Pub Date : 2008-05-14DOI: 10.1109/ISBI.2008.4541126
D. Luebke
Graphics processing units (GPUs) originally designed for computer video cards have emerged as the most powerful chip in a high-performance workstation. Unlike multicore CPU architectures, which currently ship with two or four cores, GPU architectures are "manycore" with hundreds of cores capable of running thousands of threads in parallel. NVIDIA's CUDA is a co-evolved hardware-software architecture that enables high-performance computing developers to harness the tremendous computational power and memory bandwidth of the GPU in a familiar programming environment - the C programming language. We describe the CUDA programming model and motivate its use in the biomedical imaging community.
{"title":"CUDA: Scalable parallel programming for high-performance scientific computing","authors":"D. Luebke","doi":"10.1109/ISBI.2008.4541126","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541126","url":null,"abstract":"Graphics processing units (GPUs) originally designed for computer video cards have emerged as the most powerful chip in a high-performance workstation. Unlike multicore CPU architectures, which currently ship with two or four cores, GPU architectures are \"manycore\" with hundreds of cores capable of running thousands of threads in parallel. NVIDIA's CUDA is a co-evolved hardware-software architecture that enables high-performance computing developers to harness the tremendous computational power and memory bandwidth of the GPU in a familiar programming environment - the C programming language. We describe the CUDA programming model and motivate its use in the biomedical imaging community.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"6 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":"126463161","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.2008.4540929
J. Butman, M. Linguraru
Postoperative communicating hydrocephalus has been recognized in patients with brain tumors. The associated changes in ventricle volume can be difficult to identify, particularly over short time intervals. Potentially, accurate ventricle volume estimates could provide for a better understanding of communicating hydrocephalus, and lead to more confident diagnoses. Our method evaluates ventricle size from serial brain MRI examinations, we (1) combined serial images to increase SNR (2) segmented this image to generate a ventricle template using fats marching methods and geodesic active contours, and (3) propagate the segmentation using deformable registration of the original MRI datasets. By applying this deformation to the ventricle template, serial volume estimates were obtained in a robust manner.
{"title":"Assessment of ventricle volume from serial MRI scans in communicating hydrocephalus","authors":"J. Butman, M. Linguraru","doi":"10.1109/ISBI.2008.4540929","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4540929","url":null,"abstract":"Postoperative communicating hydrocephalus has been recognized in patients with brain tumors. The associated changes in ventricle volume can be difficult to identify, particularly over short time intervals. Potentially, accurate ventricle volume estimates could provide for a better understanding of communicating hydrocephalus, and lead to more confident diagnoses. Our method evaluates ventricle size from serial brain MRI examinations, we (1) combined serial images to increase SNR (2) segmented this image to generate a ventricle template using fats marching methods and geodesic active contours, and (3) propagate the segmentation using deformable registration of the original MRI datasets. By applying this deformation to the ventricle template, serial volume estimates were obtained in a robust manner.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"17 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":"122236548","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.2008.4541104
J. Boulanger, J. Sibarita, C. Kervrann, P. Bouthemy
We present a non-parametric regression method for denoising fluorescence video-microscopy volume sequences. The designed method aims at using the 3D+t information in order to restore acquired data contaminated by Poisson and Gaussian noise. We propose to use a variance stabilization transform to deal with the combination of Poisson and Gaussian noise. Consequently, we further propose an adaptive patch-based framework able to preserve space-time discontinuities and reduce significantly noise level using the 3D+t space-time context. This approach lead to an algorithm whose parameters are calibrated and then ready for intensive use. The performance of the proposed method are then demonstrated on both synthetic and real image sequences using quantitative as well as qualitative criteria.
