Fast 3D Reconstruction Algorithms for Cryo-EM

描述（由申请人提供）：本研究旨在开发用于低温电子显微镜（Cryo-EM）中小颗粒高分辨率重建的快速期望最大化（EM）算法。该算法结合了显微镜的对比度传递函数（CTF），可以使用任何基函数。此外，提出了两种策略，以加快算法的1000倍。这两种策略是（1）域缩减，其中算法的E步骤被加速，以及（2）在商品图形卡上实现，其中图形处理器的大规模并行性加速整个算法。基于核糖体和IP 3R Ca+通道蛋白质结构的模拟实验结果表明，该算法的E步和M步快速、准确. 所提出的研究扩展了以前的工作，（1）进一步发展域减少策略到多分辨率策略，（2）系统地并行化的M步骤，（3）实现和验证完整的算法。此外，系统的软件设计计划在图形处理器单元上实现算法。该软件将在万维网上提供给其他研究人员。该算法将使用模拟和真实世界的Cryo-EM图像进行验证。重建精度将通过重建中的平均均方误差来衡量，分辨率将通过傅立叶壳相关来衡量。还将精度与传统算法的重建精度进行比较。该算法的速度将被测量为图像数量和信噪比的函数。

DESCRIPTION (provided by applicant): This research proposes to develop fast expectation-maximization (EM) algorithms for 3D reconstruction of high resolution reconstruction of small particles in Cryogenic Electron Microscopy (Cryo-EM). The proposed algorithm incorporates the contrast transfer function (CTF) of the microscope and can be used with any basis functions. Further, two strategies are proposed to speed up the algorithm by a factor of 1000. The two strategies are (1) domain reduction where the E step of the algorithm is sped up, and (2) implementation on commodity graphics cards, where the massive parallelism of the graphics processors speeds up the entire algorithm. Preliminary results using simulations based on ribosome and the IP3R Ca+ channel protein structure show that the E and M steps of the proposed algorithm are fast and accurate. The proposed research extends previous work by (1) further developing the domain reduction strategy into a multi-resolution strategy, (2) systematically parallelizing the M step, and (3) implementing and validating the complete algorithm. Further, systematic software design is planned for implementing the algorithm on graphics processor units. This software will be made available on the world-wide web for other researchers. The algorithm will be validated using simulations and real-world Cryo-EM images. The reconstruction accuracy will be measured by the average mean squared error in reconstruction, and the resolution will be measured by Fourier shell correlation. The accuracy will also be compared to the reconstruction accuracy of conventional algorithms. The speed of the algorithm will be measured as a function of number of images and signal to noise ratio.