Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193013
J. Shtok, Michael Elad, M. Zibulevsky
This work concerns with linear and spatially-adaptive direct reconstruction algorithms for 2-D parallel-beam transmission tomography, extending the Filtered Back-Projection (FBP). The standard apodized Ram-Lak filter kernel is replaced with a bank of statistically trained 2-D convolution kernels, leading to improved reconstruction results. Two types of filter training procedures are considered. The first deals with reconstruction from noisy and truncated projections in a predefined region of interest, for images from a known family. In the second algorithm, termed SPADES, the training aims at improving the impulse response properties of the overall projection-reconstruction scheme. In this algorithm, the degree of smoothing applied to the reconstructed image is spatially controlled by a switch rule. Both methods are shown by simulations to operate well and lead to substantially improved reconstruction results.
{"title":"Direct adaptive algorithms for CT reconstruction","authors":"J. Shtok, Michael Elad, M. Zibulevsky","doi":"10.1109/ISBI.2009.5193013","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193013","url":null,"abstract":"This work concerns with linear and spatially-adaptive direct reconstruction algorithms for 2-D parallel-beam transmission tomography, extending the Filtered Back-Projection (FBP). The standard apodized Ram-Lak filter kernel is replaced with a bank of statistically trained 2-D convolution kernels, leading to improved reconstruction results. Two types of filter training procedures are considered. The first deals with reconstruction from noisy and truncated projections in a predefined region of interest, for images from a known family. In the second algorithm, termed SPADES, the training aims at improving the impulse response properties of the overall projection-reconstruction scheme. In this algorithm, the degree of smoothing applied to the reconstructed image is spatially controlled by a switch rule. Both methods are shown by simulations to operate well and lead to substantially improved reconstruction results.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127116674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193209
Jonathan Bates, Y. Wang, Xiuwen Liu, W. Mio
We develop an algorithm for the registration of surfaces representing the contours of various subcortical structures of the human brain. We employ a scale-space representation of shape based on the heat kernel, which only depends on the intrinsic geometry of the surfaces. The multi-scale representation is used in conjunction with the non-linear Iterative Closest Point algorithm based on thin-plate-spline warps to establish point correspondences between shapes. The method is applied to the registration of the contours of four subcortical structures: the hippocampus, caudate nucleus, putamen, and third ventricle.
{"title":"Registration of contours of brain structures through a heat-kernel representation of shape","authors":"Jonathan Bates, Y. Wang, Xiuwen Liu, W. Mio","doi":"10.1109/ISBI.2009.5193209","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193209","url":null,"abstract":"We develop an algorithm for the registration of surfaces representing the contours of various subcortical structures of the human brain. We employ a scale-space representation of shape based on the heat kernel, which only depends on the intrinsic geometry of the surfaces. The multi-scale representation is used in conjunction with the non-linear Iterative Closest Point algorithm based on thin-plate-spline warps to establish point correspondences between shapes. The method is applied to the registration of the contours of four subcortical structures: the hippocampus, caudate nucleus, putamen, and third ventricle.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133033976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193123
Aurobrata Ghosh, Maher Moakher, R. Deriche
In Diffusion Magnetic Resonance Imaging (D-MRI), the 2nd order diffusion tensor has given rise to a widely used tool — Diffusion Tensor Imaging (DTI). However, it is known that DTI is limited to a single prominent diffusion direction and is inaccurate in regions of complex fiber structures such as crossings. Various other approaches have been introduced to recover such complex tissue micro-geometries, one of which is Higher Order Cartesian Tensors. Estimating a positive diffusion function has also been emphasised mathematically, since diffusion is a physical quantity. Recently there have been efforts to estimate 4th order diffusion tensors from Diffusion Weighted Images (DWIs), which are capable of describing crossing configurations with the added property of a positive diffusion function. We take up one such, the Ternary Quartic approach, and reformulate the estimation equation to facilitate the estimation of the non-negative 4th order diffusion tensor. With our modified approach we test on synthetic, phantom and real data and confirm previous results.
