  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.
