Unser M, Sorzano C O S, Thévenaz P, Jonić S, El-Bez C, De Carlo S, Conway J F, Trus B L
Biomedical Imaging Group, Swiss Federal Institute of Technology Lausanne, CH-1015 Lausanne VD, Switzerland.
J Struct Biol. 2005 Mar;149(3):243-55. doi: 10.1016/j.jsb.2004.10.011.
Measuring the quality of three-dimensional (3D) reconstructed biological macromolecules by transmission electron microscopy is still an open problem. In this article, we extend the applicability of the spectral signal-to-noise ratio (SSNR) to the evaluation of 3D volumes reconstructed with any reconstruction algorithm. The basis of the method is to measure the consistency between the data and a corresponding set of reprojections computed for the reconstructed 3D map. The idiosyncrasies of the reconstruction algorithm are taken explicitly into account by performing a noise-only reconstruction. This results in the definition of a 3D SSNR which provides an objective indicator of the quality of the 3D reconstruction. Furthermore, the information to build the SSNR can be used to produce a volumetric SSNR (VSSNR). Our method overcomes the need to divide the data set in two. It also provides a direct measure of the performance of the reconstruction algorithm itself; this latter information is typically not available with the standard resolution methods which are primarily focused on reproducibility alone.
通过透射电子显微镜测量三维(3D)重建生物大分子的质量仍然是一个悬而未决的问题。在本文中,我们将光谱信噪比(SSNR)的适用性扩展到对使用任何重建算法重建的3D体积的评估。该方法的基础是测量数据与为重建的3D地图计算的相应一组重投影之间的一致性。通过执行仅噪声重建,明确考虑了重建算法的特性。这导致了3D SSNR的定义,它提供了3D重建质量的客观指标。此外,用于构建SSNR的信息可用于生成体积SSNR(VSSNR)。我们的方法克服了将数据集一分为二的需要。它还提供了重建算法本身性能的直接度量;后一种信息通常无法通过主要仅关注再现性的标准分辨率方法获得。