Proceedings of SPIE--the International Society for Optical Engineering最新文献

{"title":"Assessing Open-world Foundation Models for Zero-shot Skin Segmentation in Clinical Dermatological Photographs.","authors":"Yihao Liu, Andrew J McNeil, Bohan Jiang, Michael E Kim, Inga Saknite, Aaron T Zhao, Placide Mbala-Kingebeni, Olivier Tshiani Mbaya, Tyra Silaphet, Rachel Weiss, Lori E Dodd, Edward W Cowen, Steven Z Pavletic, Veronique Nussenblatt, Benoit M Dawant, Bennett A Landman, Eric R Tkaczyk","doi":"10.1117/12.3085924","DOIUrl":"10.1117/12.3085924","url":null,"abstract":"<p><p>Skin segmentation from clinical photography is a crucial step in dermatological image analysis. However, the variability in skin tones, lighting conditions, anatomical regions, and the presence of additional objects introduces significant challenges. Due to these complexities, the segmentation process is often performed manually, as developing an algorithm capable of handling such diverse conditions is particularly difficult. Recently, open-world foundation models have emerged, offering the potential to generalize across diverse and unseen conditions. These models present a promising opportunity for dermatology. In this work, we adopt two such models-Grounding DINO and SAM 2-to construct a pipeline for <i>zero-shot</i> skin segmentation in dermatology. We evaluated our approach on two clinical skin photography datasets comprising 27,378 images. Based on a manual rating protocol, 77.1% of the segmentations were deemed acceptable, demonstrating robustness in handling real-world clinical photographs. Our results highlight the potential of open-world foundation models to address a challenging problem in dermatology with minimal human involvement.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13929 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13105278/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791607","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":"Joint Estimation of Scatter Distribution and Material Maps in Volumetric Dual-Layer Cone-Beam CT.","authors":"Xiao Jiang, Yue Fan, Altea Lorenzon, Grace J Gang, J Webster Stayman","doi":"10.1117/12.3086291","DOIUrl":"10.1117/12.3086291","url":null,"abstract":"<p><p>Dual-energy cone-beam CT (DE-CBCT) provides a flexible spectral imaging approach for volumetric material quantification, but its accuracy can be severely degraded by photon scatter. We propose a joint scatter estimation and one-step material decomposition framework for DE-CBCT using a dual-layer flat-panel detector. In this method, scatter is explicitly modeled in the physical forward model, while a diffusion model provides a sophisticated anatomical prior for material decomposition and also acts as a scatter-free prior for scatter estimation. The joint estimation framework operates iteratively, alternating between diffusion posterior sampling and prior-guided scatter estimation to jointly recover material density maps and scatter distributions. Experiments using an anthropomorphic head phantom show that direct material decomposition from cone-beam projections leads to significant bias and cross-talk between water and bone channels. In contrast, the proposed joint estimation strategy achieves accurate water-bone separation and substantially improves quantitative accuracy. The proposed method provides a promising framework for accurate spectral CBCT imaging.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13924 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13095153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791596","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":"A Pilot Study of Multimodal Dosiomics and Longitudinal Delta-Radiomics for Predicting Radiation-Induced Xerostomia in Head-and-Neck Cancer.","authors":"Peng Chen, Kailin Yang, Mojtaba Safari, Junbo Peng, Bingqi Guo, Peng Qi, Arda Durmaz, Jacob Miller, Shlomo Koyfman, Richard L J Qiu, Xiaofeng Yang, Jacob G Scott","doi":"10.1117/12.3086834","DOIUrl":"10.1117/12.3086834","url":null,"abstract":"<p><p>Radiation-induced xerostomia remains a common and debilitating side effect in head-and-neck cancer radiotherapy, despite advances in volumetric modulated arc therapy (VMAT). Traditional dose-volume histogram (DVH) metrics capture only part of the variation in toxicity, motivating the use of multimodal imaging biomarkers such as dosiomics and radiomics to characterize dose distribution and tissue response better. In this pilot study, we present an integrated framework combining DVH metrics, 3D dosiomics features, baseline planning CT (pCT) radiomics, and novel longitudinal delta-radiomics derived from daily cone-beam CT-based synthetic CT (sCT) images to predict post-treatment xerostomia severity. In a cohort of ten high-risk oropharyngeal cancer patients treated with VMAT at the Cleveland Clinic, wrapper-based feature selection yielded a compact set of 15 predictors (5 DVH, 3 dosiomics, 4 pCT radiomics, 3 Δ-sCT radiomics). Using cross-validation, four classifiers, including support-vector machine (SVM), regularized logistic regression (GLMnet), Naïve Bayes, and k-nearest neighbors, achieved consistently strong performance for discriminating grade I vs. grade II xerostomia, with AUC of 0.97-1.00, accuracy of 0.90-0.93, uniformly high sensitivity (1.00), specificity of 0.75-0.83, and F1 scores of 0.923-0.945. SVM and GLMnet showed the best overall balance of discrimination and robustness. These results demonstrate the potential of integrating dosiomics with multiphase radiomics, particularly time-resolved delta-radiomics, for individualized xerostomia risk prediction.