Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541154
Yu Bai, D. Alexander
In diffusion tensor MRI, a number of diffusion-weighted images with different diffusion-weighting gradient directions are acquired during scanning. The tensor calculation assumes that each voxel corresponds to the same anatomical location in all the measurements. Movements and distortions violate this assumption and typically the images are realigned before model fitting. The traditional method uses a non-diffusion- weighted image as the reference for registration, but the differences between diffusion-weighted images and the non- diffusion weighted reference image can cause mismatching to occur during registration, even using metrics like the mutual information (MI) that accounts for non-linear contrast differences. We propose alternative model-based methods to improve motion correction and avoid the errors that the traditional method introduces. We demonstrate quantitative improvements using the new approaches on a full data with slight, but typical, movement during acquisition.
{"title":"Model-based registration to correct for motion between acquisitions in diffusion MR imaging","authors":"Yu Bai, D. Alexander","doi":"10.1109/ISBI.2008.4541154","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541154","url":null,"abstract":"In diffusion tensor MRI, a number of diffusion-weighted images with different diffusion-weighting gradient directions are acquired during scanning. The tensor calculation assumes that each voxel corresponds to the same anatomical location in all the measurements. Movements and distortions violate this assumption and typically the images are realigned before model fitting. The traditional method uses a non-diffusion- weighted image as the reference for registration, but the differences between diffusion-weighted images and the non- diffusion weighted reference image can cause mismatching to occur during registration, even using metrics like the mutual information (MI) that accounts for non-linear contrast differences. We propose alternative model-based methods to improve motion correction and avoid the errors that the traditional method introduces. We demonstrate quantitative improvements using the new approaches on a full data with slight, but typical, movement during acquisition.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115490768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541237
Xiaoguang Lu, B. Georgescu, Yefeng Zheng, Joanne Otsuki, D. Comaniciu
3D echocardiography is one of the emerging real-time imaging modalities that is increasingly used in clinical practice to assess cardiac functions. It provides a more complete heart representation for evaluation in comparison to conventional 2D echocardiography. However, one of the drawbacks is the time it takes clinicians to navigate the 3D volumes to the anatomy of interest and to obtain standardized views that are similar to the 2D acquisitions. We propose an automated supervised learning method to detect standard multiplanar reformatted planes (MPRs) from a 3D echocardiographic volume. Extensive evaluations on a database of 326 volumes show performance comparable to intra-user variability and the execution time of the algorithm is about 2 seconds.
{"title":"AutoMPR: Automatic detection of standard planes in 3D echocardiography","authors":"Xiaoguang Lu, B. Georgescu, Yefeng Zheng, Joanne Otsuki, D. Comaniciu","doi":"10.1109/ISBI.2008.4541237","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541237","url":null,"abstract":"3D echocardiography is one of the emerging real-time imaging modalities that is increasingly used in clinical practice to assess cardiac functions. It provides a more complete heart representation for evaluation in comparison to conventional 2D echocardiography. However, one of the drawbacks is the time it takes clinicians to navigate the 3D volumes to the anatomy of interest and to obtain standardized views that are similar to the 2D acquisitions. We propose an automated supervised learning method to detect standard multiplanar reformatted planes (MPRs) from a 3D echocardiographic volume. Extensive evaluations on a database of 326 volumes show performance comparable to intra-user variability and the execution time of the algorithm is about 2 seconds.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124465973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541016
C. Seelamantula, R. Michaely, R. Leitgeb, M. Unser
New phase-shifting techniques have recently been proposed to suppress the complex-conjugate ambiguity in frequency- domain optical-coherence tomography. A phase shift is introduced, in an elegant fashion, by incorporating a small beam offset at the scanning mirror. The tomogram is then computed by using a combination of Hilbert and Fourier transforms. This is a marked deviation from the conventional approaches, wherein each A-scan is reconstructed independently of the others. In this paper, we formulate the problem in a signal processing framework and provide theoretical proofs for maximal and partial suppression of complex-conjugate ambiguity. To supplement the theoretical derivations, we provide experimental results on in vivo measurements of a human finger nail.
