Pub Date : 2008-06-13DOI: 10.1109/ISBI.2008.4541186
D. Kandaswamy, T. Blu, L. Spinelli, C. Michel, D. Ville
Source localization from EEG surface measurements is an important problem in neuro-imaging. We propose a new mathematical framework to estimate the parameters of a multi- dipole source model. To that aim, we perform 2-D analytic sensing in multiple planes. The estimation of the projection on each plane of the dipoles' positions, which is a non-linear problem, is reduced to polynomial root finding. The 3-D information is then recovered as a special case of tomographic reconstruction. The feasibility of the proposed approach is shown for both synthetic and experimental data.
{"title":"EEG source localization by multi-planar analytic sensing","authors":"D. Kandaswamy, T. Blu, L. Spinelli, C. Michel, D. Ville","doi":"10.1109/ISBI.2008.4541186","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541186","url":null,"abstract":"Source localization from EEG surface measurements is an important problem in neuro-imaging. We propose a new mathematical framework to estimate the parameters of a multi- dipole source model. To that aim, we perform 2-D analytic sensing in multiple planes. The estimation of the projection on each plane of the dipoles' positions, which is a non-linear problem, is reduced to polynomial root finding. The 3-D information is then recovered as a special case of tomographic reconstruction. The feasibility of the proposed approach is shown for both synthetic and experimental data.","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":"2008-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116998174","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 : 2008-05-29DOI: 10.1109/ISBI.2008.4541116
M. Jolly, L. Grady
This paper describes a general purpose algorithm to segment any kind of lesions in CT images. The algorithm expects a click or a stroke inside the lesion from the user and learns gray level properties on the fly. It then uses the random walker algorithm and combines multiple 2D segmentation results to produce the final 3D segmentation of the lesion. Quantitative evaluation on 293 lesions demonstrates that the method is ready for clinical use.
{"title":"3D general lesion segmentation in CT","authors":"M. Jolly, L. Grady","doi":"10.1109/ISBI.2008.4541116","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541116","url":null,"abstract":"This paper describes a general purpose algorithm to segment any kind of lesions in CT images. The algorithm expects a click or a stroke inside the lesion from the user and learns gray level properties on the fly. It then uses the random walker algorithm and combines multiple 2D segmentation results to produce the final 3D segmentation of the lesion. Quantitative evaluation on 293 lesions demonstrates that the method is ready for clinical use.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128675305","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.2008.4540933
J. Souplet, C. Lebrun, N. Ayache, G. Malandain
In multiple sclerosis (MS) research, burden of disease and treatments efficacy are mainly evaluated with lesion load and atrophy. The former being poorly correlated with patient's handicap, it is of interest to evaluate accurately the latter. A lot of methods to measure the brain atrophy are available in the literature. The brain parenchymal fraction (BPF) is one of these methods. It needs a precise segmentation of the brain and of the cerebro-spinal fluid. However, artefacts like partial volume effects (PVE) can impair this classification. According to some articles, the BPF may also be less precise in longitudinal studies. To address these points, this article proposes a new method to evaluate the BPF which is based on an expectation-minimization framework taking into consideration the PVE. Modifications of the workflow are also proposed to improve its reliability in longitudinal study. Experiments have been conducted on simulated pathological images that validate the different measures.
