We introduce ICE-TIDE, a method for cryogenic electron tomography (cryo-ET) that simultaneously aligns observations and reconstructs a high-resolution volume. The alignment of tilt series in cryo-ET is a major problem limiting the resolution of reconstructions. ICE-TIDE relies on an efficient coordinate-based implicit neural representation of the volume which enables it to directly parameterize deformations and align the projections. Furthermore, the implicit network acts as an effective regularizer, allowing for high-quality reconstruction at low signal-to-noise ratios as well as partially restoring the missing wedge information. We compare the performance of ICE-TIDE to existing approaches on realistic simulated volumes where the significant gains in resolution and accuracy of recovering deformations can be precisely evaluated. Finally, we demonstrate ICE-TIDE's ability to perform on experimental data sets.
@article{arxiv.2403.02182,
title = {Ice-Tide: Implicit Cryo-ET Imaging and Deformation Estimation},
author = {Valentin Debarnot and Vinith Kishore and Ricardo D. Righetto and Ivan Dokmanić},
journal= {arXiv preprint arXiv:2403.02182},
year = {2024}
}