Space-Time Implicit Neural Representations for Atomic Electron Tomography on Dynamic Samples

Solving for the 3D atomic structure of unknown materials is a key problem in materials science. Atomic electron tomography (AET) is a technique capable of reconstructing the 3D position and chemical species of all atoms in a nanoscale sample from a series of 2D projections from different angles. One challenge in AET is carbon contamination that accumulates on the sample while collecting the tomographic projections, creating an unwanted temporal dynamic that degrades reconstruction quality when existing tomography algorithms expect a static sample. In this work, we use an unsupervised implicit neural representation (INR) as a space-time model to computationally remove the contamination and recover a clean 3D reconstruction, and show promising preliminary results on simulated data.