Pub Date : 1993-10-31DOI: 10.1109/NSSMIC.1993.373599
S. Song, S. Napel, N. Pelc, G. Glover
The authors have developed a technique based on a solution of the Poisson equation to unwrap the phase in magnetic resonance (MR) phase images. The method is based on the assumption that the magnitude of the inter-pixel phase change is less than /spl pi/ per pixel. Therefore, the authors obtain an estimate of the phase gradient by "wrapping" the gradient of the original phase image. The problem is then to obtain the absolute phase given the estimate of the phase gradient. The least-squares (LS) solution to this problem is shown to be a solution of the Poisson equation allowing the use of fast Poisson solvers. The absolute phase is then obtained by mapping the LS phase to the nearest multiple of 2/spl pi/ from the measured phase. The proposed technique is evaluated using MR phase images and is proven to be robust in the presence of noise. An application of the proposed method to the three-point Dixon technique for water and fat separation is demonstrated.<>
{"title":"A least squares based phase unwrapping algorithm for MRI","authors":"S. Song, S. Napel, N. Pelc, G. Glover","doi":"10.1109/NSSMIC.1993.373599","DOIUrl":"https://doi.org/10.1109/NSSMIC.1993.373599","url":null,"abstract":"The authors have developed a technique based on a solution of the Poisson equation to unwrap the phase in magnetic resonance (MR) phase images. The method is based on the assumption that the magnitude of the inter-pixel phase change is less than /spl pi/ per pixel. Therefore, the authors obtain an estimate of the phase gradient by \"wrapping\" the gradient of the original phase image. The problem is then to obtain the absolute phase given the estimate of the phase gradient. The least-squares (LS) solution to this problem is shown to be a solution of the Poisson equation allowing the use of fast Poisson solvers. The absolute phase is then obtained by mapping the LS phase to the nearest multiple of 2/spl pi/ from the measured phase. The proposed technique is evaluated using MR phase images and is proven to be robust in the presence of noise. An application of the proposed method to the three-point Dixon technique for water and fat separation is demonstrated.<<ETX>>","PeriodicalId":287813,"journal":{"name":"1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130504739","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 : 1993-10-31DOI: 10.1109/NSSMIC.1993.373547
Soojin Lee, Anand Rangarajan, G. Gindi
Bayesian reconstruction methods for emission tomography allow the introduction of prior information in the form of spatial smoothness constraints on the underlying object. The authors extend these priors to model the type of smoothness that favors piecewise linear regions. Empirical evidence that this extension is useful is found in animal autoradiographs that show regions of radionuclide density whose structure is far from piecewise flat. The extension uses a "weak plate" prior (A. Blake and A. Zisserman, 1987) that allows for piecewise-ramplike regions in the reconstruction. Here, discontinuities include creases-discontinuities in the object gradient rather than in the object itself. To incorporate their new prior in a MAP approach, the authors model the prior as a Gibbs distribution and use a GEM formulation for the optimization. They use mathematical phantoms and a phantom derived from an autoradiograph to illustrate the efficacy of the weak plate prior as compared to more conventional priors.<>
{"title":"Weak plate mechanical models in Bayesian reconstruction for emission tomography","authors":"Soojin Lee, Anand Rangarajan, G. Gindi","doi":"10.1109/NSSMIC.1993.373547","DOIUrl":"https://doi.org/10.1109/NSSMIC.1993.373547","url":null,"abstract":"Bayesian reconstruction methods for emission tomography allow the introduction of prior information in the form of spatial smoothness constraints on the underlying object. The authors extend these priors to model the type of smoothness that favors piecewise linear regions. Empirical evidence that this extension is useful is found in animal autoradiographs that show regions of radionuclide density whose structure is far from piecewise flat. The extension uses a \"weak plate\" prior (A. Blake and A. Zisserman, 1987) that allows for piecewise-ramplike regions in the reconstruction. Here, discontinuities include creases-discontinuities in the object gradient rather than in the object itself. To incorporate their new prior in a MAP approach, the authors model the prior as a Gibbs distribution and use a GEM formulation for the optimization. They use mathematical phantoms and a phantom derived from an autoradiograph to illustrate the efficacy of the weak plate prior as compared to more conventional priors.<<ETX>>","PeriodicalId":287813,"journal":{"name":"1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128809342","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 : 1993-10-31DOI: 10.1109/NSSMIC.1993.701849
E. Le Saux, J. Meunier, A. Demirjian
opted for a structural analysis approach, which is described in this article, along with the other image-processing steps that precede and succeed it. We have focused our study on molar stages D through H. we Present a semi-automatic system of dental age evahation from digitized X-ray pictures. The image segmentation, contour extraction and coding, structural pattern recognition, and qualitative criteria analysis modules implemented are described. 11. Preprocessing of X-ray images We have worked on the most developed molar stages. The results, based on a limited set of 30 radiographs, show that we can achieve better performance than human experts.
