.JMI.12.S1.S13001","DOIUrl":"10.1117/1.JMI.12.S1.S13001","url":null,"abstract":"<p><p>JMI guest editors present a diverse collection of research and perspectives in a special issue celebrating the past, present, and future of digital tomosynthesisan imaging modality that continues to grow in both clinical relevance and technical sophistication.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 1","pages":"S13001"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12120691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200543","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":"Pericoronary adipose tissue feature analysis in computed tomography calcium score images in comparison to coronary computed tomography angiography.","authors":"Yingnan Song, Hao Wu, Juhwan Lee, Justin Kim, Ammar Hoori, Tao Hu, Vladislav Zimin, Mohamed Makhlouf, Sadeer Al-Kindi, Sanjay Rajagopalan, Chun-Ho Yun, Chung-Lieh Hung, David L Wilson","doi":"10.1117/1.JMI.12.1.014503","DOIUrl":"10.1117/1.JMI.12.1.014503","url":null,"abstract":"<p><strong>Purpose: </strong>We investigated the feasibility and advantages of using non-contrast CT calcium score (CTCS) images to assess pericoronary adipose tissue (PCAT) and its association with major adverse cardiovascular events (MACE). PCAT features from coronary computed tomography angiography (CCTA) have been shown to be associated with cardiovascular risk but are potentially confounded by iodine. If PCAT in CTCS images can be similarly analyzed, it would avoid this issue and enable its inclusion in formal risk assessment from readily available, low-cost CTCS images.</p><p><strong>Approach: </strong>To identify coronaries in CTCS images that have subtle visual evidence of vessels, we registered CTCS with paired CCTA images having coronary labels. We developed an \"axial-disk\" method giving regions for analyzing PCAT features in three main coronary arteries. We analyzed hand-crafted and radiomic features using univariate and multivariate logistic regression prediction of MACE and compared results against those from CCTA.</p><p><strong>Results: </strong>Registration accuracy was sufficient to enable the identification of PCAT regions in CTCS images. Motion or beam hardening artifacts were often prevalent in \"high-contrast\" CCTA but not CTCS. Mean HU and volume were increased in both CTCS and CCTA for the MACE group. There were significant positive correlations between some CTCS and CCTA features, suggesting that similar characteristics were obtained. Using hand-crafted/radiomics from CTCS and CCTA, AUCs were 0.83/0.79 and 0.83/0.77, respectively, whereas Agatston gave AUC = 0.73.</p><p><strong>Conclusions: </strong>Preliminarily, PCAT features can be assessed from three main coronary arteries in non-contrast CTCS images with performance characteristics that are at the very least comparable to CCTA.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 1","pages":"014503"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11759132/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048280","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":"Estimation of the absorbed dose in simultaneous digital breast tomosynthesis and mechanical imaging.","authors":"Anna Bjerkén, Hanna Tomic, Sophia Zackrisson, Magnus Dustler, Predrag R Bakic, Anders Tingberg","doi":"10.1117/1.JMI.12.S1.S13003","DOIUrl":"10.1117/1.JMI.12.S1.S13003","url":null,"abstract":"<p><strong>Purpose: </strong>Use of mechanical imaging (MI) as complementary to digital mammography (DM), or in simultaneous digital breast tomosynthesis (DBT) and MI - DBTMI, has demonstrated the potential to increase the specificity of breast cancer screening and reduce unnecessary biopsies compared with DM. The aim of this study is to investigate the increase in the radiation dose due to the presence of an MI sensor during simultaneous image acquisition when automatic exposure control is used.</p><p><strong>Approach: </strong>A radiation dose study was conducted on clinically available breast imaging systems with and without an MI sensor present. Our estimations were based on three approaches. In the first approach, exposure values were compared in paired clinical DBT and DBTMI acquisitions in 97 women. In the second approach polymethyl methacrylate (PMMA) phantoms of various thicknesses were used, and the average glandular dose (AGD) values were compared. Finally, a rectangular PMMA phantom with a 45 mm thickness was used, and the AGD values were estimated based on air kerma measurements with an electronic dosemeter.