# Ice-Tide: Implicit Cryo-ET Imaging and Deformation Estimation

arXiv: https://arxiv.org/abs/2403.02182 · HTML: https://mlsanity.com/paper/2403.02182/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2024.

## Built on by (most cited first, 1 of 1)

- [Rates and architectures for learning geometrically non-trivial operators](https://mlsanity.com/paper/2512.09376/) (2025)

## Abstract (arXiv)

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.