Pub Date : 2010-04-14DOI: 10.1109/isbi.2010.5490228
Ju Han, Seema Singh, Lan Sun, B. Simmons, M. Auer, B. Parvin
This paper presents a computational framework for chemical profiling of the plant cell wall through the Raman spectroscopy. The system enables query of known spectral signatures and clustering of spectral data based on intrinsic properties. As a result, presence and relative concentration of specific chemical bonds can be quantified. The primary contribution of this paper is in representation of raman profile in terms of fluorescence background and multiscale peak detection at each grid point (voxel). Such a representation allows efficient spatial segmentation based on the coupling between high-level salient properties and low-level symbolic representation at each voxel. The high-level salient properties refer to preferred peaks and their attributes for the entire image. The low-level symbolic representations are based on fluorescence background, spectral peak locations, and their attributes. We present results on a corn stover tissue section that is imaged through Raman microscopy, and the results are consistent with the literature. In addition, automatic clustering indicates several distinct layers of the cell walls with different spectral signatures.
{"title":"Chemical profiling of the plant cellwall through Raman microspectroscopy","authors":"Ju Han, Seema Singh, Lan Sun, B. Simmons, M. Auer, B. Parvin","doi":"10.1109/isbi.2010.5490228","DOIUrl":"https://doi.org/10.1109/isbi.2010.5490228","url":null,"abstract":"This paper presents a computational framework for chemical profiling of the plant cell wall through the Raman spectroscopy. The system enables query of known spectral signatures and clustering of spectral data based on intrinsic properties. As a result, presence and relative concentration of specific chemical bonds can be quantified. The primary contribution of this paper is in representation of raman profile in terms of fluorescence background and multiscale peak detection at each grid point (voxel). Such a representation allows efficient spatial segmentation based on the coupling between high-level salient properties and low-level symbolic representation at each voxel. The high-level salient properties refer to preferred peaks and their attributes for the entire image. The low-level symbolic representations are based on fluorescence background, spectral peak locations, and their attributes. We present results on a corn stover tissue section that is imaged through Raman microscopy, and the results are consistent with the literature. In addition, automatic clustering indicates several distinct layers of the cell walls with different spectral signatures.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"61 18","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120934488","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490218
Daniel Tenbrinck, M. Dawood, F. Gigengack, M. Fieseler, Xiaoyi Jiang, Klaus Schiifers
Motion correction in Positron Emission Tomography (PET) using optical flow estimation can lead to image artifacts due to Partial Volume Effects (PVE). These artifacts appear especially in cardiac gated PET images and cause blurred edges in the averaged gates. In this paper we propose a new method to motion correct PET images considering the PVE during optical flow estimation. For this purpose we introduce a local intensity correction algorithm and combine it with the optical flow computation in an iterative scheme. The results of our approach show a qualitative and quantitative improvement of the motion corrected PET gates in examinations of both human patients and laboratory mice for pre-clinical research.
{"title":"Motion correction in Positron Emission Tomography considering Partial Volume Effects in optical flow estimation","authors":"Daniel Tenbrinck, M. Dawood, F. Gigengack, M. Fieseler, Xiaoyi Jiang, Klaus Schiifers","doi":"10.1109/ISBI.2010.5490218","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490218","url":null,"abstract":"Motion correction in Positron Emission Tomography (PET) using optical flow estimation can lead to image artifacts due to Partial Volume Effects (PVE). These artifacts appear especially in cardiac gated PET images and cause blurred edges in the averaged gates. In this paper we propose a new method to motion correct PET images considering the PVE during optical flow estimation. For this purpose we introduce a local intensity correction algorithm and combine it with the optical flow computation in an iterative scheme. The results of our approach show a qualitative and quantitative improvement of the motion corrected PET gates in examinations of both human patients and laboratory mice for pre-clinical research.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"70 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120975490","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490160
G. Themelis, Athanasios Sarantopoulos, N. Harlaar, G. M. Dam, V. Ntziachristos
A new concept aiming to improve surgical outcome by providing real-time surgical feedback on tumor margin delineation is presented. We are using a novel multispectral imaging system for real time measurement of fluorescence probes with molecular specificity to tumor biomarkers. Mice bearing xenograft tumors were used to simulate surgical operations of tumor excision guided by real time fluorescence imaging. Bioluminescence imaging and histopathology were used as gold standards to confirm the in-vivo findings. Results demonstrate the capability of the method to identify tumor negative margins with high specificity and provide real time feedback to the surgeon.