{"title":"Non-parametric regression for patch-based fluorescence microscopy image sequence denoising","authors":"J. Boulanger, J. Sibarita, C. Kervrann, P. Bouthemy","doi":"10.1109/ISBI.2008.4541104","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541104","url":null,"abstract":"We present a non-parametric regression method for denoising fluorescence video-microscopy volume sequences. The designed method aims at using the 3D+t information in order to restore acquired data contaminated by Poisson and Gaussian noise. We propose to use a variance stabilization transform to deal with the combination of Poisson and Gaussian noise. Consequently, we further propose an adaptive patch-based framework able to preserve space-time discontinuities and reduce significantly noise level using the 3D+t space-time context. This approach lead to an algorithm whose parameters are calibrated and then ready for intensive use. The performance of the proposed method are then demonstrated on both synthetic and real image sequences using quantitative as well as qualitative criteria.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"22 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":"114229489","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.2008.4541312
S. Sekhar, W. Al-Nuaimy, A. Nandi
The retinal fundus photograph is widely used in the diagnosis and treatment of various eye diseases such as diabetic retinopathy and glaucoma. Medical image analysis and processing has great significance in the field of medicine, especially in non-invasive treatment and clinical study. Normally fundus images are manually graded by specially trained clinicians in a time-consuming and resource-intensive process. A computer-aided fundus image analysis could provide an immediate detection and characterisation of retinal features prior to specialist inspection. This paper describes a novel method to automatically localise one such feature: the optic disk. The proposed method consists of two steps: in the first step, a circular region of interest is found by first isolating the brightest area in the image by means of morphological processing, and in the second step, the Hough transform is used to detect the main circular feature (corresponding to the optical disk) within the positive horizontal gradient image within this region of interest. Initial results on a database of fundus images show that the proposed method is effective and favourable in relation to comparable techniques.
{"title":"Automated localisation of retinal optic disk using Hough transform","authors":"S. Sekhar, W. Al-Nuaimy, A. Nandi","doi":"10.1109/ISBI.2008.4541312","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541312","url":null,"abstract":"The retinal fundus photograph is widely used in the diagnosis and treatment of various eye diseases such as diabetic retinopathy and glaucoma. Medical image analysis and processing has great significance in the field of medicine, especially in non-invasive treatment and clinical study. Normally fundus images are manually graded by specially trained clinicians in a time-consuming and resource-intensive process. A computer-aided fundus image analysis could provide an immediate detection and characterisation of retinal features prior to specialist inspection. This paper describes a novel method to automatically localise one such feature: the optic disk. The proposed method consists of two steps: in the first step, a circular region of interest is found by first isolating the brightest area in the image by means of morphological processing, and in the second step, the Hough transform is used to detect the main circular feature (corresponding to the optical disk) within the positive horizontal gradient image within this region of interest. Initial results on a database of fundus images show that the proposed method is effective and favourable in relation to comparable techniques.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114040886","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.2008.4541118
L. Caldeira, J. Sanches
Dynamic-contrast enhanced MRI (DCE-MRI) is used in clinical practice to assess liver tumor malignancy. An algorithm to get information for automatic classification of tumors is presented. The Maximum value and WashIn and WashOut rates, obtained from the perfusion curves measured from the DCE-MRI images, are used in the classification process. The perfusion curves are described by a linear discrete pharmacokinetic (PK) model, based on multi-compartment paradigm where the input is the bolus injection. The arterial input function (AIF) that is usually estimated in the closest artery is assumed here to be the response of a second order linear system to the bolus injection. Therefore, the complete chain is modeled as a third order system with a single zero. The alignment procedure is performed by using the Mutual Information (MI) criterion with a non-rigid transformation to compensate the displacements occurred during the acquisition process. It is shown that the Maximum values and the WashIn and WashOut rates of the perfusion curves in malignant tumors are higher than in healthy tissues. This fact is used to classify them. Furthermore, it is also shown, that inside the tumor, the parameters associated with the perfusion curves for each pixel (time courses) present a higher variance than in the healthy tissues, which may also be used to increase the accuracy of the classifier. Examples using real data are presented.
