Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193149
Ling Li, Xiaofeng Niu, Yongyi Yang
For the purpose of motion-compensated processing we propose a temporal modeling approach for determining the image motion in a gated cardiac sequence, wherein the inherent image motion is periodic over time. To exploit the periodic nature of the cardiac motion, we use a Fourier harmonic representation to describe the motion field for the entire sequence. We then determine the motion field by estimating the parameters of this representation model. This joint estimation approach can take advantage of the statistics of all the available image data in the sequence. In the experiments, we applied the proposed approach to motioncompensated 4D reconstruction of gated cardiac SPECT images. Our results demonstrate that it could achieve robust estimation of the motion field and lead to improved image reconstruction despite the presence of strong imaging noise.
{"title":"A periodic optical flow model for cardiac gated images","authors":"Ling Li, Xiaofeng Niu, Yongyi Yang","doi":"10.1109/ISBI.2009.5193149","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193149","url":null,"abstract":"For the purpose of motion-compensated processing we propose a temporal modeling approach for determining the image motion in a gated cardiac sequence, wherein the inherent image motion is periodic over time. To exploit the periodic nature of the cardiac motion, we use a Fourier harmonic representation to describe the motion field for the entire sequence. We then determine the motion field by estimating the parameters of this representation model. This joint estimation approach can take advantage of the statistics of all the available image data in the sequence. In the experiments, we applied the proposed approach to motioncompensated 4D reconstruction of gated cardiac SPECT images. Our results demonstrate that it could achieve robust estimation of the motion field and lead to improved image reconstruction despite the presence of strong imaging noise.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133575206","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 : 2009-06-28DOI: 10.1109/ISBI.2009.5193328
L. Zhan, A. Leow, Siwei Zhu, M. Chiang, M. Barysheva, A. Toga, K. Mcmahon, G. Zubicaray, M. Wright, P. Thompson
High-angular resolution diffusion imaging (HARDI) can reconstruct fiber pathways in the brain with extraordinary detail, identifying anatomical features and connections not seen with conventional MRI. HARDI overcomes several limitations of standard diffusion tensor imaging, which fails to model diffusion correctly in regions where fibers cross or mix. As HARDI can accurately resolve sharp signal peaks in angular space where fibers cross, we studied how many gradients are required in practice to compute accurate orientation density functions, to better understand the trade-off between longer scanning times and more angular precision. We computed orientation density functions analytically from tensor distribution functions (TDFs) which model the HARDI signal at each point as a unit-mass probability density on the 6D manifold of symmetric positive definite tensors. In simulated two-fiber systems with varying Rician noise, we assessed how many diffusion-sensitized gradients were sufficient to (1) accurately resolve the diffusion profile, and (2) measure the exponential isotropy (EI), a TDF-derived measure of fiber integrity that exploits the full multidirectional HARDI signal. At lower SNR, the reconstruction accuracy, measured using the Kullback-Leibler divergence, rapidly increased with additional gradients, and EI estimation accuracy plateaued at around 70 gradients.
{"title":"Analyzing multi-fiber reconstruction in high angular resolution diffusion imaging using the tensor distribution function","authors":"L. Zhan, A. Leow, Siwei Zhu, M. Chiang, M. Barysheva, A. Toga, K. Mcmahon, G. Zubicaray, M. Wright, P. Thompson","doi":"10.1109/ISBI.2009.5193328","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193328","url":null,"abstract":"High-angular resolution diffusion imaging (HARDI) can reconstruct fiber pathways in the brain with extraordinary detail, identifying anatomical features and connections not seen with conventional MRI. HARDI overcomes several limitations of standard diffusion tensor imaging, which fails to model diffusion correctly in regions where fibers cross or mix. As HARDI can accurately resolve sharp signal peaks in angular space where fibers cross, we studied how many gradients are required in practice to compute accurate orientation density functions, to better understand the trade-off between longer scanning times and more angular precision. We computed orientation density functions analytically from tensor distribution functions (TDFs) which model the HARDI signal at each point as a unit-mass probability density on the 6D manifold of symmetric positive definite tensors. In simulated two-fiber systems with varying Rician noise, we assessed how many diffusion-sensitized gradients were sufficient to (1) accurately resolve the diffusion profile, and (2) measure the exponential isotropy (EI), a TDF-derived measure of fiber integrity that exploits the full multidirectional HARDI signal. At lower SNR, the reconstruction accuracy, measured using the Kullback-Leibler divergence, rapidly increased with additional gradients, and EI estimation accuracy plateaued at around 70 gradients.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115202053","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 : 2009-06-28DOI: 10.1109/ISBI.2009.5193170
G. M. Faustino, M. Gattass, S. Rehen, C. Lucena
In this paper, we propose an automatic embryonic stem cell detection and counting method for fluorescence microscopy images. We handle with pluripotent stem cells cultured in vitro. Our approach uses the luminance information to generate a graph-based image representation. Next, a graph mining process is used to detect the cells. The proposed method was extensively tested on a database of 92 images and specialists validated the results. We obtained an average precision, recall and F-measure of 93.97%, 92.04% and 92.87%, respectively.
