A Monte Carlo framework for missing wedge restoration and noise removal in cryo-electron tomography

IF 3.5 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Emmanuel Moebel, Charles Kervrann
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引用次数: 14

Abstract

We propose a statistical method to address an important issue in cryo-electron tomography image analysis: reduction of a high amount of noise and artifacts due to the presence of a missing wedge (MW) in the spectral domain. The method takes as an input a 3D tomogram derived from limited-angle tomography, and gives as an output a 3D denoised and artifact compensated volume. The artifact compensation is achieved by filling up the MW with meaningful information. To address this inverse problem, we compute a Minimum Mean Square Error (MMSE) estimator of the uncorrupted image. The underlying high-dimensional integral is computed by applying a dedicated Markov Chain Monte-Carlo (MCMC) sampling procedure based on the Metropolis-Hasting (MH) algorithm. The proposed MWR (Missing Wedge Restoration) algorithm can be used to enhance visualization or as a pre-processing step for image analysis, including segmentation and classification of macromolecules. Results are presented for both synthetic data and real 3D cryo-electron images.

Abstract Image

低温电子断层扫描中缺失楔形恢复和噪声去除的蒙特卡罗框架
我们提出了一种统计方法来解决低温电子断层成像分析中的一个重要问题:减少由于谱域中缺失楔形(MW)的存在而产生的大量噪声和伪影。该方法以有限角度层析成像得到的三维层析成像作为输入,并给出三维去噪和伪影补偿的体作为输出。工件补偿是通过用有意义的信息填充MW来实现的。为了解决这个逆问题,我们计算了未损坏图像的最小均方误差(MMSE)估计量。利用基于Metropolis-Hasting (MH)算法的专用马尔可夫链蒙特卡罗(MCMC)采样程序计算底层高维积分。所提出的MWR (Missing Wedge Restoration)算法可用于增强可视化或作为图像分析的预处理步骤,包括大分子的分割和分类。给出了合成数据和真实三维低温电子图像的结果。
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来源期刊
Journal of Structural Biology: X
Journal of Structural Biology: X Biochemistry, Genetics and Molecular Biology-Structural Biology
CiteScore
6.50
自引率
0.00%
发文量
20
审稿时长
62 days
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