SIAM Journal on Imaging Sciences最新文献_第10页

Centering Noisy Images with Application to Cryo-EM. 噪声图像定心及其在低温电镜中的应用。

IF 2.1 3区数学

SIAM Journal on Imaging Sciences Pub Date : 2021-01-01 Epub Date: 2021-05-25 DOI: 10.1137/20m1365946

Ayelet Heimowitz, Nir Sharon, Amit Singer

{"title":"Centering Noisy Images with Application to Cryo-EM.","authors":"Ayelet Heimowitz, Nir Sharon, Amit Singer","doi":"10.1137/20m1365946","DOIUrl":"https://doi.org/10.1137/20m1365946","url":null,"abstract":"We target the problem of estimating the center of mass of objects in noisy two-dimensional images. We assume that the noise dominates the image, and thus many standard approaches are vulnerable to estimation errors, e.g., the direct computation of the center of mass and the geometric median which is a robust alternative to the center of mass. In this paper, we define a novel surrogate function to the center of mass. We present a mathematical and numerical analysis of our method and show that it outperforms existing methods for estimating the center of mass of an object in various realistic scenarios. As a case study, we apply our centering method to data from single-particle cryo-electron microscopy (cryo-EM), where the goal is to reconstruct the three-dimensional structure of macromolecules. We show how to apply our approach for a better translational alignment of molecule images picked from experimental data. In this way, we facilitate the succeeding steps of reconstruction and streamline the entire cryo-EM pipeline, saving computational time and supporting resolution enhancement.","PeriodicalId":49528,"journal":{"name":"SIAM Journal on Imaging Sciences","volume":"14 2","pages":"689-716"},"PeriodicalIF":2.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8813033/pdf/nihms-1739531.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39591769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Recovery of surfaces and functions in high dimensions: sampling theory and links to neural networks. 高维曲面和函数的恢复：采样理论和神经网络链接。

IF 2.1 3区数学

SIAM Journal on Imaging Sciences Pub Date : 2021-01-01 Epub Date: 2021-05-10 DOI: 10.1137/20M1340654

Qing Zou, Mathews Jacob

{"title":"Recovery of surfaces and functions in high dimensions: sampling theory and links to neural networks.","authors":"Qing Zou, Mathews Jacob","doi":"10.1137/20M1340654","DOIUrl":"10.1137/20M1340654","url":null,"abstract":"Several imaging algorithms including patch-based image denoising, image time series recovery, and convolutional neural networks can be thought of as methods that exploit the manifold structure of signals. While the empirical performance of these algorithms is impressive, the understanding of recovery of the signals and functions that live on manifold is less understood. In this paper, we focus on the recovery of signals that live on a union of surfaces. In particular, we consider signals living on a union of smooth band-limited surfaces in high dimensions. We show that an exponential mapping transforms the data to a union of low-dimensional subspaces. Using this relation, we introduce a sampling theoretical framework for the recovery of smooth surfaces from few samples and the learning of functions living on smooth surfaces. The low-rank property of the features is used to determine the number of measurements needed to recover the surface. Moreover, the low-rank property of the features also provides an efficient approach, which resembles a neural network, for the local representation of multidimensional functions on the surface. The direct representation of such a function in high dimensions often suffers from the curse of dimensionality; the large number of parameters would translate to the need for extensive training data. The low-rank property of the features can significantly reduce the number of parameters, which makes the computational structure attractive for learning and inference from limited labeled training data.","PeriodicalId":49528,"journal":{"name":"SIAM Journal on Imaging Sciences","volume":"14 2","pages":"580-619"},"PeriodicalIF":2.1,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8323788/pdf/nihms-1673810.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39265387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian. 谱嵌入规范：深入探究图形拉普拉奇的频谱

IF 2.1 3区数学

SIAM Journal on Imaging Sciences Pub Date : 2020-01-01 Epub Date: 2020-06-30 DOI: 10.1137/18m1283160