{"title":"A new evaluation of the brain parenchymal fraction: Application in multiple sclerosis longitudinal studies","authors":"J. Souplet, C. Lebrun, N. Ayache, G. Malandain","doi":"10.1109/ISBI.2008.4540933","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4540933","url":null,"abstract":"In multiple sclerosis (MS) research, burden of disease and treatments efficacy are mainly evaluated with lesion load and atrophy. The former being poorly correlated with patient's handicap, it is of interest to evaluate accurately the latter. A lot of methods to measure the brain atrophy are available in the literature. The brain parenchymal fraction (BPF) is one of these methods. It needs a precise segmentation of the brain and of the cerebro-spinal fluid. However, artefacts like partial volume effects (PVE) can impair this classification. According to some articles, the BPF may also be less precise in longitudinal studies. To address these points, this article proposes a new method to evaluate the BPF which is based on an expectation-minimization framework taking into consideration the PVE. Modifications of the workflow are also proposed to improve its reliability in longitudinal study. Experiments have been conducted on simulated pathological images that validate the different measures.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"8 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":"115448944","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.2008.4541325
Z. Sun, D. Rivière, E. Duchesnay, B. Thirion, F. Poupon, J. F. Mangin
The cortical folding patterns are very different from one individual to another. Here we try to find folding patterns automatically using large-scale datasets by non-supervised clustering analysis. The sulci of each brain are detected and identified using the brain VIS A open software. The 3D moment invariants are calculated and used as the shape descriptors of the sulci identified. A partial clustering algorithm using bootstrap sampling and bagging (PCBB) is devised for cortical pattern mining. Partial clusters are found using a modified hierarchical clustering method constrained by an objective function which looks for the most compact and dissimilar clusters. Bagging is used to increase stability. Experiments on simulated and real datasets are used to demonstrate the strength and stability of this algorithm compared to other standard approaches. Some cortical patterns are found using our method. In particular, the patterns found for the left cingulate sulcus are consistent with the patterns described in the atlas of Ono.
{"title":"Defining cortical sulcus patterns using partial clustering based on bootstrap and bagging","authors":"Z. Sun, D. Rivière, E. Duchesnay, B. Thirion, F. Poupon, J. F. Mangin","doi":"10.1109/ISBI.2008.4541325","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541325","url":null,"abstract":"The cortical folding patterns are very different from one individual to another. Here we try to find folding patterns automatically using large-scale datasets by non-supervised clustering analysis. The sulci of each brain are detected and identified using the brain VIS A open software. The 3D moment invariants are calculated and used as the shape descriptors of the sulci identified. A partial clustering algorithm using bootstrap sampling and bagging (PCBB) is devised for cortical pattern mining. Partial clusters are found using a modified hierarchical clustering method constrained by an objective function which looks for the most compact and dissimilar clusters. Bagging is used to increase stability. Experiments on simulated and real datasets are used to demonstrate the strength and stability of this algorithm compared to other standard approaches. Some cortical patterns are found using our method. In particular, the patterns found for the left cingulate sulcus are consistent with the patterns described in the atlas of Ono.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"1 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":"127209488","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.2008.4541012
R. Mangoubi, Mukund Desai, N. Lowry, P. Sammak
We apply texture image analysis to automated classification of stem cell nuclei, based on the observation that chromatin in human embryonic stem cells becomes more granular during differentiation. Using known probability models for texture multiresolution decompositions, we derive likelihood ratio test statistics. We also derive the probability density functions of these non-Gaussian statistics and use them to evaluate the performance of the classification test. Results indicate that the test can distinguish with probability 0.95 between nuclei that are pluripotent and those with varying degrees of differentiation. The test recognizes nuclei with similar differentiation level even if prior information says the contrary. This approach should be useful for classifying genome-wide epigenetic changes and chromatin remodeling during human development. Finally, the test statistics and their density functions are applicable to a general texture classification problem.
{"title":"Performance evaluation of multiresolution texture analysis of stem cell chromatin","authors":"R. Mangoubi, Mukund Desai, N. Lowry, P. Sammak","doi":"10.1109/ISBI.2008.4541012","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541012","url":null,"abstract":"We apply texture image analysis to automated classification of stem cell nuclei, based on the observation that chromatin in human embryonic stem cells becomes more granular during differentiation. Using known probability models for texture multiresolution decompositions, we derive likelihood ratio test statistics. We also derive the probability density functions of these non-Gaussian statistics and use them to evaluate the performance of the classification test. Results indicate that the test can distinguish with probability 0.95 between nuclei that are pluripotent and those with varying degrees of differentiation. The test recognizes nuclei with similar differentiation level even if prior information says the contrary. This approach should be useful for classifying genome-wide epigenetic changes and chromatin remodeling during human development. Finally, the test statistics and their density functions are applicable to a general texture classification problem.","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":"126129278","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.2008.4541282
S. Takerkart, Romain Fenouil, Jérome Piovano, A. Reynaud, L. Hoffart, F. Chavane, T. Papadopoulo, J. Conrath, G. Masson
We describe an image processing framework designed to detect and quantify the genesis of microscopic choroidal blood vessels. We used fluorescein angiography to monitor the dynamic of neo-vascularization of the retina after inducing lesions with a calibrated laser pulse. The angiogenesis can be revealed by an increase in the overall fluorescence level and/or diffusion size of the lesion. The proposed framework allows measuring both features from mis-aligned angiograms acquired with different gains and contrasts. It consists in aligning all the images, homogenizing their intensity characteristics and segmenting the lesions. In particular, we implemented a level set segmentation algorithm to delineate the diffusion area. We show that our framework allows detecting neo-vascularization when one of these features changes by less than 10%.