{"title":"Advancing the technology and applications of surgical fluorescence imaging with targeted fluorochromes","authors":"G. Themelis, Athanasios Sarantopoulos, N. Harlaar, G. M. Dam, V. Ntziachristos","doi":"10.1109/ISBI.2010.5490160","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490160","url":null,"abstract":"A new concept aiming to improve surgical outcome by providing real-time surgical feedback on tumor margin delineation is presented. We are using a novel multispectral imaging system for real time measurement of fluorescence probes with molecular specificity to tumor biomarkers. Mice bearing xenograft tumors were used to simulate surgical operations of tumor excision guided by real time fluorescence imaging. Bioluminescence imaging and histopathology were used as gold standards to confirm the in-vivo findings. Results demonstrate the capability of the method to identify tumor negative margins with high specificity and provide real time feedback to the surgeon.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121131426","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490347
Philipp J. Stolka, Hyun-Jae Kang, M. Choti, E. Boctor
Handheld 2D ultrasound is very useful for intra-operative imaging, but requires some reconstruction effort in order to create 3D US volumes, unless one is using large and expensive 3D US probes. Unlike common probe tracking approaches involving either global or local tracking algorithms (suffering from jitter and complexity or from drift), we propose to use a combination of local sensors to reconstruct the probe trajectory with multiple degrees of freedom. The presented sensors are very low-cost- optical mice and a Wii Remote - yet enable flexible 3D US acquisition with no global tracking overhead. The resulting trajectories are then used as input for a dynamically expanding, pixel-nearest-neighbor 3D US volume reconstruction.
{"title":"Multi-DoF probe trajectory reconstruction with local sensors for 2D-to-3D ultrasound","authors":"Philipp J. Stolka, Hyun-Jae Kang, M. Choti, E. Boctor","doi":"10.1109/ISBI.2010.5490347","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490347","url":null,"abstract":"Handheld 2D ultrasound is very useful for intra-operative imaging, but requires some reconstruction effort in order to create 3D US volumes, unless one is using large and expensive 3D US probes. Unlike common probe tracking approaches involving either global or local tracking algorithms (suffering from jitter and complexity or from drift), we propose to use a combination of local sensors to reconstruct the probe trajectory with multiple degrees of freedom. The presented sensors are very low-cost- optical mice and a Wii Remote - yet enable flexible 3D US acquisition with no global tracking overhead. The resulting trajectories are then used as input for a dynamically expanding, pixel-nearest-neighbor 3D US volume reconstruction.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121193481","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490416
P. D. Tafti, R. Delgado-Gonzalo, A. Stalder, M. Unser
We introduce stochastic models for flow fields with parameters that dictate the scale-dependent (self-similar) character of the field and control the balance between its rotational vs compressive behaviour. The development of our models is motivated by the availability of imaging modalities that measure flow vector fields (flow-sensitive MRI and Doppler ultrasound). To study such data, we formulate estimators of the model parameters, and use them to quantify the Hurst exponent and directional properties of synthetic and real-world flow fields (measured by means of phase-contrast MRI) in 3D.
{"title":"Fractal modelling and analysis of flow-field images","authors":"P. D. Tafti, R. Delgado-Gonzalo, A. Stalder, M. Unser","doi":"10.1109/ISBI.2010.5490416","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490416","url":null,"abstract":"We introduce stochastic models for flow fields with parameters that dictate the scale-dependent (self-similar) character of the field and control the balance between its rotational vs compressive behaviour. The development of our models is motivated by the availability of imaging modalities that measure flow vector fields (flow-sensitive MRI and Doppler ultrasound). To study such data, we formulate estimators of the model parameters, and use them to quantify the Hurst exponent and directional properties of synthetic and real-world flow fields (measured by means of phase-contrast MRI) in 3D.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121274657","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490215
S. Chun, C. Schretter, J. Fessler
Recent advances in medical imaging technologies have made 4D image sequences available in clinical routine. As a consequence, image registration techniques are evolving from alignment of pairs of static volumetric images to spatio-temporal registration of dynamic (4D) images. Since the elastic image registration problem is ill-posed, additional prior information or constraints are usually required to regularize the problem. This work proposes to enforce local invertibility (diffeomorphism) of 4D deformations. A novel sufficient condition for local invertibility over continuous space and time is proposed and a practical regularization prior is designed from the theory. The method has been applied to an image registration (motion tracking) of a dynamic 4D CT image sequence. Results show that using proposed regularizer leads to deformations that are more plausible for respiratory motion than the standard approach without additional temporal regularization.