{"title":"Theoretical analysis of complex-conjugate-ambiguity suppression in frequency-domain optical-coherence tomography","authors":"C. Seelamantula, R. Michaely, R. Leitgeb, M. Unser","doi":"10.1109/ISBI.2008.4541016","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541016","url":null,"abstract":"New phase-shifting techniques have recently been proposed to suppress the complex-conjugate ambiguity in frequency- domain optical-coherence tomography. A phase shift is introduced, in an elegant fashion, by incorporating a small beam offset at the scanning mirror. The tomogram is then computed by using a combination of Hilbert and Fourier transforms. This is a marked deviation from the conventional approaches, wherein each A-scan is reconstructed independently of the others. In this paper, we formulate the problem in a signal processing framework and provide theoretical proofs for maximal and partial suppression of complex-conjugate ambiguity. To supplement the theoretical derivations, we provide experimental results on in vivo measurements of a human finger nail.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122954944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541128
T. Pécot, C. Kervrann, P. Bouthemy
Green Fluorescent Protein (GFP)-tagging and time-lapse fluorescence microscopy enable to observe molecular dynamics and interactions in live cells. Original image analysis methods are then required to process challenging 2D or 3D image sequences. To address the tracking problem of several hundreds of objects, we propose an original framework that provides general information about vesicle transport, that is traffic flows between origin and destination regions detected in the image sequence. Traffic estimation can be accomplished by adapting the advances in Network Tomography commonly used in network communications. In this paper, we address image partition given vesicle stocking areas and multipaths routing for vesicle transport. This approach has been developed for real fluorescence image sequences and Rab proteins.
{"title":"Minimal paths and probabilistic models for origin-destination traffic estimation in live cell imaging","authors":"T. Pécot, C. Kervrann, P. Bouthemy","doi":"10.1109/ISBI.2008.4541128","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541128","url":null,"abstract":"Green Fluorescent Protein (GFP)-tagging and time-lapse fluorescence microscopy enable to observe molecular dynamics and interactions in live cells. Original image analysis methods are then required to process challenging 2D or 3D image sequences. To address the tracking problem of several hundreds of objects, we propose an original framework that provides general information about vesicle transport, that is traffic flows between origin and destination regions detected in the image sequence. Traffic estimation can be accomplished by adapting the advances in Network Tomography commonly used in network communications. In this paper, we address image partition given vesicle stocking areas and multipaths routing for vesicle transport. This approach has been developed for real fluorescence image sequences and Rab proteins.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122946934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541007
J. Angulo, B. Schaack
In fluorescence-labelled cell assays for high content screening applications, image processing software is necessary to have automatic algorithms for segmenting the cells individually and for quantifying their intensities, size/shape parameters, etc. Mathematical morphology is a non-linear image processing technique which is proven to be a very powerful tool in biomedical microscopy image analysis. This paper presents a morphological methodology based on connected filters, watershed transformation and granulometries for segmenting cells of different size, contrast, etc. In particular, the performance of the algorithms is illustrated with cell images from a toxicity assay in three-labels (Hoechst, EGFP, Phalloi'din) on nanodrops cell-on-chip format.
{"title":"Morphological-based adaptive segmentation and quantification of cell assays in high content screening","authors":"J. Angulo, B. Schaack","doi":"10.1109/ISBI.2008.4541007","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541007","url":null,"abstract":"In fluorescence-labelled cell assays for high content screening applications, image processing software is necessary to have automatic algorithms for segmenting the cells individually and for quantifying their intensities, size/shape parameters, etc. Mathematical morphology is a non-linear image processing technique which is proven to be a very powerful tool in biomedical microscopy image analysis. This paper presents a morphological methodology based on connected filters, watershed transformation and granulometries for segmenting cells of different size, contrast, etc. In particular, the performance of the algorithms is illustrated with cell images from a toxicity assay in three-labels (Hoechst, EGFP, Phalloi'din) on nanodrops cell-on-chip format.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123625214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541239
Lin Du, Jian Li, P. Stoica
Delay-and-sum (DAS) beamforming is the standard technique for ultrasound imaging applications. Due to its data independent property, DAS may suffer from poorer resolution and worse interference suppression capability than the adaptive standard Capon beamformer (SCB). However, the performance of SCB is sensitive to the errors in the sample covariance matrix and the signal steering vector. Therefore, robust adaptive beamforming techniques are desirable. In this paper, we consider ultrasound imaging via applying a user parameter free robust adaptive beamformer, which uses a shrinkage-based general linear combination (QLC) algorithm to obtain an enhanced estimate of the array covariance matrix. We present several multistatic adaptive ultrasound imaging (MAUI) approaches based on QLC to achieve high resolution and good interference suppression capability. The performance of the proposed MAUI approaches is demonstrated via an experimental example.