{"title":"A quantification framework for post-lesion neo-vascularization in retinal angiography","authors":"S. Takerkart, Romain Fenouil, Jérome Piovano, A. Reynaud, L. Hoffart, F. Chavane, T. Papadopoulo, J. Conrath, G. Masson","doi":"10.1109/ISBI.2008.4541282","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541282","url":null,"abstract":"We describe an image processing framework designed to detect and quantify the genesis of microscopic choroidal blood vessels. We used fluorescein angiography to monitor the dynamic of neo-vascularization of the retina after inducing lesions with a calibrated laser pulse. The angiogenesis can be revealed by an increase in the overall fluorescence level and/or diffusion size of the lesion. The proposed framework allows measuring both features from mis-aligned angiograms acquired with different gains and contrasts. It consists in aligning all the images, homogenizing their intensity characteristics and segmenting the lesions. In particular, we implemented a level set segmentation algorithm to delineate the diffusion area. We show that our framework allows detecting neo-vascularization when one of these features changes by less than 10%.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"273 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":"123322004","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.2008.4541109
R. Zdunek, Zhaoshui He, A. Cichocki
In tomographic image reconstruction from limited-view projections the underlying inverse problem is ill-posed with the rank-deficient system matrix. The minimal-norm least squares solution may considerably differs from the true solution, and hence a priori knowledge is needed to improve the reconstruction. In our approach, we assume that the true image presents sparse features with uniform spacial smoothness. The sparsity constraints are imposed with the lscrp diversity measure that is minimized with the FOCUSS algorithm. The spacial smoothness is enforced with the adaptive Wiener noise removing implemented in each FOCUSS iterations. The simulation results demonstrate the benefits of the proposed approach.
{"title":"Tomographic image reconstruction from limited-view projections with Wiener filtered focuss algorithm","authors":"R. Zdunek, Zhaoshui He, A. Cichocki","doi":"10.1109/ISBI.2008.4541109","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541109","url":null,"abstract":"In tomographic image reconstruction from limited-view projections the underlying inverse problem is ill-posed with the rank-deficient system matrix. The minimal-norm least squares solution may considerably differs from the true solution, and hence a priori knowledge is needed to improve the reconstruction. In our approach, we assume that the true image presents sparse features with uniform spacial smoothness. The sparsity constraints are imposed with the lscrp diversity measure that is minimized with the FOCUSS algorithm. The spacial smoothness is enforced with the adaptive Wiener noise removing implemented in each FOCUSS iterations. The simulation results demonstrate the benefits of the proposed approach.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"37 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":"126648887","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.2008.4541090
Orly Zvitia, Arnaldo Mayer, H. Greenspan
In this paper we present a robust approach to the registration of white matter tractographies extracted from DT-MRI scans. The fibers are projected into a high dimensional feature space defined by the sequence of their 3D coordinates. Adaptive mean-shift (AMS) clustering is applied to extract a compact set of representative fiber-modes (FM). Each FM is assigned to a multivariate Gaussian distribution according to its population thereby leading to a Mixture of Gaussians (MoG) representation for the entire set of fibers. The registration between two fiber sets is treated as the alignment of two MoGs and is performed by maximizing their correlation ratio. A 9 parameter affine transform is recovered and eventually refined to a 12 parameters affine transform using an innovative mean-shift (MS) based registration refinement scheme presented in this paper. The validation of the algorithm on intra-subject data demonstrates its robustness against two main tractography artifacts: interrupted and deviating fiber tracts.