Xiuyuan Cheng, Gal Mishne

引用次数: 0

Simplifying Transforms for General Elastic Metrics on the Space of Plane Curves. 平面曲线空间上一般弹性度量的简化变换。

IF 2.1 3区数学

SIAM Journal on Imaging Sciences Pub Date : 2020-01-01 Epub Date: 2020-03-12 DOI: 10.1137/19m1265132

Tom Needham, Sebastian Kurtek

{"title":"Simplifying Transforms for General Elastic Metrics on the Space of Plane Curves.","authors":"Tom Needham, Sebastian Kurtek","doi":"10.1137/19m1265132","DOIUrl":"https://doi.org/10.1137/19m1265132","url":null,"abstract":"In the shape analysis approach to computer vision problems, one treats shapes as points in an infinite-dimensional Riemannian manifold, thereby facilitating algorithms for statistical calculations such as geodesic distance between shapes and averaging of a collection of shapes. The performance of these algorithms depends heavily on the choice of the Riemannian metric. In the setting of plane curve shapes, attention has largely been focused on a two-parameter family of first order Sobolev metrics, referred to as elastic metrics. They are particularly useful due to the existence of simplifying coordinate transformations for particular parameter values, such as the well-known square-root velocity transform. In this paper, we extend the transformations appearing in the existing literature to a family of isometries, which take any elastic metric to the flat L 2 metric. We also extend the transforms to treat piecewise linear curves and demonstrate the existence of optimal matchings over the diffeomorphism group in this setting. We conclude the paper with multiple examples of shape geodesics for open and closed curves. We also show the benefits of our approach in a simple classification experiment.","PeriodicalId":49528,"journal":{"name":"SIAM Journal on Imaging Sciences","volume":"13 1","pages":"445-473"},"PeriodicalIF":2.1,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1137/19m1265132","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39316946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21

Accelerated Optimization in the PDE Framework Formulations for the Active Contour Case. 主动轮廓情况下的 PDE 框架公式加速优化。

IF 2.1 3区数学

SIAM Journal on Imaging Sciences Pub Date : 2020-01-01 Epub Date: 2020-11-19 DOI: 10.1137/19m1304210

Anthony Yezzi, Ganesh Sundaramoorthi, Minas Benyamin

{"title":"Accelerated Optimization in the PDE Framework Formulations for the Active Contour Case.","authors":"Anthony Yezzi, Ganesh Sundaramoorthi, Minas Benyamin","doi":"10.1137/19m1304210","DOIUrl":"10.1137/19m1304210","url":null,"abstract":"Following the seminal work of Nesterov, accelerated optimization methods have been used to powerfully boost the performance of first-order, gradient based parameter estimation in scenarios where second-order optimization strategies are either inapplicable or impractical. Not only does accelerated gradient descent converge considerably faster than traditional gradient descent, but it also performs a more robust local search of the parameter space by initially overshooting and then oscillating back as it settles into a final configuration, thereby selecting only local minimizers with a basis of attraction large enough to contain the initial overshoot. This behavior has made accelerated and stochastic gradient search methods particularly popular within the machine learning community. In their recent PNAS 2016 paper, A Variational Perspective on Accelerated Methods in Optimization, Wibisono, Wilson, and Jordan demonstrate how a broad class of accelerated schemes can be cast in a variational framework formulated around the Bregman divergence, leading to continuum limit ODEs. We show how their formulation may be further extended to infinite dimensional manifolds (starting here with the geometric space of curves and surfaces) by substituting the Bregman divergence with inner products on the tangent space and explicitly introducing a distributed mass model which evolves in conjunction with the object of interest during the optimization process. The coevolving mass model, which is introduced purely for the sake of endowing the optimization with helpful dynamics, also links the resulting class of accelerated PDE based optimization schemes to fluid dynamical formulations of optimal mass transport.","PeriodicalId":49528,"journal":{"name":"SIAM Journal on Imaging Sciences","volume":"13 4","pages":"2029-2062"},"PeriodicalIF":2.1,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8320808/pdf/nihms-1676294.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39265386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MULTI-ENERGY CONE-BEAM CT RECONSTRUCTION WITH A SPATIAL SPECTRAL NONLOCAL MEANS ALGORITHM. 基于空间谱非局部均值算法的多能锥束ct重建。

