Cryogenic electron tomography (cryo-ET) is an imaging technique that visualizes biological molecules and intracellular structures in their native 3D environments at nanometer resolution. In cryo-ET, a three-dimensional sample is observed by tilting it around a fixed axis while capturing two-dimensional projections at known viewing directions using the transmission electron microscope (TEM).
Projects
End-to-end localized deep learning for Cryo-ET: Cryo-electron tomography (cryo-ET) enables 3D visualization of cellular environments. Accurate reconstruction of high-resolution volumes is complicated by the very low signal-to-noise ratio and a restricted range of sample tilts, creating a missing wedge of Fourier information. Recent selfsupervised deep learning approaches, which post-process initial reconstructions done by filtered backprojection (FBP), have significantly improved reconstruction quality, but they are computationally expensive, demand large memory, and require retraining for each new dataset. End-to-end supervised learning is an appealing alternative but is impeded by the lack of ground truth and the large memory demands of high-resolution volumetric data. Training on synthetic data often leads to overfitting and poor generalization to real data, and, to date, no general end-to-end deep learning reconstructors exist for cryo-ET. In this work, we introduce CryoLithe, a local, memory-efficient reconstruction network that directly estimates the volume from an aligned tilt-series, overcoming the suboptimal FBP. We demonstrate that leveraging transform-domain locality makes our network robust to distribution shifts, enabling effective supervised training and giving excellent results on real data—without retraining or fine-tuning
Overview of the Patch Extraction Process:
The network architecture:
ICE-TIDE: Implicit Cryo-ET Imaging and Deformation Estimation: 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.
Publications
2025
@article{kishore2025end,
title={End-to-end localized deep learning for Cryo-ET},
author={Kishore, Vinith and Debarnot, Valentin and Righetto, Ricardo D and Khorashadizadeh, AmirEhsan and Dokmani{\'c}, Ivan},
journal={arXiv preprint arXiv:2501.15246},
year={2025}
}