Three-dimensional electron tomography is used to understand the structure and properties of samples in chemistry, materials science, geoscience, and biology. With the recent development of high-resolution detectors and algorithms that can account for multiple-scattering events, thicker samples can be examined at finer resolution, resulting in larger reconstruction volumes than previously possible. In this work, we propose a distributed computing framework that reconstructs large volumes by decomposing a projected tilt-series into smaller datasets such that sub-volumes can be simultaneously reconstructed on separate compute nodes using a cluster. We demonstrate our method by reconstructing a multiple-scattering layered clay (montmorillonite) sample at high resolution from a large field-of-view tilt-series phase contrast transmission electron microscopty dataset.
@article{arxiv.2110.07857,
title = {Distributed Reconstruction Algorithm for Electron Tomography with Multiple-scattering Samples},
author = {David Ren and Michael Whittaker and Colin Ophus and Laura Waller},
journal= {arXiv preprint arXiv:2110.07857},
year = {2021}
}