{"title":"Ternary Quartic approach for positive 4th order diffusion tensors revisited","authors":"Aurobrata Ghosh, Maher Moakher, R. Deriche","doi":"10.1109/ISBI.2009.5193123","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193123","url":null,"abstract":"In Diffusion Magnetic Resonance Imaging (D-MRI), the 2nd order diffusion tensor has given rise to a widely used tool — Diffusion Tensor Imaging (DTI). However, it is known that DTI is limited to a single prominent diffusion direction and is inaccurate in regions of complex fiber structures such as crossings. Various other approaches have been introduced to recover such complex tissue micro-geometries, one of which is Higher Order Cartesian Tensors. Estimating a positive diffusion function has also been emphasised mathematically, since diffusion is a physical quantity. Recently there have been efforts to estimate 4th order diffusion tensors from Diffusion Weighted Images (DWIs), which are capable of describing crossing configurations with the added property of a positive diffusion function. We take up one such, the Ternary Quartic approach, and reformulate the estimation equation to facilitate the estimation of the non-negative 4th order diffusion tensor. With our modified approach we test on synthetic, phantom and real data and confirm previous results.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130145617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193281
T. Szilágyi, M. Brady
The (semi-) automatic detection of significant (feature) points is a key task in in vivo assessment of cancer staging and progression. However, this is a challenging task due to the relatively poor signal-to-noise, limited resolution and variable intensity of medical images. We propose to use phase congruence (PC), the Morrone and Owens (1987) feature model, to extract local descriptors. We overcome the limitations of the currently accepted PC measure, estimate PC without using an image energy weighting factor. We show that: (i) relative phase values from a single scale are not equivalent to phase values from PC, and should not be used to assess local image structure; and (ii) our approach results in higher specificity to features of interest, and lower sensitivity to noise, as demonstrated in in vitro microscopy (e.g. tumour microvessels) and in vivo pre-clinical pancreatic cancer images.
{"title":"Feature extraction from cancer images using local phase congruency: A reliable source of image descriptors","authors":"T. Szilágyi, M. Brady","doi":"10.1109/ISBI.2009.5193281","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193281","url":null,"abstract":"The (semi-) automatic detection of significant (feature) points is a key task in in vivo assessment of cancer staging and progression. However, this is a challenging task due to the relatively poor signal-to-noise, limited resolution and variable intensity of medical images. We propose to use phase congruence (PC), the Morrone and Owens (1987) feature model, to extract local descriptors. We overcome the limitations of the currently accepted PC measure, estimate PC without using an image energy weighting factor. We show that: (i) relative phase values from a single scale are not equivalent to phase values from PC, and should not be used to assess local image structure; and (ii) our approach results in higher specificity to features of interest, and lower sensitivity to noise, as demonstrated in in vitro microscopy (e.g. tumour microvessels) and in vivo pre-clinical pancreatic cancer images.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"4 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123897632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193018
C. Seelamantula, N. Pavillon, C. Depeursinge, M. Unser
We address the problem of zero-order-free image reconstruction in digital holographic microscopy. We show how the goal can be achieved by confining the object-wave modulation to one quadrant of the frequency domain, and by maintaining a reference-wave intensity higher than that of the object. The proposed technique is nonlinear, noniterative, and leads to exact reconstruction in the absence of noise. We also provide experimental results on holograms of yew pollen grains to validate the theoretical results.
{"title":"Zero-order-free image reconstruction in digital holographic microscopy","authors":"C. Seelamantula, N. Pavillon, C. Depeursinge, M. Unser","doi":"10.1109/ISBI.2009.5193018","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193018","url":null,"abstract":"We address the problem of zero-order-free image reconstruction in digital holographic microscopy. We show how the goal can be achieved by confining the object-wave modulation to one quadrant of the frequency domain, and by maintaining a reference-wave intensity higher than that of the object. The proposed technique is nonlinear, noniterative, and leads to exact reconstruction in the absence of noise. We also provide experimental results on holograms of yew pollen grains to validate the theoretical results.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121396598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5192978
Fethallah Benmansour, L. Cohen
In this paper we present a new interactive method for tubular structure extraction. The main application and motivation for this work is vessel tracking in 3D medical images. The basic tools are minimal paths solved using the fast marching algorithm. This leads to interactive tools for the physician by clicking on a small number of points in order to obtain a minimal path between two points or a set of paths in the case of a tree structure. Our method is based on a variant of the minimal path method that models the vessel as a centerline and surface by adding one dimension for the local radius around the centerline. The crucial step of our method is the definition of the local metrics to minimize (based on the local orientation using a Riemannian Metric). This approach is made available for the physician using an Object Oriented Language (OOL) interface. We show results on two CT cardiac images for coronary arteries segmentation.
