Proceedings of SPIE--the International Society for Optical Engineering最新文献_第4页

Understanding the impact of patient-specific geometries on Cherenkov emission-to-dose relationship during External Beam Radiation Therapy (EBRT). 了解外部束放射治疗（EBRT）期间患者特异性几何形状对切伦科夫辐射剂量关系的影响。

Proceedings of SPIE--the International Society for Optical Engineering Pub Date : 2025-01-01 Epub Date: 2025-03-20 DOI: 10.1117/12.3048321

Baozhu Lu, Brook Byrd, Yifeng Zhu, Daniel Alexander, John P Plastaras, Taoran Li, Lisha Chen, Brian W Pogue, Timothy C Zhu

引用次数: 0

Real-time dosimetry using scintillator technology in external beam radiation therapy (EBRT). 闪烁体技术在体外放射治疗（EBRT）中的实时剂量测定。

Proceedings of SPIE--the International Society for Optical Engineering Pub Date : 2025-01-01 Epub Date: 2025-03-20 DOI: 10.1117/12.3048325

Baozhu Lu, Yifeng Zhu, Brook Byrd, Daniel Alexander, John P Plastaras, Taoran Li, Lisha Chen, Brian W Pogue, Timothy C Zhu

{"title":"Real-time dosimetry using scintillator technology in external beam radiation therapy (EBRT).","authors":"Baozhu Lu, Yifeng Zhu, Brook Byrd, Daniel Alexander, John P Plastaras, Taoran Li, Lisha Chen, Brian W Pogue, Timothy C Zhu","doi":"10.1117/12.3048325","DOIUrl":"10.1117/12.3048325","url":null,"abstract":"Total Skin Electron Therapy (TSET) is a specialized radiotherapy technique used for the treatment of cutaneous T-cell lymphoma and other diffuse skin malignancies. Accurate real-time dosimetry is essential for ensuring precise dose delivery while minimizing toxicity to healthy tissues. This study presents the development and implementation of an advanced scintillator-based real-time dosimetry system designed to enhance dosimetry precision in TSET, utilizing a source-to-surface distance (SSD) of 5 meters with an intervening spoiler to optimize dose distribution. In a phantom study, nine scintillators were vertically positioned 20 cm apart on a white PVC board, alongside diodes that served as absolute dose references. Scintillator signals were captured using Cherenkov imaging cameras and fitting with correction factors were applied to ensure consistency between scintillator and diode-based dose measurements. Additionally, in vivo patient data were analyzed, revealing variations in scintillator response depending on positioning and camera selection. The results indicate that scintillators provide a feasible solution for real-time dosimetry in TSET, contingent upon strict regulation of scintillator placement and patient posture. Further optimization and standardization of scintillator positioning are necessary to enhance clinical implementation and dosimetry accuracy, ensuring reliable and precise dose monitoring in TSET applications.","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13309 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12266726/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144651460","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}

引用次数: 0

Probing bacterial membranes with polarization-resolved second harmonic scattering. 用偏振分辨二次谐波散射探测细菌膜。

Proceedings of SPIE--the International Society for Optical Engineering Pub Date : 2024-08-01 Epub Date: 2024-10-02 DOI: 10.1117/12.3028197

Eleanor F Page, Marea J Blake, Tessa R Calhoun

引用次数: 0

Exploring advanced 2D acquisitions in breast tomosynthesis: T-shaped and Pentagon geometries. 探索乳腺断层合成中的先进二维采集：T 形和五角形几何图形。

Proceedings of SPIE--the International Society for Optical Engineering Pub Date : 2024-06-01 Epub Date: 2024-05-29 DOI: 10.1117/12.3027054

Priyash Singh, Chloe J Choi, Bruno Barufaldi, Andrew D A Maidment, Raymond J Acciavatti

