Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541063
M. Freiman, Y. Edrei, E. Gross, Leo Joskowicz, R. Abramovitch
We present a novel method for computer aided early detection of liver metastases. The method used fMRI-based statistical modeling to characterize colorectal hepatic metastases and follow their early hemodynamical changes. Changes in hepatic hemodynamics were evaluated from T2*-W fMRI images acquired during the breathing of air, air-CO2, and carbogen. A classification model was built to differentiate between metastatic and healthy liver tissue. The model was constructed from 128 validated fMRI samples of metastatic and healthy mice liver tissue using histogram-based features and SVM classification engine. The model was subsequently tested with a set of 32 early, non-validated fMRI samples. Our model yielded an accuracy of 84.38% with 80% precision.
{"title":"Liver metastasis early detection using fMRI based statistical model","authors":"M. Freiman, Y. Edrei, E. Gross, Leo Joskowicz, R. Abramovitch","doi":"10.1109/ISBI.2008.4541063","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541063","url":null,"abstract":"We present a novel method for computer aided early detection of liver metastases. The method used fMRI-based statistical modeling to characterize colorectal hepatic metastases and follow their early hemodynamical changes. Changes in hepatic hemodynamics were evaluated from T2*-W fMRI images acquired during the breathing of air, air-CO2, and carbogen. A classification model was built to differentiate between metastatic and healthy liver tissue. The model was constructed from 128 validated fMRI samples of metastatic and healthy mice liver tissue using histogram-based features and SVM classification engine. The model was subsequently tested with a set of 32 early, non-validated fMRI samples. Our model yielded an accuracy of 84.38% with 80% precision.","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":"129733845","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.2008.4541151
J. Cisternas, T. Asahi, M. Gálvez, G. Rojas
We present a regularization scheme for diffusion tensor images, that respects the geometrical structure of diffusion ellipsoids and does not introduce artifacts such as anisotropy drops. The method can be stated as a variational problem and solved by means of a gradient flow. The main ingredient is the notion of a distance between two ellipsoids that considers differences in shape as well as differences in orientation. The method is specialized to the case of cylindrically-symmetric ellipsoids and implemented in terms of ordinary vector manipulations such as cross and dot products. The regularization algorithm is tested using a synthetic tensor field and a dataset acquired from a diffusion phantom. In both cases the algorithm was able to reduce the noise from the tensor field.
{"title":"Regularization of diffusion tensor images","authors":"J. Cisternas, T. Asahi, M. Gálvez, G. Rojas","doi":"10.1109/ISBI.2008.4541151","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541151","url":null,"abstract":"We present a regularization scheme for diffusion tensor images, that respects the geometrical structure of diffusion ellipsoids and does not introduce artifacts such as anisotropy drops. The method can be stated as a variational problem and solved by means of a gradient flow. The main ingredient is the notion of a distance between two ellipsoids that considers differences in shape as well as differences in orientation. The method is specialized to the case of cylindrically-symmetric ellipsoids and implemented in terms of ordinary vector manipulations such as cross and dot products. The regularization algorithm is tested using a synthetic tensor field and a dataset acquired from a diffusion phantom. In both cases the algorithm was able to reduce the noise from the tensor field.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"13 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":"128950371","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.2008.4541262
K. Cleary, Jill Bruno, Jason Wright, F. Banovac
This paper gives an overview of computer-assisted and image-guided systems for abdominal interventions. Computer-assisted means that the power of the computer is used to provide the physician a virtual reality view of the anatomy. Image-guided means that the intervention is carried out based on imaging modalities such as CT, MM, and ultrasound. These minimally invasive procedures are rapidly increasing in popularity as they cause less trauma to the patient and the technology to carry them out continues to improve.
{"title":"Computer-assisted and image-guided abdominal interventions","authors":"K. Cleary, Jill Bruno, Jason Wright, F. Banovac","doi":"10.1109/ISBI.2008.4541262","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541262","url":null,"abstract":"This paper gives an overview of computer-assisted and image-guided systems for abdominal interventions. Computer-assisted means that the power of the computer is used to provide the physician a virtual reality view of the anatomy. Image-guided means that the intervention is carried out based on imaging modalities such as CT, MM, and ultrasound. These minimally invasive procedures are rapidly increasing in popularity as they cause less trauma to the patient and the technology to carry them out continues to improve.","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":"126954446","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.2008.4541107
E. Miqueles, A. R. Pierro
X-Ray fluorescence computed tomography (XFCT) aims at reconstructing fluorescence density from emission data given the measured X-Ray attenuation. In this paper, inspired by emission tomography (ECT) reconstruction literature, we propose and compare different reconstruction methods for XFCT, based on iteratively inverting the generalized attenuated Radon transform. We compare the different approaches using simulated and real data as well.
