Single particle cryo-electron microscopy has become a critical tool in structural biology over the last decade, able to achieve atomic scale resolution in three dimensional models from hundreds of thousands of (noisy) two-dimensional projection views of particles frozen at unknown orientations. This is accomplished by using a suite of software tools to (i) identify particles in large micrographs, (ii) obtain low-resolution reconstructions, (iii) refine those low-resolution structures, and (iv) finally match the obtained electron scattering density to the constituent atoms that make up the macromolecule or macromolecular complex of interest. Here, we focus on the second stage of the reconstruction pipeline: obtaining a low resolution model from picked particle images. Our goal is to create an algorithm that is capable of ab initio reconstruction from small data sets (on the order of a few thousand selected particles). More precisely, we seek an algorithm that is robust, automatic, able to assess particle quality, and fast enough that it can potentially be used to assist in the assessment of the data being generated while the microscopy experiment is still underway.

翻译：过去十年来,单一粒子冷冻器显微镜已成为结构生物学中的一个关键工具,能够从数十万个(噪音)的二维投影视图中以三维模型实现原子规模解析,这些模型来自数十万个(噪音)的粒子在未知方向上被冻结的二维粒子。这是通过使用一套软件工具实现的:(一) 在大型显微镜中识别粒子,(二) 获得低分辨率重建,(三) 改进这些低分辨率结构,(四) 最后将获得的电子散射密度与构成大型分子或宏观分子复合体的原子成份相匹配。在这里,我们侧重于重建管道的第二阶段:从所选粒子图像中获取低分辨率模型。我们的目标是建立一个能够从小数据集中(按几千个选定粒子的顺序)进行初始重建的算法。更确切地说,我们寻求一种强大、自动、能够评估粒子质量的算法,而且速度足以用来帮助评估微镜实验仍在进行中生成的数据的评估。