Autonomous Electron Tomography Reconstruction with Machine Learning

Abstract Modern electron tomography has progressed to higher resolution at lower doses by leveraging compressed sensing (CS) methods that minimize total variation (TV). However, these sparsity-emphasized reconstruction algorithms introduce tunable parameters that greatly influence the reconstruction quality. Here, Pareto front analysis shows that high-quality tomograms are reproducibly achieved when TV minimization is heavily weighted. However, in excess, CS tomography creates overly smoothed three-dimensional (3D) reconstructions. Adding momentum to the gradient descent during reconstruction reduces the risk of over-smoothing and better ensures that CS is well behaved. For simulated data, the tedious process of tomography parameter selection is efficiently solved using Bayesian optimization with Gaussian processes. In combination, Bayesian optimization with momentum-based CS greatly reduces the required compute time—an 80% reduction was observed for the 3D reconstruction of SrTiO3 nanocubes. Automated parameter selection is necessary for large-scale tomographic simulations that enable the 3D characterization of a wider range of inorganic and biological materials.