{"title":"Fluorescence tomography: Reconstruction by iterative methods","authors":"E. Miqueles, A. R. Pierro","doi":"10.1109/ISBI.2008.4541107","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541107","url":null,"abstract":"X-Ray fluorescence computed tomography (XFCT) aims at reconstructing fluorescence density from emission data given the measured X-Ray attenuation. In this paper, inspired by emission tomography (ECT) reconstruction literature, we propose and compare different reconstruction methods for XFCT, based on iteratively inverting the generalized attenuated Radon transform. We compare the different approaches using simulated and real data as well.","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":"130703209","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.2008.4541082
S. Kadoury, F. Cheriet, H. Labelle
In this paper, we propose a hybrid approach using a statistical 3D model of the spine generated from a database of 732 scoliotic patients with high-level anatomical primitives identified and matched on biplanar radiographic images for the three-dimensional reconstruction of the scoliotic spine. The 3D scoliotic curve reconstructed from a coronal and sagittal radiograph is used to generate an approximate statistical model based on a transformation algorithm which incorporates intuitive geometrical properties. An iterative optimization procedure integrating similarity measures such as deformable vertebral contours and epipolar constraints is then applied to globally refine the 3D anatomical landmarks on each vertebra level of the spine. A qualitative evaluation of the retro-projection of the vertebral contours obtained from the proposed method gave promising results while the quantitative comparison yield similar accuracy on the localization of low-level primitives such as the six landmarks identified by an expert on each vertebra.
{"title":"A statistical image-based approach for the 3D reconstruction of the scoliotic spine from biplanar radiographs","authors":"S. Kadoury, F. Cheriet, H. Labelle","doi":"10.1109/ISBI.2008.4541082","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541082","url":null,"abstract":"In this paper, we propose a hybrid approach using a statistical 3D model of the spine generated from a database of 732 scoliotic patients with high-level anatomical primitives identified and matched on biplanar radiographic images for the three-dimensional reconstruction of the scoliotic spine. The 3D scoliotic curve reconstructed from a coronal and sagittal radiograph is used to generate an approximate statistical model based on a transformation algorithm which incorporates intuitive geometrical properties. An iterative optimization procedure integrating similarity measures such as deformable vertebral contours and epipolar constraints is then applied to globally refine the 3D anatomical landmarks on each vertebra level of the spine. A qualitative evaluation of the retro-projection of the vertebral contours obtained from the proposed method gave promising results while the quantitative comparison yield similar accuracy on the localization of low-level primitives such as the six landmarks identified by an expert on each vertebra.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"10 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132148171","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.2008.4541135
M. Chiang, M. Barysheva, Agatha D. Lee, S. Madsen, A. Klunder, A. Toga, K. Mcmahon, G. Zubicaray, M. Meredith, M. Wright, Anuj Srivastava, N. Balov, P. Thompson
We report the first 3D maps of genetic effects on brain fiber complexity. We analyzed HARDI brain imaging data from 90 young adult twins using an information-theoretic measure, the Jensen-Shannon divergence (JSD), to gauge the regional complexity of the white matter fiber orientation distribution functions (ODF). HARDI data were fluidly registered using Karcher means and ODF square-roots for interpolation; each subject's JSD map was computed from the spatial coherence of the ODFs in each voxel's neighborhood. We evaluated the genetic influences on generalized fiber anisotropy (GFA) and complexity (JSD) using structural equation models (SEM). At each voxel, genetic and environmental components of data variation were estimated, and their goodness of fit tested by permutation. Color- coded maps revealed that the optimal models varied for different brain regions. Fiber complexity was predominantly under genetic control, and was higher in more highly anisotropic regions. These methods show promise for discovering factors affecting fiber connectivity in the brain.
