Distributed Reconstruction Algorithm for Electron Tomography with Multiple-scattering Samples

TL;DR

This paper introduces a distributed computing method for high-resolution 3D electron tomography of thick, multiple-scattering samples, enabling larger reconstructions by parallel processing of tilt-series data.

Contribution

It presents a novel distributed reconstruction algorithm that decomposes tilt-series data for parallel processing, improving the ability to reconstruct large, complex samples at high resolution.

Findings

Successful reconstruction of layered clay sample at high resolution

Demonstrated scalability with large field-of-view datasets

Enhanced capability for thick, multiple-scattering samples

Abstract

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.