IF 2.1 3区数学

SIAM Journal on Imaging Sciences Pub Date : 2018-01-01 Epub Date: 2018-05-08 DOI: 10.1137/17M1123237

Bin Li, Chenyang Shen, Yujie Chi, Ming Yang, Yifei Lou, Linghong Zhou, Xun Jia

{"title":"MULTI-ENERGY CONE-BEAM CT RECONSTRUCTION WITH A SPATIAL SPECTRAL NONLOCAL MEANS ALGORITHM.","authors":"Bin Li, Chenyang Shen, Yujie Chi, Ming Yang, Yifei Lou, Linghong Zhou, Xun Jia","doi":"10.1137/17M1123237","DOIUrl":"https://doi.org/10.1137/17M1123237","url":null,"abstract":"Multi-energy computed tomography (CT) is an emerging medical image modality with a number of potential applications in diagnosis and therapy. However, high system cost and technical barriers obstruct its step into routine clinical practice. In this study, we propose a framework to realize multi-energy cone beam CT (ME-CBCT) on the CBCT system that is widely available and has been routinely used for radiotherapy image guidance. In our method, a kVp switching technique is realized, which acquires x-ray projections with kVp levels cycling through a number of values. For this kVp-switching based ME-CBCT acquisition, x-ray projections of each energy channel are only a subset of all the acquired projections. This leads to an undersampling issue, posing challenges to the reconstruction problem. We propose a spatial spectral non-local means (ssNLM) method to reconstruct ME-CBCT, which employs image correlations along both spatial and spectral directions to suppress noisy and streak artifacts. To address the intensity scale difference at different energy channels, a histogram matching method is incorporated. Our method is different from conventionally used NLM methods in that spectral dimension is included, which helps to effectively remove streak artifacts appearing at different directions in images with different energy channels. Convergence analysis of our algorithm is provided. A comprehensive set of simulation and real experimental studies demonstrate feasibility of our ME-CBCT scheme and the capability of achieving superior image quality compared to conventional filtered backprojection-type (FBP) and NLM reconstruction methods.","PeriodicalId":49528,"journal":{"name":"SIAM Journal on Imaging Sciences","volume":"11 2","pages":"1205-1229"},"PeriodicalIF":2.1,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1137/17M1123237","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36568105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Parameterized joint reconstruction of the initial pressure and sound speed distributions for photoacoustic computed tomography. 用于光声计算机断层扫描的初始压力和声速分布的参数化联合重建。

IF 2.1 3区数学

SIAM Journal on Imaging Sciences Pub Date : 2018-01-01 Epub Date: 2018-06-05 DOI: 10.1137/17M1153649

Thomas P Matthews, Joemini Poudel, Lei Li, Lihong V Wang, Mark A Anastasio

{"title":"Parameterized joint reconstruction of the initial pressure and sound speed distributions for photoacoustic computed tomography.","authors":"Thomas P Matthews, Joemini Poudel, Lei Li, Lihong V Wang, Mark A Anastasio","doi":"10.1137/17M1153649","DOIUrl":"10.1137/17M1153649","url":null,"abstract":"Accurate estimation of the initial pressure distribution in photoacoustic computed tomography (PACT) depends on knowledge of the sound speed distribution. However, the sound speed distribution is typically unknown. Further, the initial pressure and sound speed distributions cannot both, in general, be stably recovered from PACT measurements alone. In this work, a joint reconstruction (JR) method for the initial pressure distribution and a low-dimensional parameterized model of the sound speed distribution is proposed. By employing a priori information about the structure of the sound speed distribution, both the initial pressure and sound speed can be accurately recovered. The JR problem is solved by use of a proximal optimization method that allows constraints and non-smooth regularization functions for the initial pressure distribution. The gradients of the cost function with respect to the initial pressure and sound speed distributions are calculated by use of an adjoint state method that has the same per-iteration computational cost as calculating the gradient with respect to the initial pressure distribution alone. This approach is evaluated through 2D computer-simulation studies for a small animal imaging model and by application to experimental in vivo measurements of a mouse.","PeriodicalId":49528,"journal":{"name":"SIAM Journal on Imaging Sciences","volume":"11 2","pages":"1560-1588"},"PeriodicalIF":2.1,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6447310/pdf/nihms-1015338.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37127613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Structural Variability from Noisy Tomographic Projections. 噪声层析成像投影的结构变异性。