{"title":"User parameter free approaches to multistatic adaptive ultrasound imaging","authors":"Lin Du, Jian Li, P. Stoica","doi":"10.1109/ISBI.2008.4541239","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541239","url":null,"abstract":"Delay-and-sum (DAS) beamforming is the standard technique for ultrasound imaging applications. Due to its data independent property, DAS may suffer from poorer resolution and worse interference suppression capability than the adaptive standard Capon beamformer (SCB). However, the performance of SCB is sensitive to the errors in the sample covariance matrix and the signal steering vector. Therefore, robust adaptive beamforming techniques are desirable. In this paper, we consider ultrasound imaging via applying a user parameter free robust adaptive beamformer, which uses a shrinkage-based general linear combination (QLC) algorithm to obtain an enhanced estimate of the array covariance matrix. We present several multistatic adaptive ultrasound imaging (MAUI) approaches based on QLC to achieve high resolution and good interference suppression capability. The performance of the proposed MAUI approaches is demonstrated via an experimental example.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121899274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541072
Chia Chi Teng, L. Shapiro, R. Hopper, J. V. Halen
Craniosynostosis is a congenital disease which consists of premature fusion of one or more cranial sutures, resulting in an abnormal head shape. Patients are usually treated by cranial vault expansion surgery to minimize the potential for brain damage. Full thickness cranial defects result from the expansion surgery, with the size directly proportional to the degree of expansion. The growing cranial skeleton has a unique regenerative capacity to heal small defects; however, when this regenerative capacity is exceeded, the defect is classed as one of critical size and requires surgical treatment to restore protection to the underlying brain. Although what constitutes a critical cranial defect is well known in animal models, it is not as clear for pediatric human skulls. The purpose of this study is to investigate a method that can effectively quantify healing of the pediatric cranial defect surface after cranial vault expansion surgery for craniosynostosis.
{"title":"Pediatric cranial defect surface analysis for craniosynostosis postoperation CT images","authors":"Chia Chi Teng, L. Shapiro, R. Hopper, J. V. Halen","doi":"10.1109/ISBI.2008.4541072","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541072","url":null,"abstract":"Craniosynostosis is a congenital disease which consists of premature fusion of one or more cranial sutures, resulting in an abnormal head shape. Patients are usually treated by cranial vault expansion surgery to minimize the potential for brain damage. Full thickness cranial defects result from the expansion surgery, with the size directly proportional to the degree of expansion. The growing cranial skeleton has a unique regenerative capacity to heal small defects; however, when this regenerative capacity is exceeded, the defect is classed as one of critical size and requires surgical treatment to restore protection to the underlying brain. Although what constitutes a critical cranial defect is well known in animal models, it is not as clear for pediatric human skulls. The purpose of this study is to investigate a method that can effectively quantify healing of the pediatric cranial defect surface after cranial vault expansion surgery for craniosynostosis.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122898371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4540974
Walter Sun, M. Çetin, R. Chan, A. Willsky
We propose a method for recursive segmentation of the left ventricle (LV) across a temporal sequence of magnetic resonance (MR) images. The approach involves a technique for learning the LV boundary dynamics together with a particle-based inference algorithm on a loopy graphical model capturing the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation of the boundary, and boundary estimation involves incorporating curve evolution into state estimation. By formulating the problem as one of state estimation, the segmentation at each particular time is based not only on the data observed at that instant, but also on predictions based on past and future boundary estimates. We assess and demonstrate the effectiveness of the proposed framework on a large data set of breath-hold cardiac MR image sequences.