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13930 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13110829/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791515","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":"Integrating 2D Dermatological Photography with 3D Anatomical Surfaces.","authors":"Bohan Jiang, Yihao Liu, Andrew J McNeil, Chloe Cho, Inga Saknite, Steven Z Pavletic, Edward W Cowen, Rachel Weiss, Bennett A Landman, Benoit M Dawant, Eric R Tkaczyk","doi":"10.1117/12.3085708","DOIUrl":"10.1117/12.3085708","url":null,"abstract":"<p><p>Standardized 2D photography plays an essential role in dermatologic practice, supporting longitudinal documentation, patient monitoring, and consensus-based clinical scoring. However, photographs taken from limited views often suffer from reduced anatomical context and missing body-location information. 3D representations enable unified spatial interpretation of multi-view imagery. Recent developments in computer vision have made it feasible to infer dense correspondences between 2D images and a 3D human mesh. In this study, we explored integrating 2D dermatological images with a 3D surface model using DensePose, a deep learning-based human dense correspondence framework. This creates an anatomically grounded representation that supports mesh-level analyses and recovers spatial context for each image. We use a dataset including four full body photographs (front, back, and each side) from each of 147 subjects with chronic graft-versus-host disease, for a total of 588 images. Our method integrates these multiple 2D full-body photographs captured across varied body shapes and camera angles into a 3D mesh. We further showed that the resulting 3D mesh enables quantification of the extent to which individual 2D images, or their combinations, represent the complete body surface. On average, a single full body view captures 28% of the body surface, while adding a second, third, and fourth view increases average coverage to 50%, 72%, and 80%, respectively. To assess spatial consistency, we annotated up to 10 anatomical landmarks per patient on 80 images across 20 patients and reported a median pairwise geodesic distance between corresponding landmarks of 4.6 cm. These findings can guide how dermatology images are captured and support future opportunities in monitoring, education, and communication using existing infrastructure.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13926 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13105281/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791533","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 Detection and Assessment of Post-Operative Eyelid Outcome in Trachomatous Trichiasis Surgery.","authors":"Lucie Dole, Juan C Prieto, Jerusha Weaver, Hashiya Kana, Emily W Gower","doi":"10.1117/12.3086682","DOIUrl":"10.1117/12.3086682","url":null,"abstract":"<p><p>Trachomatous trichiasis (TT) is an ocular condition in which the eyelid turns inward causing the eyelashes to scratch the eye, leading to blindness and affecting millions of people worldwide. The standard treatment for TT is surgery, where an incision is made to rotate the eyelid margin outward, repositioning the eyelashes to their original position. However, outcomes after surgery are often suboptimal, with a high risk of post-operative trichiasis (PTT). Studies have shown that the appearance of the immediate post-operative eyelid strongly correlates with the success of the procedure after six weeks, emphasizing the importance of early identification and correction of poor surgical results. We propose a mobile application that detects post-operative eyelids at higher risk of poor outcomes, enabling surgeons in the field to have immediate feedback to perform the necessary corrections and improve patient outcomes. The algorithm is based on the well-established Faster R-CNN model, which detects and classifies parts of the eyelid into three categories: under-correction, overcorrection, or appropriate correction. The model achieved 75.7% recall for under-correction and 75.6% recall for overcorrection, demonstrating strong sensitivity in identifying potential for adverse outcomes. The UI/UX of the application was designed with an intuitive interface that allows users to take a picture of an eyelid and evaluate the surgical result using the model. The algorithm runs in under 12 seconds and has been tested by TT surgeons in the field. This work has the potential to significantly improve post-operative trichiasis outcomes, reducing PTT rates, and improving life quality in resource-limited settings.