{"title":"Mapping genetic influences on brain fiber architecture with high angular resolution diffusion imaging (HARDI)","authors":"M. Chiang, M. Barysheva, Agatha D. Lee, S. Madsen, A. Klunder, A. Toga, K. Mcmahon, G. Zubicaray, M. Meredith, M. Wright, Anuj Srivastava, N. Balov, P. Thompson","doi":"10.1109/ISBI.2008.4541135","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541135","url":null,"abstract":"We report the first 3D maps of genetic effects on brain fiber complexity. We analyzed HARDI brain imaging data from 90 young adult twins using an information-theoretic measure, the Jensen-Shannon divergence (JSD), to gauge the regional complexity of the white matter fiber orientation distribution functions (ODF). HARDI data were fluidly registered using Karcher means and ODF square-roots for interpolation; each subject's JSD map was computed from the spatial coherence of the ODFs in each voxel's neighborhood. We evaluated the genetic influences on generalized fiber anisotropy (GFA) and complexity (JSD) using structural equation models (SEM). At each voxel, genetic and environmental components of data variation were estimated, and their goodness of fit tested by permutation. Color- coded maps revealed that the optimal models varied for different brain regions. Fiber complexity was predominantly under genetic control, and was higher in more highly anisotropic regions. These methods show promise for discovering factors affecting fiber connectivity in the brain.","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":"130829497","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.2008.4540928
M. Linguraru, R. Summers
Medical imaging and computer-aided diagnosis (CAD) traditionally focus on organ- or disease-based applications. To shift from organ-based to organism-based approaches, CAD needs to replicate the work of radiologists and analyze consecutively multiple organs. A fully automatic method is presented for the simultaneous segmentation of four abdominal organs from 4D CT data. Abdominal contrast- enhanced CT scans from sixteen patients were obtained at three phases: non-contrast, arterial and portal. Intra- patient data is registered non-rigidly using the demons algorithm and smoothed with anisotropic diffusion. Mutual information accounts for intensity variability within the same organ during subsequent acquisitions and data are interpolated with cubic B-splines. Then heterogeneous erosion is applied to multi-phase data using the intensity characteristics of the liver, spleen, and kidneys. The erosion filter is a 4D convolution that preserves only image regions that satisfy the above intensity criteria. Finally, a geodesic level set completes the segmentation of the four abdominal organs. This 3D evaluation of abdominal data shows great promise as a computer-aided radiology tool for multi-organ and multi-disease analysis.
{"title":"Multi-organ automatic segmentation in 4D contrast-enhanced abdominal CT","authors":"M. Linguraru, R. Summers","doi":"10.1109/ISBI.2008.4540928","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4540928","url":null,"abstract":"Medical imaging and computer-aided diagnosis (CAD) traditionally focus on organ- or disease-based applications. To shift from organ-based to organism-based approaches, CAD needs to replicate the work of radiologists and analyze consecutively multiple organs. A fully automatic method is presented for the simultaneous segmentation of four abdominal organs from 4D CT data. Abdominal contrast- enhanced CT scans from sixteen patients were obtained at three phases: non-contrast, arterial and portal. Intra- patient data is registered non-rigidly using the demons algorithm and smoothed with anisotropic diffusion. Mutual information accounts for intensity variability within the same organ during subsequent acquisitions and data are interpolated with cubic B-splines. Then heterogeneous erosion is applied to multi-phase data using the intensity characteristics of the liver, spleen, and kidneys. The erosion filter is a 4D convolution that preserves only image regions that satisfy the above intensity criteria. Finally, a geodesic level set completes the segmentation of the four abdominal organs. This 3D evaluation of abdominal data shows great promise as a computer-aided radiology tool for multi-organ and multi-disease analysis.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"77 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":"131663470","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.2008.4541060
B. Ng, R. Abugharbieh, M. McKeown
We propose inferring functional connectivity between brain regions by examining the spatial modulation of the blood oxygen level dependent (BOLD) signals within brain regions of interest (ROIs). This is motivated by our previous work, where the spatial distribution of BOLD signals within an ROI was found to be modulated by task. Applying replicator dynamics to our proposed spatial feature time courses on real functional magnetic resonance imaging (fMRI) data detected task-related changes in the composition of the brain's functional networks, whereas using classical mean intensity features resulted in little changes being detected. Thus, our results suggest that intensity is not the only co- activating feature in fMRI data. Instead, spatial modulations may also be used for inferring functional connectivity.