{"title":"Adaptive mean-shift registration of white matter tractographies","authors":"Orly Zvitia, Arnaldo Mayer, H. Greenspan","doi":"10.1109/ISBI.2008.4541090","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541090","url":null,"abstract":"In this paper we present a robust approach to the registration of white matter tractographies extracted from DT-MRI scans. The fibers are projected into a high dimensional feature space defined by the sequence of their 3D coordinates. Adaptive mean-shift (AMS) clustering is applied to extract a compact set of representative fiber-modes (FM). Each FM is assigned to a multivariate Gaussian distribution according to its population thereby leading to a Mixture of Gaussians (MoG) representation for the entire set of fibers. The registration between two fiber sets is treated as the alignment of two MoGs and is performed by maximizing their correlation ratio. A 9 parameter affine transform is recovered and eventually refined to a 12 parameters affine transform using an innovative mean-shift (MS) based registration refinement scheme presented in this paper. The validation of the algorithm on intra-subject data demonstrates its robustness against two main tractography artifacts: interrupted and deviating fiber tracts.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"4 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":"127023084","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.2008.4540934
N. Makni, P. Puech, Renaud Lopes, A. Dewalle-Vignion, O. Colot, N. Betrouni
This paper introduces an original method for automatic 3D segmentation of the prostate gland from Magnetic Resonance Imaging data. A statistical geometric model is used as a priori knowledge. Prostate boundaries are then optimized by a Bayesian classification based on Markov fields modelling. We compared the accuracy of this algorithm, free from any manual correction, with contours outlined by an expert radiologist. In 3 random cases, including prostates with cancer and benign prostatic hypertrophy (BPH), mean Hausdorff s distance (HD) and overlap ration (OR) were 8.07 mm and 0.82, respectively. Despite fast computing times, this new method showed satisfying results, even at prostate base and apex. Also, we believe that this approach may allow delineating the peripheral zone (PZ) and the transition zone (TZ) within the gland in a near future.
{"title":"Toward automatic zonal segmentation of prostate by combining a deformable model and a probabilistic framework","authors":"N. Makni, P. Puech, Renaud Lopes, A. Dewalle-Vignion, O. Colot, N. Betrouni","doi":"10.1109/ISBI.2008.4540934","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4540934","url":null,"abstract":"This paper introduces an original method for automatic 3D segmentation of the prostate gland from Magnetic Resonance Imaging data. A statistical geometric model is used as a priori knowledge. Prostate boundaries are then optimized by a Bayesian classification based on Markov fields modelling. We compared the accuracy of this algorithm, free from any manual correction, with contours outlined by an expert radiologist. In 3 random cases, including prostates with cancer and benign prostatic hypertrophy (BPH), mean Hausdorff s distance (HD) and overlap ration (OR) were 8.07 mm and 0.82, respectively. Despite fast computing times, this new method showed satisfying results, even at prostate base and apex. Also, we believe that this approach may allow delineating the peripheral zone (PZ) and the transition zone (TZ) within the gland in a near future.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"117 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":"115183875","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.2008.4541213
P. Andrey, E. Maschino, Y. Maurin
In neuroanatomical studies, the specimens are generally cut into serial sections that are processed to reveal the elements of interest. The third dimension lost during sectioning can be recovered by reconstructing three-dimensional graphical models of the studied structures. To reach statistical significance and to compare results from distinct experiments, data from different models must be combined into common representations. Due to biological and experimental variability, this requires a non-linear spatial normalisation step. In this paper, an algorithm is presented to normalise and map data into average models. The usefulness of the approach for elucidating spatial organisations in the nervous system is illustrated on rat neuroanatomical data.
{"title":"Spatial normalisation of three-dimensional neuroanatomical models using shape registration, averaging, and warping","authors":"P. Andrey, E. Maschino, Y. Maurin","doi":"10.1109/ISBI.2008.4541213","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541213","url":null,"abstract":"In neuroanatomical studies, the specimens are generally cut into serial sections that are processed to reveal the elements of interest. The third dimension lost during sectioning can be recovered by reconstructing three-dimensional graphical models of the studied structures. To reach statistical significance and to compare results from distinct experiments, data from different models must be combined into common representations. Due to biological and experimental variability, this requires a non-linear spatial normalisation step. In this paper, an algorithm is presented to normalise and map data into average models. The usefulness of the approach for elucidating spatial organisations in the nervous system is illustrated on rat neuroanatomical data.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"167 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":"122350033","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}
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