Classification and assessment of retrieved electron density maps in coherent X-ray diffraction imaging using multivariate statistics
Classification and assessment of retrieved electron density maps in coherent X-ray diffraction imaging using multivariate statistics
复制标题
使用多元统计对相干 X 射线衍射成像中检索的电子密度图进行分类和评估
DOI：
10.1107/s1600577515018202
复制
发表时间：
2016
影响因子：
2.5
通讯作者：
T. Oroguchi and M. Nakasako
中科院分区：
文献类型：
--
作者：
Y. Sekiguchi;T. Oroguchi and M. Nakasako
相干X射线衍射成像（CXDI）是用于可视化材料和生物科学中微米至亚微米尺寸的非晶体颗粒结构的技术之一。在CXDI的结构分析中，理论上可以通过使用相位恢复（PR）算法从衍射图案重建样品颗粒的电子密度图。然而，在实践中，重建是困难的，因为衍射图案的泊松噪声和丢失的数据在小角度区域由于光束停止和探测器像素的饱和的影响。与X射线蛋白质晶体学不同，在X射线蛋白质晶体学中，衍射波的相位是通过实验估计的，CXDI中的相位恢复完全依赖于PR算法驱动的计算过程。因此，客观的标准和方法，以评估检索电子密度图的准确性是必要的，除了常规参数监测PR计算的收敛性。在这里，一个数据分析方案，命名为ASURA，提出了从一组从1000个不同的随机种子的衍射图案的地图中选择最可能的电子密度图。由J个像素组成的每个电子密度图被表示为J维空间中的点。应用主成分分析来描述地图在J维空间中的分布特征。当分布的特征在于少量的主成分时，使用k-均值聚类方法对分布进行分类。分类后的地图通过几个参数进行评价，以评估地图的质量。使用所提出的方案，从非晶颗粒的衍射图案的结构分析进行了两个阶段：估计的整体形状和确定的精细结构内的支持形状。在每个阶段中，客观地选择最准确和最可能的密度图。所提出的方案的有效性进行检查，从聚集体的金属颗粒和生物样品在XFEL设施SACLA使用定制的衍射装置获得的衍射数据的应用。
Coherent X-ray diffraction imaging (CXDI) is one of the techniques used to visualize structures of non-crystalline particles of micrometer to submicrometer size from materials and biological science. In the structural analysis of CXDI, the electron density map of a sample particle can theoretically be reconstructed from a diffraction pattern by using phase-retrieval (PR) algorithms. However, in practice, the reconstruction is difficult because diffraction patterns are affected by Poisson noise and miss data in small-angle regions due to the beam stop and the saturation of detector pixels. In contrast to X-ray protein crystallography, in which the phases of diffracted waves are experimentally estimated, phase retrieval in CXDI relies entirely on the computational procedure driven by the PR algorithms. Thus, objective criteria and methods to assess the accuracy of retrieved electron density maps are necessary in addition to conventional parameters monitoring the convergence of PR calculations. Here, a data analysis scheme, named ASURA, is proposed which selects the most probable electron density maps from a set of maps retrieved from 1000 different random seeds for a diffraction pattern. Each electron density map composed of J pixels is expressed as a point in a J-dimensional space. Principal component analysis is applied to describe characteristics in the distribution of the maps in the J-dimensional space. When the distribution is characterized by a small number of principal components, the distribution is classified using the k-means clustering method. The classified maps are evaluated by several parameters to assess the quality of the maps. Using the proposed scheme, structure analysis of a diffraction pattern from a non-crystalline particle is conducted in two stages: estimation of the overall shape and determination of the fine structure inside the support shape. In each stage, the most accurate and probable density maps are objectively selected. The validity of the proposed scheme is examined by application to diffraction data that were obtained from an aggregate of metal particles and a biological specimen at the XFEL facility SACLA using custom-made diffraction apparatus.