{"title":"Automatic embryonic stem cells detection and counting method in fluorescence microscopy images","authors":"G. M. Faustino, M. Gattass, S. Rehen, C. Lucena","doi":"10.1109/ISBI.2009.5193170","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193170","url":null,"abstract":"In this paper, we propose an automatic embryonic stem cell detection and counting method for fluorescence microscopy images. We handle with pluripotent stem cells cultured in vitro. Our approach uses the luminance information to generate a graph-based image representation. Next, a graph mining process is used to detect the cells. The proposed method was extensively tested on a database of 92 images and specialists validated the results. We obtained an average precision, recall and F-measure of 93.97%, 92.04% and 92.87%, respectively.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115416560","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 : 2009-06-28DOI: 10.1109/ISBI.2009.5193234
S. Ding, M. Miga, R. Thompson, B. Dawant
This paper proposes a new method designed to track operative microscope video images recorded during tumor resection neurosurgery. Two steps are involved in this method. The first uses feature vectors constructed from color information of video images and shape information of selected vessels to find homologous points in consecutive frames. The second uses smoothing thin-plate splines (TPS) to interpolate the transformation computed with the vessels over the entire image. This approach only requires several pairs of starting and ending points selected on segments of vessels in the first frame of a video sequence. Then, the proposed method tracks the identified vessels automatically, rapidly, and robustly, even when surgical instruments obscure parts of the image frames.
{"title":"Robust vessel registration and tracking of microscope video images in tumor resection neurosurgery","authors":"S. Ding, M. Miga, R. Thompson, B. Dawant","doi":"10.1109/ISBI.2009.5193234","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193234","url":null,"abstract":"This paper proposes a new method designed to track operative microscope video images recorded during tumor resection neurosurgery. Two steps are involved in this method. The first uses feature vectors constructed from color information of video images and shape information of selected vessels to find homologous points in consecutive frames. The second uses smoothing thin-plate splines (TPS) to interpolate the transformation computed with the vessels over the entire image. This approach only requires several pairs of starting and ending points selected on segments of vessels in the first frame of a video sequence. Then, the proposed method tracks the identified vessels automatically, rapidly, and robustly, even when surgical instruments obscure parts of the image frames.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"207 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115043050","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 : 2009-06-28DOI: 10.1109/ISBI.2009.5193264
E. Brusseau, O. Basset
In the context of ultrasound elastography, we recently developed a 2D strain estimation method to image the deformation of a medium during its compression with the probe. This technique was proved to provide good-quality strain images with data acquired in a freehand configuration on elastography-dedicated phantoms and ex vivo dog tissue lesions. In this study, the application to in vivo human benign tumors is presented. The initial results obtained demonstrate that our method is able to provide easily interpretable deformation images in clinical conditions.
{"title":"In vivo examination of human lipomas with freehand elastography - Preliminary results","authors":"E. Brusseau, O. Basset","doi":"10.1109/ISBI.2009.5193264","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193264","url":null,"abstract":"In the context of ultrasound elastography, we recently developed a 2D strain estimation method to image the deformation of a medium during its compression with the probe. This technique was proved to provide good-quality strain images with data acquired in a freehand configuration on elastography-dedicated phantoms and ex vivo dog tissue lesions. In this study, the application to in vivo human benign tumors is presented. The initial results obtained demonstrate that our method is able to provide easily interpretable deformation images in clinical conditions.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117300185","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 : 2009-06-28DOI: 10.1109/ISBI.2009.5193075
Wenjia Bai, M. Brady
Positron emission tomography (PET) is a molecular imaging technique which is now widely established as a powerful tool for diagnosing a variety of cancers. However, PET images are substantially degraded by respiratory motion to the extent that this may adversely impact upon subsequent diagnosis and patientmanagement. A spatio-temporal image registration algorithm is proposed to align the moving images and correct for motion. Compared to the conventional spatial registration, the proposed algorithm has the potential to yield more accurate motion. Experimental results show that motion correction using the spatio-temporal registration algorithm significantly improves the PET image quality.