{"title":"Exploring advanced 2D acquisitions in breast tomosynthesis: T-shaped and Pentagon geometries.","authors":"Priyash Singh, Chloe J Choi, Bruno Barufaldi, Andrew D A Maidment, Raymond J Acciavatti","doi":"10.1117/12.3027054","DOIUrl":"10.1117/12.3027054","url":null,"abstract":"In this study, we investigate the performance of advanced 2D acquisition geometries - Pentagon and T-shaped - in digital breast tomosynthesis (DBT) and compare them against the conventional 1D geometry. Unlike the conventional approach, our proposed 2D geometries also incorporate anterior projections away from the chest wall. Implemented on the Next-Generation Tomosynthesis (NGT) prototype developed by X-ray Physics Lab (XPL), UPenn, we utilized various phantoms to compare three geometries: a Defrise slab phantom with alternating plastic slabs to study low-frequency modulation; a Checkerboard breast phantom (a 2D adaptation of the Defrise phantom design) to study the ability to reconstruct the fine features of the checkerboard squares; and the 360° Star-pattern phantom to assess aliasing and compute the Fourier-spectral distortion (FSD) metric that assesses spectral leakage and the contrast transfer function. We find that both Pentagon and T-shaped scans provide greater modulation amplitude of the Defrise phantom slabs and better resolve the squares of the Checkerboard phantom against the conventional scan. Notably, the Pentagon geometry exhibited a significant reduction in aliasing of spatial frequencies oriented in the right-left (RL) medio-lateral direction, which was corroborated by a near complete elimination of spectral leakage in the FSD plot. Conversely T-shaped scan redistributes the aliasing between both posteroanterior (PA) and RL directions thus maintaining non-inferiority against the conventional scan which is predominantly affected by PA aliasing. The results of this study underscore the potential of incorporating advanced 2D geometries in DBT systems, offering marked improvements in imaging performance over the conventional 1D approach.","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"13174 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11236198/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141592639","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}

引用次数: 0

A general approach to improve adversarial robustness of DNNs for medical image segmentation and detection. 提高用于医学图像分割和检测的 DNN 抗对抗鲁棒性的通用方法。

Proceedings of SPIE--the International Society for Optical Engineering Pub Date : 2024-02-01 Epub Date: 2024-04-02 DOI: 10.1117/12.3006534

Linhai Ma, Jiasong Chen, Linchen Qian, Liang Liang

{"title":"A general approach to improve adversarial robustness of DNNs for medical image segmentation and detection.","authors":"Linhai Ma, Jiasong Chen, Linchen Qian, Liang Liang","doi":"10.1117/12.3006534","DOIUrl":"10.1117/12.3006534","url":null,"abstract":"It is known that deep neural networks (DNNs) are vulnerable to adversarial noises. Improving adversarial robustness of DNNs is essential. This is not only because unperceivable adversarial noise is a threat to the performance of DNNs models, but also adversarially robust DNNs have a strong resistance to the white noises that may present everywhere in the actual world. To improve adversarial robustness of DNNs, a variety of adversarial training methods have been proposed. Most of the previous methods are designed under one single application scenario: image classification. However, image segmentation, landmark detection, and object detection are more commonly observed than classifying the entire images in the medical imaging field. Although classification tasks and other tasks (e.g., regression) share some similarities, they also differ in certain ways, e.g., some adversarial training methods use misclassification criteria, which is well-defined in classification but not in regression. These restrictions/limitations hinder application of adversarial training for many medical imaging analysis tasks. In our work, the contributions are as follows: (1) We investigated the existing adversarial training methods and discovered the challenges that make those methods unsuitable for adaptation in segmentation and detection tasks. (2) We modified and adapted some existing adversarial training methods for medical image segmentation and detection tasks. (3) We proposed a general adversarial training method for medical image segmentation and detection. (4) We implemented our method in diverse medical imaging tasks using publicly available datasets, including MRI segmentation, Cephalometric landmark detection, and blood cell detection. The experiments substantiated the effectiveness of our method.","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12926 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11491114/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482678","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}

