End-to-End Simultaneous Learning of Single-particle Orientation and 3D Map Reconstruction from Cryo-electron Microscopy Data
This addresses the challenge of cryo-EM data analysis for structural biologists, offering an incremental improvement by integrating orientation learning and reconstruction in a single framework.
The paper tackles the problem of reconstructing 3D biomolecule maps from cryo-EM data with unknown particle orientations, presenting an end-to-end unsupervised method that learns orientations and reconstructs maps from noisy images, achieving successful reconstruction on simulated data.
Cryogenic electron microscopy (cryo-EM) provides images from different copies of the same biomolecule in arbitrary orientations. Here, we present an end-to-end unsupervised approach that learns individual particle orientations from cryo-EM data while reconstructing the average 3D map of the biomolecule, starting from a random initialization. The approach relies on an auto-encoder architecture where the latent space is explicitly interpreted as orientations used by the decoder to form an image according to the linear projection model. We evaluate our method on simulated data and show that it is able to reconstruct 3D particle maps from noisy- and CTF-corrupted 2D projection images of unknown particle orientations.