</p><p><strong>Results: </strong>The relative increase in exposure estimated from digital imaging and communications in medicine headers when using an MI sensor in clinical DBTMI was <math><mrow><mn>11.9</mn> <mo>%</mo> <mo>±</mo> <mn>10.4</mn></mrow> </math> . For the phantom measurements of various thicknesses of PMMA, the relative increases in the AGD for DM and DBT measurements were, on average, <math><mrow><mn>10.7</mn> <mo>%</mo> <mo>±</mo> <mn>3.1</mn></mrow> </math> and <math><mrow><mn>11.4</mn> <mo>%</mo> <mo>±</mo> <mn>3.0</mn></mrow> </math> , respectively. The relative increase in the AGD using the electronic dosemeter was <math><mrow><mn>11.2</mn> <mo>%</mo> <mo>±</mo> <mo><</mo> <mn>0.001</mn></mrow> </math> in DM and <math><mrow><mn>12.2</mn> <mo>%</mo> <mo>±</mo> <mo><</mo> <mn>0.001</mn></mrow> </math> in DBT. The average difference in dose between the methods was <math><mrow><mn>11.5</mn> <mo>%</mo> <mo>±</mo> <mn>3.3</mn></mrow> </math> .</p><p><strong>Conclusions: </strong>Our measurements suggest that the use of simultaneous breast radiography and MI increases the AGD by an average of <math><mrow><mn>11.5</mn> <mo>%</mo> <mo>±</mo> <mn>3.3</mn></mrow> </math> . The increase in dose is within the acceptable values for mammography screening recommended by European guidelines.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 1","pages":"S13003"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11266811/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141761688","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":"Automated assessment of task-based performance of digital mammography and tomosynthesis systems using an anthropomorphic breast phantom and deep learning-based scoring.","authors":"Andrey Makeev, Kaiyan Li, Mark A Anastasio, Arthur Emig, Paul Jahnke, Stephen J Glick","doi":"10.1117/1.JMI.12.S1.S13005","DOIUrl":"10.1117/1.JMI.12.S1.S13005","url":null,"abstract":"<p><strong>Purpose: </strong>Conventional metrics used for assessing digital mammography (DM) and digital breast tomosynthesis (DBT) image quality, including noise, spatial resolution, and detective quantum efficiency, do not necessarily predict how well the system will perform in a clinical task. A number of existing phantom-based methods have their own limitations, such as unrealistic uniform backgrounds, subjective scoring using humans, and regular signal patterns unrepresentative of common clinical findings. We attempted to address this problem with a realistic breast phantom with random hydroxyapatite microcalcifications and semi-automated deep learning-based image scoring. Our goal was to develop a methodology for objective task-based assessment of image quality for tomosynthesis and DM systems, which includes an anthropomorphic phantom, a detection task (microcalcification clusters), and automated performance evaluation using a convolutional neural network.</p><p><strong>Approach: </strong>Experimental 2D and pseudo-3D mammograms of an anthropomorphic inkjet-printed breast phantom with inserted microcalcification clusters were collected on clinical mammography systems to train a signal-present/signal-absent image classifier based on Resnet-18 architecture. In a separate validation study using simulations, this Resnet-18 classifier was shown to approach the performance of an ideal observer. Microcalcification detection performance was evaluated as a function of four dose levels using receiver operating characteristic (ROC) analysis [i.e., area under the ROC curve (AUC)]. To demonstrate the use of this evaluation approach for assessing different technologies, the method was applied to two different mammography systems, as well as to mammograms with re-binned pixels emulating a lower-resolution X-ray detector.</p><p><strong>Results: </strong>Microcalcification detectability, as assessed by the deep learning classifier, was observed to vary with the exposure incident on the breast phantom for both DM and tomosynthesis. At full dose, experimental AUC was 0.96 (for DM) and 0.95 (for DBT), whereas at half dose, it dropped to 0.85 and 0.71, respectively. AUC performance on DM was significantly decreased with an effective larger pixel size obtained with re-binning. The task-based assessment approach also showed the superiority of a newer mammography system compared with an older system.</p><p><strong>Conclusions: </strong>An objective task-based methodology for assessing the image quality of mammography and tomosynthesis systems is proposed. Possible uses for this tool could be quality control, acceptance, and constancy testing, assessing the safety and effectiveness of new technology for regulatory submissions, and system optimization. The results from this study showed that the proposed evaluation method using a deep learning model observer can track differences in microcalcification signal detectability with varied exposure conditions.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 1","pages":"S13005"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11474246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142477762","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":"Our journey toward implementation of digital breast tomosynthesis in breast cancer screening: the Malmö Breast Tomosynthesis Screening Project.","authors":"Anders Tingberg, Victor Dahlblom, Magnus Dustler, Daniel Förnvik, Kristin Johnson, Pontus Timberg, Sophia Zackrisson","doi":"10.1117/1.JMI.12.S1.S13006","DOIUrl":"10.1117/1.JMI.12.S1.S13006","url":null,"abstract":"<p><strong>Purpose: </strong>The purpose is to describe the Malmö Breast Tomosynthesis Screening Project from the beginning to where we are now, and thoughts for the future.