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Fast local reconstruction by selective backprojection for low dose in dental computed tomography

Chinese Physics C 2014年第10期38卷 117-125页

作者：闫镔邓林韩玉张峰汪先超李磊 National Digital Switching System Engineering and Technological Research Center

The high radiation dose in computed tomography （CT） scans increases the lifetime risk of cancer, which becomes a major clinical concern. The backprojection-filtration （BPF） algorithm could reduce the radiation dose by reconstructing the images from truncated data in a short scan. In a dental CT, it could reduce the radiation dose for the teeth by using the projection acquired in a short scan, and could avoid irradiation to the other part by using truncated projection. However, the limit of integration for backprojection varies per PI-line, resulting in low calculation efficiency and poor parallel performance. Recently, a tent BPF has been proposed to improve the calculation efficiency by rearranging the projection. However, the memory-consuming data rebinning process is included. Accordingly, the selective BPF （S-BPF） algorithm is proposed in this paper. In this algorithm, the derivative of the projection is backprojected to the points whose x coordinate is less than that of the source focal spot to obtain the differentiated backprojection. The finite Hilbert inverse is then applied to each PI-line segment. S-BPF avoids the influence of the variable limit of integration by selective backprojection without additional time cost or memory cost. The simulation experiment and the real experiment demonstrated the higher reconstruction efficiency of S-BPF.

关键词： local reconstruction dental CT selective backprojection short scan

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local reconstruction and Dissimilarity Preserving Semi-Supervised Dimensionality Reduction

Local Reconstruction and Dissimilarity Preserving Semi-Super...

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2013年中国智能自动化学术会议

作者： Feng Li Zhengqun Wang Zhongxia Zhou Wei Xue Department of Automation College of Information and EngineeringYangzhou University

In this paper,a semi-supervised dimensionality reduction algorithm for feature extraction,named LRDPSSDR,is proposed by combining local reconstruction with dissimilarity *** focuses on local and global structure based on labeled and unlabeled samples in learning *** sets the edge weights of adjacency graph by minimizing the local reconstruction error and preserves local geometric structure of ***,the dissimilarity between samples is represented by maximizing global scatter matrix so that the global manifold structure can be preserved *** comparison and extensive experiments demonstrate the effectiveness of LRDPSSDR.

关键词： local reconstruction Dissimilarity preserving Semi-supervised dimensionality reduction Face recognition

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Relative manifold based semi-supervised dimensionality reduction

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Frontiers of Computer Science 2014年第6期8卷 923-932页

作者： Xianfa CAI Guihua WEN Jia WEI Zhiwen YU School of Medical Information Engineering Guangdong Pharmaceutical University Guangzhou 510006 China School of Computer Science and Engineering South China University of Technology Guangzhou 510006 China Shenzhen Key Laboratory of High Performance Data Mining Shenzhen 518055 China

A well-designed graph plays a fundamental role in graph-based semi-supervised learning; however, the topological structure of a constructed neighborhood is unstable in most current approaches, since they are very sensitive to the high dimensional, sparse and noisy data. This generally leads to dramatic performance degradation. To deal with this issue, we developed a relative manifold based semisupervised dimensionality reduction （RMSSDR） approach by utilizing the relative manifold to construct a better neighborhood graph with fewer short-circuit edges. Based on the relative cognitive law and manifold distance, a relative transformation is used to construct the relative space and the relative manifold. A relative transformation can improve the ability to distinguish between data points and reduce the impact of noise such that it may be more intuitive, and the relative manifold can more truly reflect the manifold structure since data sets commonly exist in a nonlinear structure. Specifically, RMSSDR makes full use of pairwise constraints that can define the edge weights of the neighborhood graph by minimizing the local reconstruction error and can preserve the global and local geometric structures of the data set. The experimental results on face data sets demonstrate that RMSSDR is better than the current state of the art comparing methods in both performance of classification and robustness.

关键词： cognitive law relative transformation relative manifold local reconstruction semi-supervised learning

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Advancement flaps

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Plastic and Aesthetic Research 2022年第1期9卷 264-273页

作者： Heather Schopper J.David Kriet Clinton Humphrey Department of Otolaryngology-Head and Neck Surgery University of Kansas Medical CenterKansas CityKS 66160USA

local advancement flaps are a key tool in the armamentarium of the reconstructive *** can be used to repair small and large defects on all areas of the face with excellent skin color and texture match,limited donor site morbidity,and flexibility to hide *** this review,we outline common categories of advancement flaps and discuss common situations for their use.

关键词： Advancement flaps local reconstruction facial cutaneous defects

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Particle segmentation algorithm for flexible single particle reconstruction

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Biophysics Reports 2017年第Z1期3卷 43-55页

作者： Qiang Zhou Niyun Zhou Hong-Wei Wang State Key Laboratory of Biomembrane and Membrane Biotechnology Center for Structural Biology School of Life Sciences Tsinghua University Ministry of Education Key Laboratory of Protein Science Tsinghua-Peking Joint Center for Life Sciences Center for Structural Biology School of Life Sciences Tsinghua University

As single particle cryo-electron microscopy has evolved to a new era of atomic resolution, sample heterogeneity still imposes a major limit to the resolution of many macromolecular complexes, especially those with continuous conformational flexibility. Here, we describe a particle segmentation algorithm towards solving structures of molecules composed of several parts that are relatively flexible with each other. In this algorithm, the different parts of a target molecule are segmented from raw images according to their alignment information obtained from a preliminary 3 D reconstruction and are subjected to single particle processing in an iterative manner. This algorithm was tested on both simulated and experimental data and showed improvement of 3 D reconstruction resolution of each segmented part of the molecule than that of the entire molecule.

关键词： Cryo-EM local reconstruction Particle segmentation Single particle reconstruction

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