{"title":"Spatio-temporal image registration for respiratory motion correction in PET imaging","authors":"Wenjia Bai, M. Brady","doi":"10.1109/ISBI.2009.5193075","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193075","url":null,"abstract":"Positron emission tomography (PET) is a molecular imaging technique which is now widely established as a powerful tool for diagnosing a variety of cancers. However, PET images are substantially degraded by respiratory motion to the extent that this may adversely impact upon subsequent diagnosis and patientmanagement. A spatio-temporal image registration algorithm is proposed to align the moving images and correct for motion. Compared to the conventional spatial registration, the proposed algorithm has the potential to yield more accurate motion. Experimental results show that motion correction using the spatio-temporal registration algorithm significantly improves the PET image quality.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123629345","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 : 2009-06-28DOI: 10.1109/ISBI.2009.5193117
S. Rajagopalan, R. Robb
Tissue engineering is an interdisciplinary effort aimed at the repair and regeneration of biological tissues through the application and control of cells, porous scaffolds and growth factors. While there is a general intuitive consensus on the influence of scaffold architecture on the regeneration of tissues, the specific magnitude of these architectural indices are loosely defined. This paper investigates the application of image-based metrology and traditional wet-lab studies to understand cellular responses and phenotype expression to scaffold microarchitecture. Unraveling the symbiotic structure-to-function relationship of scaffolds might have profound implications leading to the deployment of benchside tissue analogs to the clinical bedside.
{"title":"Image-based structure-to-function correlation of tissue engineering scaffolds","authors":"S. Rajagopalan, R. Robb","doi":"10.1109/ISBI.2009.5193117","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193117","url":null,"abstract":"Tissue engineering is an interdisciplinary effort aimed at the repair and regeneration of biological tissues through the application and control of cells, porous scaffolds and growth factors. While there is a general intuitive consensus on the influence of scaffold architecture on the regeneration of tissues, the specific magnitude of these architectural indices are loosely defined. This paper investigates the application of image-based metrology and traditional wet-lab studies to understand cellular responses and phenotype expression to scaffold microarchitecture. Unraveling the symbiotic structure-to-function relationship of scaffolds might have profound implications leading to the deployment of benchside tissue analogs to the clinical bedside.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123659803","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 : 2009-06-28DOI: 10.1109/ISBI.2009.5193306
R. Kumar, P. Rajan, Srdan Bejakovic, S. Seshamani, G. Mullin, T. Dassopoulos, Gregory Hager
Wireless capsule endoscopy (CE) is increasing being used to assess several gastrointestinal(GI) diseases and disorders. Current clinical methods are based on subjective evaluation of images. In this paper, we develop a method for ranking lesions appearing in CE images. This ranking is based on pairwise comparisons among representative images supplied by an expert. With such sparse pairwise rank information for a small number of images, we investigate methods for creating and evaluating global ranking functions. In experiments with CE images, we train statistical classifiers using color and edge feature descriptors extracted frommanually annotated regions of interest. Experiments on a data set using Crohn's disease lesions for lesion severity are presented with the developed ranking functions achieve high accuracy rates.
{"title":"Learning disease severity for capsule endoscopy images","authors":"R. Kumar, P. Rajan, Srdan Bejakovic, S. Seshamani, G. Mullin, T. Dassopoulos, Gregory Hager","doi":"10.1109/ISBI.2009.5193306","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193306","url":null,"abstract":"Wireless capsule endoscopy (CE) is increasing being used to assess several gastrointestinal(GI) diseases and disorders. Current clinical methods are based on subjective evaluation of images. In this paper, we develop a method for ranking lesions appearing in CE images. This ranking is based on pairwise comparisons among representative images supplied by an expert. With such sparse pairwise rank information for a small number of images, we investigate methods for creating and evaluating global ranking functions. In experiments with CE images, we train statistical classifiers using color and edge feature descriptors extracted frommanually annotated regions of interest. Experiments on a data set using Crohn's disease lesions for lesion severity are presented with the developed ranking functions achieve high accuracy rates.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123686129","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 : 2009-06-28DOI: 10.1109/ISBI.2009.5193067
Lili Zhou, S. Kulkarni, Bin Liu, G. Gindi
In systems like SPECT, raw data is obtained by the imaging system and then reconstructed and viewed by a human observer. We compare two approaches to optimizing SPECT for a detection task with a known signal in a statistically varying background. In a sequential approach, we optimize the collimator using an ideal observer applied to the sinogram. We then optimize the regularization of the reconstruction using a human-emulating channelized Hotelling observer (CHO). In a second approach, we use the CHO to jointly optimize the collimator and regularization. The performance of the joint approach exceeds that of the sequential approach. The collimator properties from the joint approach are closer to that of a commercial collimator than those of the sequential approach. Thus using the “best” collimator derived by an ideal observer leads to suboptimal net detection performance.