{"title":"Segmentation of the evolving left ventricle by learning the dynamics","authors":"Walter Sun, M. Çetin, R. Chan, A. Willsky","doi":"10.1109/ISBI.2008.4540974","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4540974","url":null,"abstract":"We propose a method for recursive segmentation of the left ventricle (LV) across a temporal sequence of magnetic resonance (MR) images. The approach involves a technique for learning the LV boundary dynamics together with a particle-based inference algorithm on a loopy graphical model capturing the temporal periodicity of the heart. The dynamic system state is a low-dimensional representation of the boundary, and boundary estimation involves incorporating curve evolution into state estimation. By formulating the problem as one of state estimation, the segmentation at each particular time is based not only on the data observed at that instant, but also on predictions based on past and future boundary estimates. We assess and demonstrate the effectiveness of the proposed framework on a large data set of breath-hold cardiac MR image sequences.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125201218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541242
M. Al-Mistarihi
We present a new algorithm of post-beamforming second order Volterra filter (SOVF) for deriving the quadratic kernel based on the convolution of the singular modes quadratic kernels which give the highest contrast-to-tissue ratio (CTRs) to extract quadratic components from ultrasound contrast agent (UCA) nonlinear echoes with single transmission. The new algorithm leads to reduction of tissue component and increase the specificity while optimizing the sensitivity to the UCA. The algorithm is demonstrated experimentally using images from in vivo kidney after bolus injection with UCA. Illustrative images of the kidney of a juvenile pig were obtained before and after infusion of contrast agent (SonoVue, Bracco, Geneva, Switzerland) at various concentrations. The imaging results given in this paper indicate that a signal processing approach to this clinical challenge is feasible.
{"title":"Optimization of contrast sensitivity and specificity of quadratic ultrasonic imaging","authors":"M. Al-Mistarihi","doi":"10.1109/ISBI.2008.4541242","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541242","url":null,"abstract":"We present a new algorithm of post-beamforming second order Volterra filter (SOVF) for deriving the quadratic kernel based on the convolution of the singular modes quadratic kernels which give the highest contrast-to-tissue ratio (CTRs) to extract quadratic components from ultrasound contrast agent (UCA) nonlinear echoes with single transmission. The new algorithm leads to reduction of tissue component and increase the specificity while optimizing the sensitivity to the UCA. The algorithm is demonstrated experimentally using images from in vivo kidney after bolus injection with UCA. Illustrative images of the kidney of a juvenile pig were obtained before and after infusion of contrast agent (SonoVue, Bracco, Geneva, Switzerland) at various concentrations. The imaging results given in this paper indicate that a signal processing approach to this clinical challenge is feasible.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126313555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541226
B. Cheung, B. V. Veen
A new method for estimating multivariate autoregressive (MVAR) models of cortical connectivity from surface EEG or MEG measurements is presented. Conventional approaches to this problem first attempt to solve the inverse problem to estimate cortical signals and then fit an MVAR model to the estimated signals. Our new approach expresses the measured data in tens of a hidden state equation describing MVAR cortical signal evolution and an observation equation that relates the hidden state to the surface measurements. We develop an expectation-maximization (EM) algorithm to find maximum likelihood estimates of the MVAR model parameters. Simulations show that this one-step approach performs significantly better than the conventional two-step approach at estimating the cortical signals and detecting functional connectivity between different cortical regions.
{"title":"Estimation of cortical multivariate autoregressive models for EEG/MEG using an expectation-maximization algorithm","authors":"B. Cheung, B. V. Veen","doi":"10.1109/ISBI.2008.4541226","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541226","url":null,"abstract":"A new method for estimating multivariate autoregressive (MVAR) models of cortical connectivity from surface EEG or MEG measurements is presented. Conventional approaches to this problem first attempt to solve the inverse problem to estimate cortical signals and then fit an MVAR model to the estimated signals. Our new approach expresses the measured data in tens of a hidden state equation describing MVAR cortical signal evolution and an observation equation that relates the hidden state to the surface measurements. We develop an expectation-maximization (EM) algorithm to find maximum likelihood estimates of the MVAR model parameters. Simulations show that this one-step approach performs significantly better than the conventional two-step approach at estimating the cortical signals and detecting functional connectivity between different cortical regions.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126371926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：