End-to-End Simultaneous Learning of Single-particle Orientation and 3D Map Reconstruction from Cryo-electron Microscopy Data

TL;DR

This paper introduces an unsupervised end-to-end method that simultaneously learns particle orientations and reconstructs 3D maps from cryo-EM data, improving 3D structure determination without prior orientation information.

Contribution

It presents a novel auto-encoder based approach that jointly estimates orientations and reconstructs 3D structures directly from cryo-EM images, starting from random initialization.

Findings

Successfully reconstructs 3D maps from simulated cryo-EM data.

Handles noisy and CTF-corrupted 2D projection images.

Learns orientations without supervision.

Abstract

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.