引用次数: 0

CDPNet: a radiomic feature learning method with epigenetic application to estimating MGMT promoter methylation status in glioblastoma. CDPNet：一种应用于胶质母细胞瘤 MGMT 启动子甲基化状态的放射学特征学习方法。

Proceedings of SPIE--the International Society for Optical Engineering Pub Date : 2024-02-01 Epub Date: 2024-04-02 DOI: 10.1117/12.3009716

Jun Guo, Fanyang Yu, MacLean P Nasrallah, Christos Davatzikos

{"title":"CDPNet: a radiomic feature learning method with epigenetic application to estimating MGMT promoter methylation status in glioblastoma.","authors":"Jun Guo, Fanyang Yu, MacLean P Nasrallah, Christos Davatzikos","doi":"10.1117/12.3009716","DOIUrl":"https://doi.org/10.1117/12.3009716","url":null,"abstract":"Radiomics has been widely recognized for its effectiveness in decoding tumor phenotypes through the extraction of quantitative imaging features. However, the robustness of radiomic methods to estimate clinically relevant biomarkers non-invasively remains largely untested. In this study, we propose Cascaded Data Processing Network (CDPNet), a radiomic feature learning method to predict tumor molecular status from medical images. We apply CDPNet to an epigenetic case, specifically targeting the estimation of O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation from Magnetic Resonance Imaging (MRI) scans of glioblastoma patients. CDPNet has three components: 1) Principal Component Analysis (PCA), 2) Fisher Linear Discriminant (FLD), and 3) a combination of hashing and blockwise histograms. The outlined architectural framework capitalizes on PCA to reconstruct input image patches, followed by FLD to extract discriminative filter banks, and finally using binary hashing and blockwise histogram module for indexing, pooling, and feature generation. To validate the effectiveness of CDPNet, we conducted an exhaustive evaluation on a comprehensive retrospective cohort comprising 484 IDH-wildtype glioblastoma patients with pre-operative multi-parametric MRI scans (T1, T1-Gd, T2, and T2-FLAIR). The prediction of MGMT promoter methylation status was cast as a binary classification problem. The developed model underwent rigorous training via 10-fold cross-validation on a discovery cohort of 446 patients. Subsequently, the model's performance was evaluated on a distinct and previously unseen replication cohort of 38 patients. Our method achieved an accuracy of 70.11% and an area under the curve of 0.71 (95% CI: 0.65 - 0.74).","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12930 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11034757/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140859305","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}

引用次数: 0

Few-shot Tumor Bud Segmentation Using Generative Model in Colorectal Carcinoma. 利用生成模型对结直肠癌中的少量肿瘤芽进行分割

Proceedings of SPIE--the International Society for Optical Engineering Pub Date : 2024-02-01 Epub Date: 2024-04-03 DOI: 10.1117/12.3006418

Ziyu Su, Wei Chen, Preston J Leigh, Usama Sajjad, Shuo Niu, Mostafa Rezapour, Wendy L Frankel, Metin N Gurcan, M Khalid Khan Niazi

{"title":"Few-shot Tumor Bud Segmentation Using Generative Model in Colorectal Carcinoma.","authors":"Ziyu Su, Wei Chen, Preston J Leigh, Usama Sajjad, Shuo Niu, Mostafa Rezapour, Wendy L Frankel, Metin N Gurcan, M Khalid Khan Niazi","doi":"10.1117/12.3006418","DOIUrl":"https://doi.org/10.1117/12.3006418","url":null,"abstract":"Current deep learning methods in histopathology are limited by the small amount of available data and time consumption in labeling the data. Colorectal cancer (CRC) tumor budding quantification performed using H&E-stained slides is crucial for cancer staging and prognosis but is subject to labor-intensive annotation and human bias. Thus, acquiring a large-scale, fully annotated dataset for training a tumor budding (TB) segmentation/detection system is difficult. Here, we present a DatasetGAN-based approach that can generate essentially an unlimited number of images with TB masks from a moderate number of unlabeled images and a few annotated images. The images generated by our model closely resemble the real colon tissue on H&E-stained slides. We test the performance of this model by training a downstream segmentation model, UNet++, on the generated images and masks. Our results show that the trained UNet++ model can achieve reasonable TB segmentation performance, especially at the instance level. This study demonstrates the potential of developing an annotation-efficient segmentation model for automatic TB detection and quantification.","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12933 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11098595/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140961101","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}