{"title":"Inferring functional connectivity using spatial modulation measures of fMRI signals within brain regions of interest","authors":"B. Ng, R. Abugharbieh, M. McKeown","doi":"10.1109/ISBI.2008.4541060","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541060","url":null,"abstract":"We propose inferring functional connectivity between brain regions by examining the spatial modulation of the blood oxygen level dependent (BOLD) signals within brain regions of interest (ROIs). This is motivated by our previous work, where the spatial distribution of BOLD signals within an ROI was found to be modulated by task. Applying replicator dynamics to our proposed spatial feature time courses on real functional magnetic resonance imaging (fMRI) data detected task-related changes in the composition of the brain's functional networks, whereas using classical mean intensity features resulted in little changes being detected. Thus, our results suggest that intensity is not the only co- activating feature in fMRI data. Instead, spatial modulations may also be used for inferring functional connectivity.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"31 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":"131675025","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.2008.4540954
Saskia Delpretti, F. Luisier, S. Ramani, T. Blu, M. Unser
Due to the random nature of photon emission and the various internal noise sources of the detectors, real timelapse fluorescence microscopy images are usually modeled as the sum of a Poisson process plus some Gaussian white noise. In this paper, we propose an adaptation of our SURE-LET denoising strategy to take advantage of the potentially strong similarities between adjacent frames of the observed image sequence. To stabilize the noise variance, we first apply the generalized Anscombe transform using suitable parameters automatically estimated from the observed data. With the proposed algorithm, we show that, in a reasonable computation time, real fluorescence timelapse microscopy images can be denoised with higher quality than conventional algorithms.
{"title":"Multiframe sure-let denoising of timelapse fluorescence microscopy images","authors":"Saskia Delpretti, F. Luisier, S. Ramani, T. Blu, M. Unser","doi":"10.1109/ISBI.2008.4540954","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4540954","url":null,"abstract":"Due to the random nature of photon emission and the various internal noise sources of the detectors, real timelapse fluorescence microscopy images are usually modeled as the sum of a Poisson process plus some Gaussian white noise. In this paper, we propose an adaptation of our SURE-LET denoising strategy to take advantage of the potentially strong similarities between adjacent frames of the observed image sequence. To stabilize the noise variance, we first apply the generalized Anscombe transform using suitable parameters automatically estimated from the observed data. With the proposed algorithm, we show that, in a reasonable computation time, real fluorescence timelapse microscopy images can be denoised with higher quality than conventional algorithms.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"9 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":"126349112","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.2008.4541255
C. Vonesch, Michael Unser
Wavelet-domain lscr1-regularization is a promising approach to deconvolution. The corresponding variational problem can be solved using a "thresholded Landweber" (TL) algorithm. While this iterative procedure is simple to implement, it is known to converge slowly. In this paper, we give the principle of a modified algorithm that is substantially faster. The method is applicable to arbitrary wavelet representations, thus generalizing our previous work which was restricted to the or- thonormal Shannon wavelet basis. Numerical experiments show that we can obtain up to a 10-fold speed-up with respect to the existing TL algorithm, while providing the same restoration quality. We also present an example with real data that demonstrates the feasibility of wavelet-domain regularization for 3D deconvolution microscopy.
{"title":"A fast thresholded Landweber algorithm for general wavelet bases: Application to 3D deconvolution microscopy","authors":"C. Vonesch, Michael Unser","doi":"10.1109/ISBI.2008.4541255","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541255","url":null,"abstract":"Wavelet-domain lscr1-regularization is a promising approach to deconvolution. The corresponding variational problem can be solved using a \"thresholded Landweber\" (TL) algorithm. While this iterative procedure is simple to implement, it is known to converge slowly. In this paper, we give the principle of a modified algorithm that is substantially faster. The method is applicable to arbitrary wavelet representations, thus generalizing our previous work which was restricted to the or- thonormal Shannon wavelet basis. Numerical experiments show that we can obtain up to a 10-fold speed-up with respect to the existing TL algorithm, while providing the same restoration quality. We also present an example with real data that demonstrates the feasibility of wavelet-domain regularization for 3D deconvolution microscopy.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"96 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":"122565953","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：