{"title":"X-ray Image Analysis For Dental Age Evaluation","authors":"E. Le Saux, J. Meunier, A. Demirjian","doi":"10.1109/NSSMIC.1993.701849","DOIUrl":"https://doi.org/10.1109/NSSMIC.1993.701849","url":null,"abstract":"opted for a structural analysis approach, which is described in this article, along with the other image-processing steps that precede and succeed it. We have focused our study on molar stages D through H. we Present a semi-automatic system of dental age evahation from digitized X-ray pictures. The image segmentation, contour extraction and coding, structural pattern recognition, and qualitative criteria analysis modules implemented are described. 11. Preprocessing of X-ray images We have worked on the most developed molar stages. The results, based on a limited set of 30 radiographs, show that we can achieve better performance than human experts.","PeriodicalId":287813,"journal":{"name":"1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123782402","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 : 1993-10-31DOI: 10.1109/NSSMIC.1993.373626
J. Bowsher, V. Johnson, T. Turkington, G.E. Floyd, R. Jaszczak, R. Coleman
In SPECT and PET imaging, radiopharmaceutical concentration is often strongly correlated with anatomical structure. A Bayesian image reconstruction procedure is presented that uses this a priori knowledge to improve the detection and quantification of an unknown number of lesions. The a priori distribution employed encourages the emission tomography segmentation to stay close to the anatomical segmentation. Departures from the anatomical segmentation are detected by calculating and segmenting a deviances image: Let n/sub i/ be the estimated number of photons emitted from voxel i, /spl mu//sub ri/ the estimated mean activity of the region that contains voxel i, and l(/spl lambda//sub i/;n/sub i/) the Poisson log likelihood function for /spl lambda//sub i/, where /spl lambda//sub i/ is the mean of n/sub i/. The deviances are defined as 2(l(n/sub i/;n/sub i/)-l(/spl mu//sub ri/;n/sub i/)). Parts of the image having large deviances are candidates for becoming new regions. Hypothesis testing is performed to determine which of these candidates are justified by the projection data as being new regions. The procedure was tested by adding hot lesions to a bitmap of the Hoffman brain phantom and then simulating noisy projection data. Improvements in detection and quantification of these lesions were observed as compared to FBP and ML-EM reconstructions.<>
{"title":"Improved lesion detection and quantification in emission tomography using anatomical and physiological prior information","authors":"J. Bowsher, V. Johnson, T. Turkington, G.E. Floyd, R. Jaszczak, R. Coleman","doi":"10.1109/NSSMIC.1993.373626","DOIUrl":"https://doi.org/10.1109/NSSMIC.1993.373626","url":null,"abstract":"In SPECT and PET imaging, radiopharmaceutical concentration is often strongly correlated with anatomical structure. A Bayesian image reconstruction procedure is presented that uses this a priori knowledge to improve the detection and quantification of an unknown number of lesions. The a priori distribution employed encourages the emission tomography segmentation to stay close to the anatomical segmentation. Departures from the anatomical segmentation are detected by calculating and segmenting a deviances image: Let n/sub i/ be the estimated number of photons emitted from voxel i, /spl mu//sub ri/ the estimated mean activity of the region that contains voxel i, and l(/spl lambda//sub i/;n/sub i/) the Poisson log likelihood function for /spl lambda//sub i/, where /spl lambda//sub i/ is the mean of n/sub i/. The deviances are defined as 2(l(n/sub i/;n/sub i/)-l(/spl mu//sub ri/;n/sub i/)). Parts of the image having large deviances are candidates for becoming new regions. Hypothesis testing is performed to determine which of these candidates are justified by the projection data as being new regions. The procedure was tested by adding hot lesions to a bitmap of the Hoffman brain phantom and then simulating noisy projection data. Improvements in detection and quantification of these lesions were observed as compared to FBP and ML-EM reconstructions.<<ETX>>","PeriodicalId":287813,"journal":{"name":"1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124919278","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 : 1993-10-31DOI: 10.1109/NSSMIC.1993.373608
P. A. van den Elsen, M. Viergever
Describes an automated, retrospective approach to register CT and MR brain images, using patient related image properties only. Mathematically well founded differential operators in scale space are applied to 2D or 3D image data, resulting in feature images depicting "ridgeness" The CT and MR feature images show similarity which can be used for matching using a fully automatic hierarchical correlation scheme. Results of 2D and 3D matching experiments are presented.<>
{"title":"Automated CT and MR brain image registration using geometrical feature correlation","authors":"P. A. van den Elsen, M. Viergever","doi":"10.1109/NSSMIC.1993.373608","DOIUrl":"https://doi.org/10.1109/NSSMIC.1993.373608","url":null,"abstract":"Describes an automated, retrospective approach to register CT and MR brain images, using patient related image properties only. Mathematically well founded differential operators in scale space are applied to 2D or 3D image data, resulting in feature images depicting \"ridgeness\" The CT and MR feature images show similarity which can be used for matching using a fully automatic hierarchical correlation scheme. Results of 2D and 3D matching experiments are presented.<<ETX>>","PeriodicalId":287813,"journal":{"name":"1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122602107","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 : 1993-10-31DOI: 10.1109/NSSMIC.1993.373572