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13929 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13109787/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791571","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":"Color CT, literally.","authors":"Scott S Hsieh","doi":"10.1117/12.3086134","DOIUrl":"10.1117/12.3086134","url":null,"abstract":"<p><p>Spectral CT has long been described as \"color CT\" figuratively, with energy bands of invisible x-rays analogous to the wavelength bands of visible light. Here, we propose to take the idea of \"color CT\" concept literally: what would happen if the photodiodes used in energy-integrating detectors were color sensitive, just like subpixels in consumer cameras are color sensitive? This idea at first seems to yield no tangible benefit, but we hypothesize that spectral <i>optical</i> imaging can be used to measure spectral <i>X-ray</i> imaging. Using information from publicly available datasheets, we modeled the optical transport of gadolinium oxysulfide (GOS), the most common scintillator used today. GOS emits majority green but minority red light, and the green light preferentially self-absorbed. This explains the characteristic yellow color of raw GOS crystal, and leads us to predict that low energy X-rays, which deposit energy at shallower depths, will produce relatively more red photons that reach the photodiode. This effect is weak but occurs after X-ray quanta have been amplified (roughly 1000-fold) into visible light quanta, so it could be detected with appropriately sensitive digitization electronics. We compare the proposed color detector to conventional dual-layer detectors, and find that the spectral separation is worse primarily because of the GOS K-edge. While its spectral performance would be poor, it could be made widely available because it requires changes in the silicon circuitry only. It is possible that color photodiodes would one day replace monochromatic diodes in energy-integrating detectors, making spectral imaging standard among new scanners.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13924 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13105320/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791623","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":"One-Step Material Decomposition Using Spectral Diffusion Posterior Sampling in Sparse-View Dual-Layer CT.","authors":"Yue Fan, Xiao Jiang, Zimo Liu, Grace J Gang, J Webster Stayman","doi":"10.1117/12.3087781","DOIUrl":"10.1117/12.3087781","url":null,"abstract":"<p><p>Spectral X-ray imaging technology can enhance interventional imaging by providing material-specific information. However, typical interventional systems with cone beam CT (CBCT) face limitations in scan range and acquisition speed, making sparse-view imaging an attractive option for faster, lower-dose scans, although sparse data pose greater challenges for accurate material quantification. We previously proposed a Spectral Diffusion Posterior Sampling (Spectral DPS) framework for generalizable one-step material decomposition, which integrates a learned diffusion prior with a physics-based forward model. The approach requires only a single unconditional training while providing flexibility of application across different system configurations. In this work, we investigate the performance of Spectral DPS on a physical dual-layer CBCT system under different sparse-view conditions. Specifically, an anthropomorphic head phantom was scanned on a bench-top CBCT system equipped with a dual-layer flat-panel detector, and one-step material decomposition was performed using Spectral DPS with the number of projections ranging from 720 down to 45 views. Results demonstrate that Spectral DPS can achieve accurate water-bone separation, and maintains uniform soft tissue appearance even though such homogeneous regions represent an out-of-distribution condition. Decomposition results remain robust down to 120 views, with minimal loss of structural detail and quantitative accuracy. At more extreme sparsity, Spectral DPS exhibits increased variability and artifacts, though bony structures remain discernible at as few as 90 views. These findings highlight the potential of Spectral DPS to enable accurate and robust material decomposition under certain sparse-view acquisitions for interventional CBCT imaging.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13924 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13102010/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147791553","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":"Impact of reconstruction kernel variability on segmentation consistency in low-dose thoracic CT.","authors":"Eléanor Liard, Aravind R Krishnan, Fabien Maldonado, Kim Sandler, Yuankai Huo, Lianrui Zuo, Bennett A Landman","doi":"10.1117/12.3085743","DOIUrl":"10.1117/12.3085743","url":null,"abstract":"<p><p>Structural thoracic segmentation with computed tomography (CT) has become a ubiquitous step in medical image processing. Several tools-including TotalSegmentator-now provide efficient, open methods for labeling regions of interest (ROI) in CT. However, variability in CT reconstruction kernels, especially between soft and hard kernels, can affect image characteristics and potentially impact automatic segmentation, downstream analysis, and clinical decisions. Yet, this variability and its impact on downstream analysis have not been fully investigated in a systematic way. To address this, we examine the consistency of thoracic region segmentations produced by TotalSegmentator when applied to 9,529 CT images reconstructed with paired soft and hard kernels from the National Lung Screening Trial (NLST) dataset. It includes 9,529 CT scan pairs from 9,519 subjects (5,733 men, 3,796 women; ages 43-74), with each subject contributing one scan session, including data from multiple vendors (Siemens, Philips