{"title":"Sufficient condition for local invertibility of spatio-temporal 4D B-spline deformations","authors":"S. Chun, C. Schretter, J. Fessler","doi":"10.1109/ISBI.2010.5490215","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490215","url":null,"abstract":"Recent advances in medical imaging technologies have made 4D image sequences available in clinical routine. As a consequence, image registration techniques are evolving from alignment of pairs of static volumetric images to spatio-temporal registration of dynamic (4D) images. Since the elastic image registration problem is ill-posed, additional prior information or constraints are usually required to regularize the problem. This work proposes to enforce local invertibility (diffeomorphism) of 4D deformations. A novel sufficient condition for local invertibility over continuous space and time is proposed and a practical regularization prior is designed from the theory. The method has been applied to an image registration (motion tracking) of a dynamic 4D CT image sequence. Results show that using proposed regularizer leads to deformations that are more plausible for respiratory motion than the standard approach without additional temporal regularization.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122368246","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490055
Fumihiko Ino, Yusuke Okitsu, Taketo Kishi, S. Ohnishi, K. Hagihara
This paper presents a graphics processing unit (GPU) based method capable of accelerating cone-beam reconstruction of large volume data, which cannot be entirely stored in video memory. Our method accelerates the Feldkamp, Davis and Kress (FDK) algorithm in a multi-GPU environment. We present how the entire volume can be efficiently decomposed into small portions to reduce the usage of video memory on each graphics card. Experimental results are also presented to understand the reconstruction throughput on an nVIDIA Tesla S1070 server. It takes approximately three minutes to reconstruct a 20483-voxel volume from 720 20482-pixel projections. The effective bandwidth of video memory reaches 137 GB/s per GPU, demonstrating a higher utilization of texture caches.
本文提出了一种基于图形处理单元(GPU)的方法，能够对不能完全存储在显存中的大容量数据加速锥束重建。我们的方法在多gpu环境下加速了FDK算法。我们介绍了如何有效地将整个卷分解成小部分，以减少每个图形卡上视频内存的使用。通过实验结果了解了在nVIDIA Tesla S1070服务器上的重构吞吐量。从720个20482像素的投影中重建一个20483体素的体积大约需要3分钟。每个GPU的显存有效带宽达到137 GB/s，纹理缓存的利用率更高。
{"title":"Out-of-core cone beam reconstruction using multiple GPUs","authors":"Fumihiko Ino, Yusuke Okitsu, Taketo Kishi, S. Ohnishi, K. Hagihara","doi":"10.1109/ISBI.2010.5490055","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490055","url":null,"abstract":"This paper presents a graphics processing unit (GPU) based method capable of accelerating cone-beam reconstruction of large volume data, which cannot be entirely stored in video memory. Our method accelerates the Feldkamp, Davis and Kress (FDK) algorithm in a multi-GPU environment. We present how the entire volume can be efficiently decomposed into small portions to reduce the usage of video memory on each graphics card. Experimental results are also presented to understand the reconstruction throughput on an nVIDIA Tesla S1070 server. It takes approximately three minutes to reconstruct a 20483-voxel volume from 720 20482-pixel projections. The effective bandwidth of video memory reaches 137 GB/s per GPU, demonstrating a higher utilization of texture caches.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127094229","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490103
M. Langer, F. Peyrin
In zoom-in tomography, the aim is to image a region of interest lying partially or fully within the imaged object, using a high resolution tomographic scan covering only the ROI, and a low resolution scan covering the whole object. We analyze the problem from a multiresolution point of view and propose an algorithm for combining the two data sets using the discrete wavelet transform and the Haar wavelet. We compare the proposed algorithm to a previously reported method that involves padding of the high resolution data with a supersampled version of the low resolution data, to zero padding and edge extension, using synthetic data sets. We show that the proposed algorithm is insensitive to offsets between the two data sets, but that it is slightly more sensitive to noise.