T. Pan, M. King, D. de Vries, M. Ljungberg
In SPECT imaging of the chest, non-uniform attenuation correction requires use of a patient specific attenuation map. Such a map can be obtained by estimating the regions occupied by (1) the lungs and (2) the soft tissue and bones, and then assigning values of the attenuation coefficient to each region. The authors propose a method to segment such regions from the Compton scatter and photopeak window SPECT slices of Tc-99m Sestamibi studies. The Compton scatter slices are used to segment the body outline, and to estimate the region of the lungs with the anatomic information on the back bone and sternum locations from the photopeak window slices. To investigate the accuracy of using Compton scatter slices in estimating the regions of the body and the lungs, a Monte Carlo SPECT simulation of an anthropomorphic phantom with an activity distribution and noise characteristics similar to patient data was performed. Different activities were simulated in the lungs to study the influence of lung uptake. Energy windows of various widths were simulated for use in locating a suitable Compton scatter window for imaging. In a separate simulation, the map of the probability of scatter interactions (up to third order) from photons originating at a point within the heart was recorded to allow investigation of the contrast provided by the difference in density between the lungs and surrounding bones and soft tissue. The results demonstrated that (1) sufficient contrast can be derived from Compton scatter data for segmentation of the lungs; (2) accuracy of determination of body and lung regions of about 99% and 89%, respectively, can be achieved and (3) a wide energy window away from the photopeak window for recording the scattered events is preferred for the segmentation of lungs.<>
{"title":"Segmentation of the body and lungs from Compton scatter and photopeak window data in SPECT: a Monte Carlo investigation","authors":"T. Pan, M. King, D. de Vries, M. Ljungberg","doi":"10.1109/NSSMIC.1993.373572","DOIUrl":"https://doi.org/10.1109/NSSMIC.1993.373572","url":null,"abstract":"In SPECT imaging of the chest, non-uniform attenuation correction requires use of a patient specific attenuation map. Such a map can be obtained by estimating the regions occupied by (1) the lungs and (2) the soft tissue and bones, and then assigning values of the attenuation coefficient to each region. The authors propose a method to segment such regions from the Compton scatter and photopeak window SPECT slices of Tc-99m Sestamibi studies. The Compton scatter slices are used to segment the body outline, and to estimate the region of the lungs with the anatomic information on the back bone and sternum locations from the photopeak window slices. To investigate the accuracy of using Compton scatter slices in estimating the regions of the body and the lungs, a Monte Carlo SPECT simulation of an anthropomorphic phantom with an activity distribution and noise characteristics similar to patient data was performed. Different activities were simulated in the lungs to study the influence of lung uptake. Energy windows of various widths were simulated for use in locating a suitable Compton scatter window for imaging. In a separate simulation, the map of the probability of scatter interactions (up to third order) from photons originating at a point within the heart was recorded to allow investigation of the contrast provided by the difference in density between the lungs and surrounding bones and soft tissue. The results demonstrated that (1) sufficient contrast can be derived from Compton scatter data for segmentation of the lungs; (2) accuracy of determination of body and lung regions of about 99% and 89%, respectively, can be achieved and (3) a wide energy window away from the photopeak window for recording the scattered events is preferred for the segmentation of lungs.<<ETX>>","PeriodicalId":287813,"journal":{"name":"1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference","volume":"39 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122809461","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 : 1993-10-31DOI: 10.1109/NSSMIC.1993.701666
G. Tonelli
{"title":"The Silicon Tracker Of CMS","authors":"G. Tonelli","doi":"10.1109/NSSMIC.1993.701666","DOIUrl":"https://doi.org/10.1109/NSSMIC.1993.701666","url":null,"abstract":"","PeriodicalId":287813,"journal":{"name":"1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121881780","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 : 1993-10-31DOI: 10.1109/NSSMIC.1993.373570
S. Kumamura, N. Niki, Hiromu Nishitani, H. Sato
