Super-sampling SART with ordered subsets

Abstract

In tomography, the quality of the reconstruction is essential because the complete cascade of the subsequent analysis is based on it. To date, weighted back-projection (WBP) has been the most commonly used technique due to its versatility and performance in sub-tomogram averaging. Here we present super-sampling SART that is based on the simultaneous algebraic reconstruction technique. While algebraic reconstruction techniques typically produce better contrast and lately showed a significant improvement in terms of processing speed, sub-tomogram averages derived from those reconstructions were inferior in resolution compared to those derived from WBP data. Super-sampling SART, however, outperforms both in term of contrast and the resolution achieved in sub-tomogram averaging several other tested methods and in particular WBP. The main feature of super-sampling SART, as the name implies, is the super-sampling option - by which parameter-based up-sampling and down-sampling are used to reduce artifacts. In particular, the aliasing that is omnipresent in the reconstruction can be practically eliminated without a significant increase in the computational time. Furthermore, super-sampling SART reaches convergence within a single iteration, making the processing time comparable to WBP, and eliminating the ambiguity of parameter-controlled convergence times. We find that grouping of projections increases the contrast, while when projections are used individually the resolution can be maximized. Using sub-tomogram averaging of ribosomes as a test case, we show that super-sampling SART achieves equal or better sub-tomogram averaging results than WBP, which is of particular importance in cryo-electron tomography.

Keywords: Algebraic reconstruction techniques; Reconstruction methods; Sub-tomogram averaging; Weighted backprojection.