IF 4.6 3区数学

SIAM Journal on Imaging Sciences Pub Date : 2018-01-01 Epub Date: 2018-05-31 DOI: 10.1137/17M1153509

Joakim Andén, Amit Singer

{"title":"Structural Variability from Noisy Tomographic Projections.","authors":"Joakim Andén, Amit Singer","doi":"10.1137/17M1153509","DOIUrl":"10.1137/17M1153509","url":null,"abstract":"In cryo-electron microscopy, the three-dimensional (3D) electric potentials of an ensemble of molecules are projected along arbitrary viewing directions to yield noisy two-dimensional images. The volume maps representing these potentials typically exhibit a great deal of structural variability, which is described by their 3D covariance matrix. Typically, this covariance matrix is approximately low rank and can be used to cluster the volumes or estimate the intrinsic geometry of the conformation space. We formulate the estimation of this covariance matrix as a linear inverse problem, yielding a consistent least-squares estimator. For n images of size N-by-N pixels, we propose an algorithm for calculating this covariance estimator with computational complexity <math><mrow><mi>O</mi> <mo>(</mo> <mi>n</mi> <msup><mi>N</mi> <mn>4</mn></msup> <mo>+</mo> <msqrt><mi>κ</mi></msqrt> <msup><mi>N</mi> <mn>6</mn></msup> <mo>log</mo> <mi>N</mi> <mo>)</mo></mrow> </math> , where the condition number κ is empirically in the range 10-200. Its efficiency relies on the observation that the normal equations are equivalent to a deconvolution problem in six dimensions. This is then solved by the conjugate gradient method with an appropriate circulant preconditioner. The result is the first computationally efficient algorithm for consistent estimation of the 3D covariance from noisy projections. It also compares favorably in runtime with respect to previously proposed nonconsistent estimators. Motivated by the recent success of eigenvalue shrinkage procedures for high-dimensional covariance matrix estimation, we incorporate a shrinkage procedure that improves accuracy at lower signal-to-noise ratios. We evaluate our methods on simulated datasets and achieve classification results comparable to state-of-the-art methods in shorter running time. We also present results on clustering volumes in an experimental dataset, illustrating the power of the proposed algorithm for practical determination of structural variability.","PeriodicalId":49528,"journal":{"name":"SIAM Journal on Imaging Sciences","volume":"11 2","pages":"1441-1492"},"PeriodicalIF":4.6,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6294454/pdf/nihms975632.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36830690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ACTIVE MEAN FIELDS FOR PROBABILISTIC IMAGE SEGMENTATION: CONNECTIONS WITH CHAN-VESE AND RUDIN-OSHER-FATEMI MODELS. 概率图像分割的主动平均场:与chanvese和rudin-osher-fatemi模型的联系。

IF 2.1 3区数学

SIAM Journal on Imaging Sciences Pub Date : 2017-01-01 Epub Date: 2017-07-27 DOI: 10.1137/16M1058601