and GE Medical Systems) and reconstruction kernels, drawn from the Lung Screening Study (LSS) (n=6,794), American College of Radiology Imaging Network (ACRIN) with biomarkers (n=1,488), and ACRIN without biomarkers (n=1,247), with 10 duplicated subjects treated as separate individuals. We quantified kernel-induced variability using Dice similarity coefficient, volumetric and intensity differences across 84 thoracic regions. We evaluated the relationship between the average and difference of volumes, Hounsfield Units (HU) and standard deviation of HU of ROI. Additionally, we assessed manufacturer-specific differences in segmentation consistency between Philips, Siemens and GE Medical Systems. Graphical representations with Bland-Altman plots show that some thoracic regions (e.g. heart) are more robust to kernel variation than others (e.g. thyroid gland), and Siemens scanners show the lowest segmentation variability between kernel types. These findings provide early insights into when kernel-induced variability affects automatic segmentation performance differently across the body in clinical and research workflows, therefore emphasizing the need for consistent CT reconstruction or kernel harmonization.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13925 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13245479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148213612","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":"Modeling Workflow, Operational, and Financial Implications of AI-Enabled Same-Day Diagnostic Follow-Up for Screening Mammograms.","authors":"Yannan Lin, Anne C Hoyt, Nina M Capiro, Olivia Linden, Luoting Zhuang, Mehmet Ulvi Saygi Ayvaci, Mehmet Eren Ahsen, Ya-Chen Tina Shih, William Hsu","doi":"10.1117/12.3087732","DOIUrl":"10.1117/12.3087732","url":null,"abstract":"<p><p>While much of the evaluation of artificial intelligence (AI) in healthcare has focused on technical performance metrics such as accuracy or area under the curve, real-world adoption critically depends on how AI reshapes clinical workflows, operations, and revenue streams. Simulation models provide a means to anticipate these impacts before implementation, allowing stakeholders to weigh benefits against potential harm. In this study, we used discrete-event simulation to evaluate an AI-assisted workflow for same-day diagnostic breast imaging following abnormal screening mammograms. The revised workflow captured an additional of 1.1% mammography screening patients who might otherwise be lost to follow-up. It also eliminated the need for a second visit for diagnostic workup for 11% of mammography screening patients. It also increased daily work relative value units by 4.8%, translating to an estimated $15,979 in additional annual gain, while extending clinic operating hours by 2.9%, amounting to 109.5 hours annually. These findings highlight how simulation modeling can inform the operational and financial implications of AI adoption in imaging workflows in clinical practice.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13930 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13061406/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147647836","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":"A Generative Model of Lung CT Conditioned on Radiomics Features.","authors":"Patrick Li, Yijie Yuan, Xin Wang, Huay Din, Cheng Ting Lin, Grace Hyun Kim, Michael F McNitt-Gray, J Webster Stayman, Grace J Gang","doi":"10.1117/12.3088074","DOIUrl":"10.1117/12.3088074","url":null,"abstract":"<p><p>Deep learning image generation has been an active area of research in a number of applications. However, traditional generative models are not able to control specific properties of the image outputs. In this work, we propose a deep learning model that produces images according to user-specified texture feature values. We adopted a diffusion transformer architecture and used texture features to condition the reverse process. The model was trained on lung patches from a public lung CT database. Two texture features, autocorrelation and inverse difference derived from the Gray-Level Co-Occurrence Matrix were used as conditional inputs. We evaluated the ability of the model to produce samples with similar feature values as the conditional inputs. Both in-distribution and out-of-distribution conditions were evaluated. Results indicate that the model is able to generate image patches resembling lung parenchyma. The autocorrelation and inverse difference of generated images have good agreement with and exhibit low variability around the conditional inputs. The concordance correlation coefficient between real and generated samples is 0.9962 for autocorrelation and 0.9402 for inverse difference. Visual assessment of image samples reveals that real and generated images share similar features, consistent with their radiomic properties. Findings from this work indicate that the diffusion transformer model is able to generate images with texture features closely aligning with the conditional inputs, supporting its utility for highly controlled data generation for a variety of applications.</p>","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13924 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13089804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147724675","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}