引用次数: 0

Inter-vendor harmonization of CT reconstruction kernels using unpaired image translation. 利用非配对图像转换实现 CT 重建内核的供应商间协调。

Proceedings of SPIE--the International Society for Optical Engineering Pub Date : 2024-02-01 Epub Date: 2024-04-02 DOI: 10.1117/12.3006608

Aravind R Krishnan, Kaiwen Xu, Thomas Li, Chenyu Gao, Lucas W Remedios, Praitayini Kanakaraj, Ho Hin Lee, Shunxing Bao, Kim L Sandler, Fabien Maldonado, Ivana Išgum, Bennett A Landman

{"title":"Inter-vendor harmonization of CT reconstruction kernels using unpaired image translation.","authors":"Aravind R Krishnan, Kaiwen Xu, Thomas Li, Chenyu Gao, Lucas W Remedios, Praitayini Kanakaraj, Ho Hin Lee, Shunxing Bao, Kim L Sandler, Fabien Maldonado, Ivana Išgum, Bennett A Landman","doi":"10.1117/12.3006608","DOIUrl":"10.1117/12.3006608","url":null,"abstract":"The reconstruction kernel in computed tomography (CT) generation determines the texture of the image. Consistency in reconstruction kernels is important as the underlying CT texture can impact measurements during quantitative image analysis. Harmonization (i.e., kernel conversion) minimizes differences in measurements due to inconsistent reconstruction kernels. Existing methods investigate harmonization of CT scans in single or multiple manufacturers. However, these methods require paired scans of hard and soft reconstruction kernels that are spatially and anatomically aligned. Additionally, a large number of models need to be trained across different kernel pairs within manufacturers. In this study, we adopt an unpaired image translation approach to investigate harmonization between and across reconstruction kernels from different manufacturers by constructing a multipath cycle generative adversarial network (GAN). We use hard and soft reconstruction kernels from the Siemens and GE vendors from the National Lung Screening Trial dataset. We use 50 scans from each reconstruction kernel and train a multipath cycle GAN. To evaluate the effect of harmonization on the reconstruction kernels, we harmonize 50 scans each from Siemens hard kernel, GE soft kernel and GE hard kernel to a reference Siemens soft kernel (B30f) and evaluate percent emphysema. We fit a linear model by considering the age, smoking status, sex and vendor and perform an analysis of variance (ANOVA) on the emphysema scores. Our approach minimizes differences in emphysema measurement and highlights the impact of age, sex, smoking status and vendor on emphysema quantification.","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12926 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11392419/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302986","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}

引用次数: 0

Identification of functional white matter networks in BOLD fMRI. 识别 BOLD fMRI 白质功能网络

Proceedings of SPIE--the International Society for Optical Engineering Pub Date : 2024-02-01 Epub Date: 2024-04-02 DOI: 10.1117/12.3006231

Alexa L Eby, Lucas W Remedios, Michael E Kim, Muwei Li, Yurui Gao, John C Gore, Kurt G Schilling, Bennett A Landman

