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Single-tilt scheme is nowadays the prevalent acquisition geometry in electron tomography and subtomogram averaging experiments. Being an incomplete scheme that induces ill-posedness in the sense of the X-ray or Radon transform inverse problem, it introduces a number of artifacts that directly influence the quality of tomographic reconstructions. Though individually described by different authors before, a systematic study of these acquisition geometry-related artifacts in one place and across representative set of reconstruction methods has not been, to our knowledge, performed before. Moreover, the effects of these artifacts on the reconstructed density are sometimes misinterpreted, attributing them to the wrong cause, especially if their effects accumulate. In this work, we systematically study the major artifacts of single-tilt geometry known as the missing wedge (incomplete projection set problem), the missing information and the specimen-level interior problem (long-object problem). First, we illustratively describe, using a unified terminology, how and why these artifacts arise and when they can be avoided. Next, we describe the effects of these artifacts on the reconstructions across all major classes of reconstruction methods, including newly-appeared methods like the Iterative Nonuniform fast Fourier transform based Reconstruction method (INFR) and the Progressive Stochastic Reconstruction Technique (PSRT). Finally, we draw conclusions and recommendations on numerous points, especially regarding the mutual influence of the geometric artifacts, ability of different reconstruction methods to suppress them as well as implications to the interpretation of both electron tomography and subtomogram averaging experiments.

We present a novel software package for the problem "reconstruction from projections" in electron microscopy. The Ettention framework consists of a set of modular building-blocks for tomographic reconstruction algorithms. The well-known block iterative reconstruction method based on Kaczmarz algorithm is implemented using these building-blocks, including adaptations specific to electron tomography. Ettention simultaneously features (1) a modular, object-oriented software design, (2) optimized access to high-performance computing (HPC) platforms such as graphic processing units (GPU) or many-core architectures like Xeon Phi, and (3) accessibility to microscopy end-users via integration in the IMOD package and eTomo user interface. We also provide developers with a clean and well-structured application programming interface (API) that allows for extending the software easily and thus makes it an ideal platform for algorithmic research while hiding most of the technical details of high-performance computing.

Cryo Electron Tomography (cryoET) plays an essential role in Structural Biology, as it is the only technique that allows to study the structure of large macromolecular complexes in their close to native environment in situ . The reconstruction methods currently in use, such as Weighted Back Projection (WBP) or Simultaneous Iterative Reconstruction Technique (SIRT), deliver noisy and low-contrast reconstructions, which complicates the application of high-resolution protocols, such as Subtomogram Averaging (SA). We propose a Progressive Stochastic Reconstruction Technique (PSRT) – a novel iterative approach to tomographic reconstruction in cryoET based on Monte Carlo random walks guided by Metropolis–Hastings sampling strategy. We design a progressive reconstruction scheme to suit the conditions present in cryoET and apply it successfully to reconstructions of macromolecular complexes from both synthetic and experimental datasets. We show how to integrate PSRT into SA, where it provides an elegant solution to the region-of-interest problem and delivers high-contrast reconstructions that significantly improve template-based localization without any loss of high-resolution structural information. Furthermore, the locality of SA is exploited to design an importance sampling scheme which significantly speeds up the otherwise slow Monte Carlo approach. Finally, we design a new memory efficient solution for the specimen-level interior problem of cryoET, removing all associated artifacts.