The authors describe a method for an accurate extraction image of cerebral soft tissues from MR images in order to realize accurate diagnosis. With MRI it is possible to observe different soft tissues images of an anatomical section using different pulse sequences. However, it is difficult to 3D visualize one soft tissue with fuzzy shapes from MR images. To avoid this difficulty the authors used a combination of multichannel MR images, a fuzzy c-means clustering, and an object connectivity-check. Using volume rendering the authors could visualize a 3D extraction image of the brain and tumor.<>
{"title":"3D visualization of fuzzy shapes using multichannel MR images","authors":"S. Kumamura, N. Niki, Hiromu Nishitani, H. Sato","doi":"10.1109/NSSMIC.1993.373570","DOIUrl":"https://doi.org/10.1109/NSSMIC.1993.373570","url":null,"abstract":"The authors describe a method for an accurate extraction image of cerebral soft tissues from MR images in order to realize accurate diagnosis. With MRI it is possible to observe different soft tissues images of an anatomical section using different pulse sequences. However, it is difficult to 3D visualize one soft tissue with fuzzy shapes from MR images. To avoid this difficulty the authors used a combination of multichannel MR images, a fuzzy c-means clustering, and an object connectivity-check. Using volume rendering the authors could visualize a 3D extraction image of the brain and tumor.<<ETX>>","PeriodicalId":287813,"journal":{"name":"1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131801531","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 : 1993-10-31DOI: 10.1109/NSSMIC.1993.701683
V. Eremin, W. Chen, Z. Li
A new analytical, one dimensional method to obtain the induced current shapes and simulation of chasrge shapes for p{sup +} {minus}n{minus}n{sup +} silicon detectors in the case of minimum ionization particle has been developed here. jExact solutions have been found for both electron and hole current shapes. Simulations of induced charge shapes of detectors have also been given. The results of this work are consistent with the earlier work where a semi-analytical method had been used.
{"title":"Analytical Solutions Of Minimum Ionization Particle Induced Current Shapes Of Silicon Detectors And Simulation Of Charge Collection Properties","authors":"V. Eremin, W. Chen, Z. Li","doi":"10.1109/NSSMIC.1993.701683","DOIUrl":"https://doi.org/10.1109/NSSMIC.1993.701683","url":null,"abstract":"A new analytical, one dimensional method to obtain the induced current shapes and simulation of chasrge shapes for p{sup +} {minus}n{minus}n{sup +} silicon detectors in the case of minimum ionization particle has been developed here. jExact solutions have been found for both electron and hole current shapes. Simulations of induced charge shapes of detectors have also been given. The results of this work are consistent with the earlier work where a semi-analytical method had been used.","PeriodicalId":287813,"journal":{"name":"1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134353378","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 : 1993-10-31DOI: 10.1109/NSSMIC.1993.701854
W. Ge, H.K. Lee, O. Nalcioglu
A new iterative nonlinear least squares fitting technique is developed to fit the NMR free induction decay (FlD) signals in the time domain. The new technique makes it possible fitting all the parameters, e.g., frequencies, decay factors, amplitudes and phases, simultaneously. The corresponding initial values are obtained by linear prediction singular value decomposition (LPSVD)[ 11, which is a completely automatic process without any manual processing. The application of the new fitting technique yields a list of fitted parameters. Since the fitting process is carried out in the time domain, it is possible to fit truncated signals or the ones with shorter duration without a degradation of the resolution. The new technique also enables one to resolve close and overlapping frequency components which can not be resolved by fast Fourier transform (FFT) alone[2]. The FFT is used to provide initial frequencies for some weak components in case of low signal-to-noise ratio, but only as a complementary procedure.
{"title":"Simultaneous Nonlinear Least Squares Fitting Technique For NMR Spectroscopy","authors":"W. Ge, H.K. Lee, O. Nalcioglu","doi":"10.1109/NSSMIC.1993.701854","DOIUrl":"https://doi.org/10.1109/NSSMIC.1993.701854","url":null,"abstract":"A new iterative nonlinear least squares fitting technique is developed to fit the NMR free induction decay (FlD) signals in the time domain. The new technique makes it possible fitting all the parameters, e.g., frequencies, decay factors, amplitudes and phases, simultaneously. The corresponding initial values are obtained by linear prediction singular value decomposition (LPSVD)[ 11, which is a completely automatic process without any manual processing. The application of the new fitting technique yields a list of fitted parameters. Since the fitting process is carried out in the time domain, it is possible to fit truncated signals or the ones with shorter duration without a degradation of the resolution. The new technique also enables one to resolve close and overlapping frequency components which can not be resolved by fast Fourier transform (FFT) alone[2]. The FFT is used to provide initial frequencies for some weak components in case of low signal-to-noise ratio, but only as a complementary procedure.","PeriodicalId":287813,"journal":{"name":"1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134526905","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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