{"title":"A new interactive method for coronary arteries segmentation based on tubular anisotropy","authors":"Fethallah Benmansour, L. Cohen","doi":"10.1109/ISBI.2009.5192978","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5192978","url":null,"abstract":"In this paper we present a new interactive method for tubular structure extraction. The main application and motivation for this work is vessel tracking in 3D medical images. The basic tools are minimal paths solved using the fast marching algorithm. This leads to interactive tools for the physician by clicking on a small number of points in order to obtain a minimal path between two points or a set of paths in the case of a tree structure. Our method is based on a variant of the minimal path method that models the vessel as a centerline and surface by adding one dimension for the local radius around the centerline. The crucial step of our method is the definition of the local metrics to minimize (based on the local orientation using a Riemannian Metric). This approach is made available for the physician using an Object Oriented Language (OOL) interface. We show results on two CT cardiac images for coronary arteries segmentation.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129317250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193317
N. El-Zehiry, Adel Said Elmaghraby
In this paper, we introduce a graph cut based active surface model that incorporates graph cuts optimization tools with implicit surface representation to solve the segmentation problem. We will introduce a discrete formulation of the surface evolution problem, prove that the discrete energy function is graph representable and propose how to optimize it using graph cuts. The advantage of this model is two fold: First, Graph cuts are mostly global optimization tools which makes the model very robust and not sensitive to initialization, moreover, the dynamic labeling associated with graph cuts optimization tools makes the model very fast. Second, the implicit representation of the surface makes it robust to topology changes.
{"title":"An active surface model for volumetric image segmentation","authors":"N. El-Zehiry, Adel Said Elmaghraby","doi":"10.1109/ISBI.2009.5193317","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193317","url":null,"abstract":"In this paper, we introduce a graph cut based active surface model that incorporates graph cuts optimization tools with implicit surface representation to solve the segmentation problem. We will introduce a discrete formulation of the surface evolution problem, prove that the discrete energy function is graph representable and propose how to optimize it using graph cuts. The advantage of this model is two fold: First, Graph cuts are mostly global optimization tools which makes the model very robust and not sensitive to initialization, moreover, the dynamic labeling associated with graph cuts optimization tools makes the model very fast. Second, the implicit representation of the surface makes it robust to topology changes.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128734374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193195
Dan Wang, A. Tewfik
Parametric representation of deformable object with complex surface has been a challenge in various medical applications for its demanding resource consumptions. This paper proposed an efficient algorithm to construct a compact basis for a sequence of deformed 3D organ, in which those surfaces can be sparsely represented with a small number of parameters. The key idea in this paper is to explore the correlations among the deformed surfaces of an organ and extract the principle basis for representation and reconstruction. Both theoretical analysis and extensive simulations verified that the presented algorithm yields a three-order magnitude reduction in computational and storage complexity relative to traditional approaches while maintaining high precision for surface reconstruction. The proposed algorithm can be used for organ deformation tracking and optimal sampling strategy design.
{"title":"Sparse representation of deformable 3D organs","authors":"Dan Wang, A. Tewfik","doi":"10.1109/ISBI.2009.5193195","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193195","url":null,"abstract":"Parametric representation of deformable object with complex surface has been a challenge in various medical applications for its demanding resource consumptions. This paper proposed an efficient algorithm to construct a compact basis for a sequence of deformed 3D organ, in which those surfaces can be sparsely represented with a small number of parameters. The key idea in this paper is to explore the correlations among the deformed surfaces of an organ and extract the principle basis for representation and reconstruction. Both theoretical analysis and extensive simulations verified that the presented algorithm yields a three-order magnitude reduction in computational and storage complexity relative to traditional approaches while maintaining high precision for surface reconstruction. The proposed algorithm can be used for organ deformation tracking and optimal sampling strategy design.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128744576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193059
S. Pallavaram, P. D'haese, M. Remple, J. Neimat, C. Kao, Rui Li, P. Konrad, B. Dawant
Recently, many groups have reported on the occurrence of brain shift in stereotactic surgery and its impact on the procedure. A shift of deep brain structures by only a few millimeters can potentially increase the number of required microelectrode and/or macroelectrode tracks. This can cause complications and potentially affect implantation accuracy. Detecting intra-operative brain shift and, more significantly correcting for it intra-operatively can thus impact the procedure and its outcome. In this study, we have used intra-operative stimulation response data to assess brain shift. Using a shift free functional atlas containing therapeutic response to stimulation (efficacy) data from a population of patients we build statistical efficacy maps on new patients. We then compare the information provided by the maps with the actual intra-operative responses of those patients to detect brain shift. Our preliminary results show that by maximizing the correlation between statistical maps and intra-operative observations, it may be possible to detect intra-operative brain shift and potentially correct for it.