{"title":"A wavelet algorithm for zoom-in tomography","authors":"M. Langer, F. Peyrin","doi":"10.1109/ISBI.2010.5490103","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490103","url":null,"abstract":"In zoom-in tomography, the aim is to image a region of interest lying partially or fully within the imaged object, using a high resolution tomographic scan covering only the ROI, and a low resolution scan covering the whole object. We analyze the problem from a multiresolution point of view and propose an algorithm for combining the two data sets using the discrete wavelet transform and the Haar wavelet. We compare the proposed algorithm to a previously reported method that involves padding of the high resolution data with a supersampled version of the low resolution data, to zero padding and edge extension, using synthetic data sets. We show that the proposed algorithm is insensitive to offsets between the two data sets, but that it is slightly more sensitive to noise.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"256 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127929954","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490339
D. Loeckx, F. Maes, D. Vandermeulen, P. Suetens
Recently, different approaches have emerged for voxel based nonrigid image registration using local instead of global similarity measures. Benefits are more accurate registration or the ability to subdivide the global similarity in local contributions. Within this article, we provide a general method to localise similarity measures using overlapping regions. Moreover, we extend the concept of partial volume estimation, introduced for mutual information (MI), to other similarity measures. We compare local and global sum of squared differences (SSD), cross correlation (CC) and MI for different sizes of the local regions. In general, local MI gives the highest accuracy, even for image pairs of the same modality. Partial volume estimation slightly improves the accuracy for local measures; the improvement is more pronounced for label images.
{"title":"Voxel based nonrigid image registration using local and partial volume similarity measures","authors":"D. Loeckx, F. Maes, D. Vandermeulen, P. Suetens","doi":"10.1109/ISBI.2010.5490339","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490339","url":null,"abstract":"Recently, different approaches have emerged for voxel based nonrigid image registration using local instead of global similarity measures. Benefits are more accurate registration or the ability to subdivide the global similarity in local contributions. Within this article, we provide a general method to localise similarity measures using overlapping regions. Moreover, we extend the concept of partial volume estimation, introduced for mutual information (MI), to other similarity measures. We compare local and global sum of squared differences (SSD), cross correlation (CC) and MI for different sizes of the local regions. In general, local MI gives the highest accuracy, even for image pairs of the same modality. Partial volume estimation slightly improves the accuracy for local measures; the improvement is more pronounced for label images.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129026255","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 : 2010-04-14DOI: 10.1109/ISBI.2010.5490237
E. Shaked, O. Michailovich
The problem of reconstruction of digital images from their degraded measurements is regarded as a problem of central importance in various fields of engineering and imaging sciences. In such cases, the degradation is typically caused by the resolution limitations of an imaging device in use and/or by measurement noise. In the field of optics and nuclear imaging, the noise is commonly assumed to obey a Poisson distribution. In this note, a novel method for de-noising and/or de-blurring of digital images corrupted by Poisson noise is introduced. The proposed method is derived under the assumption that the image of interest can be sparsely represented in the domain of a linear transform. Consequently, a shrinkage-based iterative procedure is proposed, which guarantees convergence to the global maximizer of an associated maximuma-posteriori criterion.
{"title":"Deconvolution of poissonian images via iterative shrinkage","authors":"E. Shaked, O. Michailovich","doi":"10.1109/ISBI.2010.5490237","DOIUrl":"https://doi.org/10.1109/ISBI.2010.5490237","url":null,"abstract":"The problem of reconstruction of digital images from their degraded measurements is regarded as a problem of central importance in various fields of engineering and imaging sciences. In such cases, the degradation is typically caused by the resolution limitations of an imaging device in use and/or by measurement noise. In the field of optics and nuclear imaging, the noise is commonly assumed to obey a Poisson distribution. In this note, a novel method for de-noising and/or de-blurring of digital images corrupted by Poisson noise is introduced. The proposed method is derived under the assumption that the image of interest can be sparsely represented in the domain of a linear transform. Consequently, a shrinkage-based iterative procedure is proposed, which guarantees convergence to the global maximizer of an associated maximuma-posteriori criterion.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130313534","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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