{"title":"Strategies to jointly optimize spect collimator and reconstruction parameters for a detection task","authors":"Lili Zhou, S. Kulkarni, Bin Liu, G. Gindi","doi":"10.1109/ISBI.2009.5193067","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193067","url":null,"abstract":"In systems like SPECT, raw data is obtained by the imaging system and then reconstructed and viewed by a human observer. We compare two approaches to optimizing SPECT for a detection task with a known signal in a statistically varying background. In a sequential approach, we optimize the collimator using an ideal observer applied to the sinogram. We then optimize the regularization of the reconstruction using a human-emulating channelized Hotelling observer (CHO). In a second approach, we use the CHO to jointly optimize the collimator and regularization. The performance of the joint approach exceeds that of the sequential approach. The collimator properties from the joint approach are closer to that of a commercial collimator than those of the sequential approach. Thus using the “best” collimator derived by an ideal observer leads to suboptimal net detection performance.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116795577","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 : 2009-06-28DOI: 10.1109/ISBI.2009.5193257
James R. Anderson, B. Jones, Jia-Hui Yang, M. V. Shaw, C. Watt, P. Koshevoy, J. Spaltenstein, E. Jurrus, K. Venkataraju, R. Whitaker, D. Mastronarde, T. Tasdizen, R. Marc
Complete mapping of neuronal networks requires data acquisition at synaptic resolution with canonical coverage of tissues and robust neuronal classification. Transmission electron microscopy (TEM) remains the optimal tool for network mapping. However, capturing high resolution, large, serial section TEM (ssTEM) image volumes is complicated by the need to precisely mosaic distorted image tiles and subsequently register distorted mosaics. Moreover, most cell or tissue class markers are not optimized for TEM imaging. We present a complete framework for neuronal reconstruction at ultrastructural resolution, allowing the elucidation of complete neuronal circuits. This workflow combines TEM-compliant small molecule profiling with automated image tile mosaicking, automated slice-to-slice image registration and terabyte-scale image browsing for volume annotation. Networks that previously would require decades of assembly can now be completed in months, enabling large-scale connectivity analyses of both new and legacy data. Additionally, these approaches can be extended to other tissue or biological network systems.
{"title":"Ultrastructural mapping of neural circuitry: A computational framework","authors":"James R. Anderson, B. Jones, Jia-Hui Yang, M. V. Shaw, C. Watt, P. Koshevoy, J. Spaltenstein, E. Jurrus, K. Venkataraju, R. Whitaker, D. Mastronarde, T. Tasdizen, R. Marc","doi":"10.1109/ISBI.2009.5193257","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193257","url":null,"abstract":"Complete mapping of neuronal networks requires data acquisition at synaptic resolution with canonical coverage of tissues and robust neuronal classification. Transmission electron microscopy (TEM) remains the optimal tool for network mapping. However, capturing high resolution, large, serial section TEM (ssTEM) image volumes is complicated by the need to precisely mosaic distorted image tiles and subsequently register distorted mosaics. Moreover, most cell or tissue class markers are not optimized for TEM imaging. We present a complete framework for neuronal reconstruction at ultrastructural resolution, allowing the elucidation of complete neuronal circuits. This workflow combines TEM-compliant small molecule profiling with automated image tile mosaicking, automated slice-to-slice image registration and terabyte-scale image browsing for volume annotation. Networks that previously would require decades of assembly can now be completed in months, enabling large-scale connectivity analyses of both new and legacy data. Additionally, these approaches can be extended to other tissue or biological network systems.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116833995","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：