</p><p><strong>Approach: </strong>In two acts, we describe the efforts made by our research group to improve breast cancer screening by introducing digital breast tomosynthesis (DBT), all the way from initial studies to a large prospective population-based screening trial and beyond.</p><p><strong>Results: </strong>Our studies have shown that DBT has significant advantages over digital mammography (DM), the current gold standard method for breast cancer screening in Europe, in many aspects except a major one-the increased radiologist workload introduced with DBT compared with DM. It is foreseen that AI could be a viable solution to overcome this problem.</p><p><strong>Conclusions: </strong>We have proved that one-view DBT is a highly efficient screening approach with respect to diagnostic performance.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 Suppl 1","pages":"S13006"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11501043/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510422","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":"Learning disentangled representations to harmonize connectome network measures.","authors":"Nancy R Newlin, Michael E Kim, Praitayini Kanakaraj, Kimberly Pechman, Niranjana Shashikumar, Elizabeth Moore, Derek Archer, Timothy Hohman, Angela Jefferson, Daniel Moyer, Bennett A Landman","doi":"10.1117/1.JMI.12.1.014004","DOIUrl":"10.1117/1.JMI.12.1.014004","url":null,"abstract":"<p><strong>Purpose: </strong>Connectome network metrics are commonly regarded as fundamental properties of the brain, and their alterations have been implicated in the development of Alzheimer's disease, multiple sclerosis, and traumatic brain injury. However, these metrics are actually estimated properties through a multistage propagation from local voxel diffusion estimations, regional tractography, and region of interest mapping. These estimation processes are significantly influenced by choices specific to imaging protocols and software, producing site-wise effects.</p><p><strong>Approach: </strong>Recent advances in disentanglement techniques offer opportunities to learn representational spaces that separate factors that cause domain shifts from intrinsic biological factors. Although these techniques have been applied in unsupervised brain anomaly detection and image-level features, their application to the unique manifold structures of connectome adjacency matrices remains unexplored. Here, we explore the conditional variational autoencoder structure for generating site-invariant representations of the connectome, allowing the harmonization of brain network measures.</p><p><strong>Results: </strong>Focusing on the context of aging, we conducted a study involving 823 patients across two sites. This approach effectively segregates site-specific influences from biological features, aligns network measures across different domains (Cohen's <math><mrow><mi>D</mi> <mo><</mo> <mn>0.2</mn></mrow> </math> and Mann-Whitney <math><mrow><mi>U</mi> <mtext>-</mtext> <mtext>test</mtext> <mo><</mo> <mn>0.05</mn></mrow> </math> ), and maintains associations with age ( <math><mrow><mn>2.71</mn> <mo>×</mo> <msup><mrow><mn>10</mn></mrow> <mrow><mo>-</mo> <mn>02</mn></mrow> </msup> <mo>±</mo> <mn>2.86</mn> <mo>×</mo> <msup><mrow><mn>10</mn></mrow> <mrow><mo>-</mo> <mn>03</mn></mrow> </msup> </mrow> </math> error in years) and sex ( <math><mrow><mn>0.92</mn> <mo>±</mo> <mn>0.02</mn></mrow> </math> accuracy).</p><p><strong>Conclusions: </strong>Our findings demonstrate that using latent representations significantly harmonizes network measures and provides robust metrics for multi-site brain network analysis.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 1","pages":"014004"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11826349/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143434157","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":"Toward Continued Growth for the JMI Community.","authors":"","doi":"10.1117/1.JMI.12.1.010101","DOIUrl":"https://doi.org/10.1117/1.JMI.12.1.010101","url":null,"abstract":"<p><p>JMI Editor in Chief Bennett Landman provides an overview of JMI Volume 12 Issue 1 and spotlights key aspects of JMI peer review, with an eye toward continued growth for the JMI community.</p>","PeriodicalId":47707,"journal":{"name":"Journal of Medical Imaging","volume":"12 1","pages":"010101"},"PeriodicalIF":1.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11817210/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143415687","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}