Marc Niethammer, Kilian M Pohl, Firdaus Janoos, William M Wells

{"title":"ACTIVE MEAN FIELDS FOR PROBABILISTIC IMAGE SEGMENTATION: CONNECTIONS WITH CHAN-VESE AND RUDIN-OSHER-FATEMI MODELS.","authors":"Marc Niethammer, Kilian M Pohl, Firdaus Janoos, William M Wells","doi":"10.1137/16M1058601","DOIUrl":"https://doi.org/10.1137/16M1058601","url":null,"abstract":"Segmentation is a fundamental task for extracting semantically meaningful regions from an image. The goal of segmentation algorithms is to accurately assign object labels to each image location. However, image-noise, shortcomings of algorithms, and image ambiguities cause uncertainty in label assignment. Estimating the uncertainty in label assignment is important in multiple application domains, such as segmenting tumors from medical images for radiation treatment planning. One way to estimate these uncertainties is through the computation of posteriors of Bayesian models, which is computationally prohibitive for many practical applications. On the other hand, most computationally efficient methods fail to estimate label uncertainty. We therefore propose in this paper the Active Mean Fields (AMF) approach, a technique based on Bayesian modeling that uses a mean-field approximation to efficiently compute a segmentation and its corresponding uncertainty. Based on a variational formulation, the resulting convex model combines any label-likelihood measure with a prior on the length of the segmentation boundary. A specific implementation of that model is the Chan-Vese segmentation model (CV), in which the binary segmentation task is defined by a Gaussian likelihood and a prior regularizing the length of the segmentation boundary. Furthermore, the Euler-Lagrange equations derived from the AMF model are equivalent to those of the popular Rudin-Osher-Fatemi (ROF) model for image denoising. Solutions to the AMF model can thus be implemented by directly utilizing highly-efficient ROF solvers on log-likelihood ratio fields. We qualitatively assess the approach on synthetic data as well as on real natural and medical images. For a quantitative evaluation, we apply our approach to the icgbench dataset.","PeriodicalId":49528,"journal":{"name":"SIAM Journal on Imaging Sciences","volume":"10 3","pages":"1069-1103"},"PeriodicalIF":2.1,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1137/16M1058601","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35625157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A forward-adjoint operator pair based on the elastic wave equation for use in transcranial photoacoustic computed tomography. 基于弹性波动方程的前向伴随算子对在经颅光声计算机断层扫描中的应用。

IF 2.1 3区数学

SIAM Journal on Imaging Sciences Pub Date : 2017-01-01 Epub Date: 2017-11-16 DOI: 10.1137/16M1107619

Kenji Mitsuhashi, Joemini Poudel, Thomas P Matthews, Alejandro Garcia-Uribe, Lihong V Wang, Mark A Anastasio

{"title":"A forward-adjoint operator pair based on the elastic wave equation for use in transcranial photoacoustic computed tomography.","authors":"Kenji Mitsuhashi, Joemini Poudel, Thomas P Matthews, Alejandro Garcia-Uribe, Lihong V Wang, Mark A Anastasio","doi":"10.1137/16M1107619","DOIUrl":"10.1137/16M1107619","url":null,"abstract":"Photoacoustic computed tomography (PACT) is an emerging imaging modality that exploits optical contrast and ultrasonic detection principles to form images of the photoacoustically induced initial pressure distribution within tissue. The PACT reconstruction problem corresponds to an inverse source problem in which the initial pressure distribution is recovered from measurements of the radiated wavefield. A major challenge in transcranial PACT brain imaging is compensation for aberrations in the measured data due to the presence of the skull. Ultrasonic waves undergo absorption, scattering and longitudinal-to-shear wave mode conversion as they propagate through the skull. To properly account for these effects, a wave-equation-based inversion method should be employed that can model the heterogeneous elastic properties of the skull. In this work, a forward model based on a finite-difference time-domain discretization of the three-dimensional elastic wave equation is established and a procedure for computing the corresponding adjoint of the forward operator is presented. Massively parallel implementations of these operators employing multiple graphics processing units (GPUs) are also developed. The developed numerical framework is validated and investigated in computer19 simulation and experimental phantom studies whose designs are motivated by transcranial PACT applications.","PeriodicalId":49528,"journal":{"name":"SIAM Journal on Imaging Sciences","volume":"10 4","pages":"2022-2048"},"PeriodicalIF":2.1,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1137/16M1107619","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35781227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 25