About Open Cryo-ET

The physics of imaging, and the statistical machine learning behind reconstruction — in one place, made clear.
This is not another tool's documentation. It is an encyclopedia of why.

Cryo-ET lets us see inside a cell close to its native state — but its 3-D reconstructions are hard to read: a ±60° tilt leaves a missing wedge that stretches structure along one axis, and the low dose that protects the sample buries the signal in noise. The missing wedge is not noise; it is a whole block of information that was never measured. Filling it back in takes both the physics of imaging and probabilistic, generative modelling — and this site develops both sides at once.

How to read it

We want two kinds of reader to get through it: the one who knows only biology, and the one who knows only machine learning. Every concept opens with an Intuition that builds the picture, then a Depth section with the derivation; many pages carry a demo computed live in your browser. You need not absorb all the math at once — follow the nine knowledge bases down, and step back to the prerequisites whenever a page loses you. The further you read, the clearer the missing wedge becomes.

Our work

The site also develops our own family of self-supervised Cryo-ET reconstruction methods — no ground-truth labels, missing-wedge restoration written as a Bayesian inverse problem. CryoGEN-I is a MAP point estimate under an energy prior[1]; CryoGEN-II turns to optimal transport for global distribution matching[2]; CryoWGEN adds entropic regularization for an uncertainty-capturing Boltzmann posterior. They focus on reconstruction itself: turning that missing block back into a more isotropic, more complete volume.

How to cite

If this site or these methods help your work, please cite:

1. Yunfei Teng, Yuxuan Ren, Kai Chen, Xi Chen, Zhaoming Chen, Qiwei Ye. CryoGEN: Generative Energy-based Models for Cryogenic Electron Tomography Reconstruction. ICLR, 2025. openreview.net/forum?id=uOb7rij7sR
2. Yunfei Teng et al. CryoGEN-II: Cryogenic Electron Tomography Reconstruction via Generative Network. CVPR 2026 Workshops (VISION). openaccess.thecvf.com

@inproceedings{teng2025cryogen,
  title     = {CryoGEN: Generative Energy-based Models for
               Cryogenic Electron Tomography Reconstruction},
  author    = {Teng, Yunfei and Ren, Yuxuan and Chen, Kai and
               Chen, Xi and Chen, Zhaoming and Ye, Qiwei},
  booktitle = {International Conference on Learning Representations (ICLR)},
  year      = {2025},
  url       = {https://openreview.net/forum?id=uOb7rij7sR}
}

@inproceedings{teng2026cryogen2,
  title     = {CryoGEN-II: Cryogenic Electron Tomography
               Reconstruction via Generative Network},
  author    = {Teng, Yunfei and others},
  booktitle = {CVPR 2026 Workshops (VISION)},
  year      = {2026},
  url       = {https://openaccess.thecvf.com/content/CVPR2026W/VISION26/papers/Teng_CryoGEN-II_Cryogenic_Electron_Tomography_Reconstruction_via_Generative_Network_CVPRW_2026_paper.pdf}
}

Contact

Maintained by the Open-Cryo Team, an open-source academic group. Fully bilingual — switch in the top right. Currently in trial run, still being polished. Corrections, additions, and collaborations welcome: yt1208@nyu.edu.