{"title":"Identification of functional white matter networks in BOLD fMRI.","authors":"Alexa L Eby, Lucas W Remedios, Michael E Kim, Muwei Li, Yurui Gao, John C Gore, Kurt G Schilling, Bennett A Landman","doi":"10.1117/12.3006231","DOIUrl":"10.1117/12.3006231","url":null,"abstract":"White matter signals in resting state blood oxygen level dependent functional magnetic resonance (BOLD-fMRI) have been largely discounted, yet there is growing evidence that these signals are indicative of brain activity. Understanding how these white matter signals capture function can provide insight into brain physiology. Moreover, functional signals could potentially be used as early markers for neurological changes, such as in Alzheimer's Disease. To investigate white matter brain networks, we leveraged the OASIS-3 dataset to extract white matter signals from resting state BOLD-FMRI data on 711 subjects. The imaging was longitudinal with a total of 2,026 images. Hierarchical clustering was performed to investigate clusters of voxel-level correlations on the timeseries data. The stability of clusters was measured with the average Dice coefficients on two different cross fold validations. The first validated the stability between scans, and the second validated the stability between populations. Functional clusters at hierarchical levels 4, 9, 13, 18, and 24 had local maximum stability, suggesting better clustered white matter. In comparison with JHU-DTI-SS Type-I Atlas defined regions, clusters at lower hierarchical levels identified well-defined anatomical lobes. At higher hierarchical levels, functional clusters mapped motor and memory functional regions, identifying 50.00%, 20.00%, 27.27%, and 35.14% of the frontal, occipital, parietal, and temporal lobe regions respectively.","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12926 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11364407/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142115750","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}

引用次数: 0

Nonlinear Gradient Field Estimation in Diffusion MRI Tensor Simulation. 弥散 MRI 张量模拟中的非线性梯度场估计

Proceedings of SPIE--the International Society for Optical Engineering Pub Date : 2024-02-01 Epub Date: 2024-04-02 DOI: 10.1117/12.3005364

Praitayini Kanakaraj, Tianyuan Yao, Nancy R Newlin, Leon Y Cai, Kurt G Schilling, Baxter P Rogers, Adam Anderson, Daniel Moyer, Bennett A Landman

{"title":"Nonlinear Gradient Field Estimation in Diffusion MRI Tensor Simulation.","authors":"Praitayini Kanakaraj, Tianyuan Yao, Nancy R Newlin, Leon Y Cai, Kurt G Schilling, Baxter P Rogers, Adam Anderson, Daniel Moyer, Bennett A Landman","doi":"10.1117/12.3005364","DOIUrl":"10.1117/12.3005364","url":null,"abstract":"Gradient nonlinearities not only induce spatial distortion in magnetic resonance imaging (MRI), but also introduce discrepancies between intended and acquired diffusion sensitization in diffusion weighted (DW) MRI. Advances in scanner performance have increased the importance of correcting gradient nonlinearities. The most common approaches for gradient nonlinear field estimations rely on phantom calibration field maps which are not always feasible, especially on retrospective data. Here, we derive a quadratic minimization problem for the complete gradient nonlinear field (L(r)). This approach starts with corrupt diffusion signal and estimates the L(r) in two scenarios: (1) the true diffusion tensor known and (2) the true diffusion tensor unknown (i.e., diffusion tensor is estimated). We show the validity of this mathematical approach, both theoretically and through tensor simulation. The estimated field is assessed through diffusion tensor metrics: mean diffusivity (MD), fractional anisotropy (FA), and principal eigenvector (V1). In simulation with 300 diffusion tensors, the study shows the mathematical model is not ill-posed and remains stable. We find when the true diffusion tensor is known (1) the change in determinant of the estimated L(r) field and the true field is near zero and (2) the median difference in estimated L(r) corrected diffusion metrics to true values is near zero. We find the results of L(r) estimation are dependent on the level of L(r) corruption. This work provides an approach to estimate gradient field without the need for additional calibration scans. To the best of our knowledge, the mathematical derivation presented here is novel.","PeriodicalId":74505,"journal":{"name":"Proceedings of SPIE--the International Society for Optical Engineering","volume":"12925 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11364409/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142115746","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}

引用次数: 0