{"title":"Liver tumor assessment with DCE-MRI","authors":"L. Caldeira, J. Sanches","doi":"10.1109/ISBI.2008.4541118","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541118","url":null,"abstract":"Dynamic-contrast enhanced MRI (DCE-MRI) is used in clinical practice to assess liver tumor malignancy. An algorithm to get information for automatic classification of tumors is presented. The Maximum value and WashIn and WashOut rates, obtained from the perfusion curves measured from the DCE-MRI images, are used in the classification process. The perfusion curves are described by a linear discrete pharmacokinetic (PK) model, based on multi-compartment paradigm where the input is the bolus injection. The arterial input function (AIF) that is usually estimated in the closest artery is assumed here to be the response of a second order linear system to the bolus injection. Therefore, the complete chain is modeled as a third order system with a single zero. The alignment procedure is performed by using the Mutual Information (MI) criterion with a non-rigid transformation to compensate the displacements occurred during the acquisition process. It is shown that the Maximum values and the WashIn and WashOut rates of the perfusion curves in malignant tumors are higher than in healthy tissues. This fact is used to classify them. Furthermore, it is also shown, that inside the tumor, the parameters associated with the perfusion curves for each pixel (time courses) present a higher variance than in the healthy tissues, which may also be used to increase the accuracy of the classifier. Examples using real data are presented.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125631194","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.2008.4540983
Ihor Smal, W. Niessen, E. Meijering
Tracking of multiple objects in biological image data is a challenging problem due largely to poor imaging conditions and complicated motion scenarios. Existing tracking algorithms for this purpose often do not provide sufficient robustness and/or are computationally expensive. In this paper we propose a new object detection scheme, based on importance sampling from image intensity distributions, and show how it can be easily incorporated into a probabilistic tracking framework based on Kalman or particle filtering. Experiments on synthetic as well as real fluorescence microscopy image data from different biological studies show that the resulting tracking algorithm yields smaller localization errors at much lower execution times compared to other available methods.
{"title":"A new detection scheme for multiple object tracking in fluorescence microscopy by joint probabilistic data association filtering","authors":"Ihor Smal, W. Niessen, E. Meijering","doi":"10.1109/ISBI.2008.4540983","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4540983","url":null,"abstract":"Tracking of multiple objects in biological image data is a challenging problem due largely to poor imaging conditions and complicated motion scenarios. Existing tracking algorithms for this purpose often do not provide sufficient robustness and/or are computationally expensive. In this paper we propose a new object detection scheme, based on importance sampling from image intensity distributions, and show how it can be easily incorporated into a probabilistic tracking framework based on Kalman or particle filtering. Experiments on synthetic as well as real fluorescence microscopy image data from different biological studies show that the resulting tracking algorithm yields smaller localization errors at much lower execution times compared to other available methods.","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":"122817424","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.2008.4541094
Hui Zhang, Paul Yushkevich, T. Simon, J. Gee
In this paper, we describe a novel technique for modeling sheet-like white matter (WM) fasciculi using continuous medial representation (cm-rep). In the cm-rep framework, the skeleton of a fasciculus is described by a parametric surface patch. This modeling scheme is particularly appropriate for sheet-like structures, because the shapes of such objects can be effectively captured by their skeletons. We show that dimensionality reduction can be achieved without much loss of spatial specificity by projecting data along the "less interesting" thickness direction onto the skeletons. We demonstrate that local fiber orientation of the modeled fasciculi can be encoded in our framework and show how this information can be leveraged for deriving and analyzing brain connectivity patterns on the skeleton themselves.
{"title":"Surface-based modeling of white matter fasciculi with orientation encoding","authors":"Hui Zhang, Paul Yushkevich, T. Simon, J. Gee","doi":"10.1109/ISBI.2008.4541094","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541094","url":null,"abstract":"In this paper, we describe a novel technique for modeling sheet-like white matter (WM) fasciculi using continuous medial representation (cm-rep). In the cm-rep framework, the skeleton of a fasciculus is described by a parametric surface patch. This modeling scheme is particularly appropriate for sheet-like structures, because the shapes of such objects can be effectively captured by their skeletons. We show that dimensionality reduction can be achieved without much loss of spatial specificity by projecting data along the \"less interesting\" thickness direction onto the skeletons. We demonstrate that local fiber orientation of the modeled fasciculi can be encoded in our framework and show how this information can be leveraged for deriving and analyzing brain connectivity patterns on the skeleton themselves.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"17 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":"122855427","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.2008.4540997
Adam T. Szafran, M. Marcelli, M. Mancini
Evidence suggest that a subgroup of patients affected by either prostate cancer or androgen insensitivity syndrome harbor mutations within the androgen receptor that may contribute to the disease phenotype. To characterize the effects of these AR mutations, we have developed a high content screening assay able to determine AR transcriptional activity, cellular distribution, and cellular patterning simultaneously at the single cell level. We demonstrate that two mutations (F764L, R840C) isolated from AIS patients retain the ability to achieve similar levels of transcriptional activity, nuclear translocation, and nuclear hyperspeckling as wild type receptor, but require significantly higher levels of agonist. Differences in responses seen between the different compounds tested also suggest that the assay could be amendable to agonist screening for personalized patient drug selection.
