Super-sampling SART with ordered subsets

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.