{"title":"Detecting brain shift during deep brain stimulation surgery using intra-operative data and functional atlases: A preliminary study","authors":"S. Pallavaram, P. D'haese, M. Remple, J. Neimat, C. Kao, Rui Li, P. Konrad, B. Dawant","doi":"10.1109/ISBI.2009.5193059","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193059","url":null,"abstract":"Recently, many groups have reported on the occurrence of brain shift in stereotactic surgery and its impact on the procedure. A shift of deep brain structures by only a few millimeters can potentially increase the number of required microelectrode and/or macroelectrode tracks. This can cause complications and potentially affect implantation accuracy. Detecting intra-operative brain shift and, more significantly correcting for it intra-operatively can thus impact the procedure and its outcome. In this study, we have used intra-operative stimulation response data to assess brain shift. Using a shift free functional atlas containing therapeutic response to stimulation (efficacy) data from a population of patients we build statistical efficacy maps on new patients. We then compare the information provided by the maps with the actual intra-operative responses of those patients to detect brain shift. Our preliminary results show that by maximizing the correlation between statistical maps and intra-operative observations, it may be possible to detect intra-operative brain shift and potentially correct for it.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115827464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-06-28DOI: 10.1109/ISBI.2009.5193003
Alexandre Gramfort, M. Kowalski
The inverse problem with distributed dipoles models in M/EEG is strongly ill-posed requiring to set priors on the solution. Most common priors are based on a convenient ℓ2 norm. However such methods are known to smear the estimated distribution of cortical currents. In order to provide sparser solutions, other norms than ℓ2 have been proposed in the literature, but they often do not pass the test of real data. Here we propose to perform the inverse problem on multiple experimental conditions simultaneously and to constrain the corresponding active regions to be different, while preserving the robust ℓ2 prior over space and time. This approach is based on a mixed norm that sets a ℓ1 prior between conditions. The optimization is performed with an efficient iterative algorithm able to handle highly sampled distributed models. The method is evaluated on two synthetic datasets reproducing the organization of the primary somatosensory cortex (S1) and the primary visual cortex (V1), and validated with MEG somatosensory data.
{"title":"Improving M/EEG source localizationwith an inter-condition sparse prior","authors":"Alexandre Gramfort, M. Kowalski","doi":"10.1109/ISBI.2009.5193003","DOIUrl":"https://doi.org/10.1109/ISBI.2009.5193003","url":null,"abstract":"The inverse problem with distributed dipoles models in M/EEG is strongly ill-posed requiring to set priors on the solution. Most common priors are based on a convenient ℓ2 norm. However such methods are known to smear the estimated distribution of cortical currents. In order to provide sparser solutions, other norms than ℓ2 have been proposed in the literature, but they often do not pass the test of real data. Here we propose to perform the inverse problem on multiple experimental conditions simultaneously and to constrain the corresponding active regions to be different, while preserving the robust ℓ2 prior over space and time. This approach is based on a mixed norm that sets a ℓ1 prior between conditions. The optimization is performed with an efficient iterative algorithm able to handle highly sampled distributed models. The method is evaluated on two synthetic datasets reproducing the organization of the primary somatosensory cortex (S1) and the primary visual cortex (V1), and validated with MEG somatosensory data.","PeriodicalId":272938,"journal":{"name":"2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116336171","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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