Spectral Signal-to-Noise Ratio and Resolution Assessment of 3D Reconstructions

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

Optimization of basis functions for both reconstruction and visualization

AbstractAlgebraic reconstruction techniques for the reconstruction of distributions from projections have yielded improvements in diverse fields such as medical imaging and electron microscopy. An important property of these methods is that they allow the use of various basis functions. Recently spherically symmetric functions (blobs) have been introduced as efficacious basis functions for reconstruction. However, basis functions whose parameters were found to be appropriate for use in reconstruction are not necessarily good for visualization. We propose a method of selecting blob parameters for both reconstruction and visualization