{"title":"High throughput multiplex image analyses for androgen receptor function","authors":"Adam T. Szafran, M. Marcelli, M. Mancini","doi":"10.1109/ISBI.2008.4540997","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4540997","url":null,"abstract":"Evidence suggest that a subgroup of patients affected by either prostate cancer or androgen insensitivity syndrome harbor mutations within the androgen receptor that may contribute to the disease phenotype. To characterize the effects of these AR mutations, we have developed a high content screening assay able to determine AR transcriptional activity, cellular distribution, and cellular patterning simultaneously at the single cell level. We demonstrate that two mutations (F764L, R840C) isolated from AIS patients retain the ability to achieve similar levels of transcriptional activity, nuclear translocation, and nuclear hyperspeckling as wild type receptor, but require significantly higher levels of agonist. Differences in responses seen between the different compounds tested also suggest that the assay could be amendable to agonist screening for personalized patient drug selection.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"50 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":"131756642","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.2008.4541266
Nassir Navab, J. Traub, T. Wendler, A. Buck, S. Ziegler
Since the 1920s, functional imaging has continuously contributed with novel methods in medical diagnostics. Its usage in the operation room has been limited in the past, although there is a great potential for localization of target structures and control of the surgery outcome. One example of functional information in the operation room is the use of nuclear probes. These devices are radiation detectors that provide a ID signal that allows the surgeons to get information about the distribution of a radioactive labeled structure. We extended nuclear probes with a spatial localization system in order to generate functional 3D surface images or functional tomographic images in the operating room. In this paper we summarize our methodology, discuss current limitations and possible remedies, and provide an outlook towards a new generation of image guided surgery based on anatomical and functional intraoperative imaging.
{"title":"Navigated nuclear probes for intra-operative functional imaging","authors":"Nassir Navab, J. Traub, T. Wendler, A. Buck, S. Ziegler","doi":"10.1109/ISBI.2008.4541266","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541266","url":null,"abstract":"Since the 1920s, functional imaging has continuously contributed with novel methods in medical diagnostics. Its usage in the operation room has been limited in the past, although there is a great potential for localization of target structures and control of the surgery outcome. One example of functional information in the operation room is the use of nuclear probes. These devices are radiation detectors that provide a ID signal that allows the surgeons to get information about the distribution of a radioactive labeled structure. We extended nuclear probes with a spatial localization system in order to generate functional 3D surface images or functional tomographic images in the operating room. In this paper we summarize our methodology, discuss current limitations and possible remedies, and provide an outlook towards a new generation of image guided surgery based on anatomical and functional intraoperative imaging.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"50 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":"127607920","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.2008.4541164
M. Chakravarty, B. Bedell, S. Zehntner, Alan C. Evans, D. Collins
Animal models are widely used to improve our understanding of the complex pathophysiological processes underlying diseases of the central nervous system (CNS), as well as a providing a means of evaluating the efficacy of new therapeutic agents. The advent of high-resolution, dedicated small animal magnetic resonance imaging and positron emission tomography scanners has greatly improved the value of animal imaging for such studies. However, the use of in vivo imaging markers requires extensive validation against gold standard, ex vivo tissue studies. In this paper, we describe methods for three-dimensional reconstruction of two-dimensional serial histological sections to create volumetric data, a major step in the use of ex vivo data for validating in vivo imaging techniques.
{"title":"Three-dimensional reconstruction of serial histological mouse brain sections","authors":"M. Chakravarty, B. Bedell, S. Zehntner, Alan C. Evans, D. Collins","doi":"10.1109/ISBI.2008.4541164","DOIUrl":"https://doi.org/10.1109/ISBI.2008.4541164","url":null,"abstract":"Animal models are widely used to improve our understanding of the complex pathophysiological processes underlying diseases of the central nervous system (CNS), as well as a providing a means of evaluating the efficacy of new therapeutic agents. The advent of high-resolution, dedicated small animal magnetic resonance imaging and positron emission tomography scanners has greatly improved the value of animal imaging for such studies. However, the use of in vivo imaging markers requires extensive validation against gold standard, ex vivo tissue studies. In this paper, we describe methods for three-dimensional reconstruction of two-dimensional serial histological sections to create volumetric data, a major step in the use of ex vivo data for validating in vivo imaging techniques.","PeriodicalId":184204,"journal":{"name":"2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"25 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":"134295766","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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