  Cryo-electron microscopy (cryo-EM) has become a tool of fundamental
importance in structural biology, helping us understand the basic building
blocks of life. The algorithmic challenge of cryo-EM is to jointly estimate the
unknown 3D poses and the 3D electron scattering potential of a biomolecule from
millions of extremely noisy 2D images. Existing reconstruction algorithms,
however, cannot easily keep pace with the rapidly growing size of cryo-EM
datasets due to their high computational and memory cost. We introduce cryoAI,
an ab initio reconstruction algorithm for homogeneous conformations that uses
direct gradient-based optimization of particle poses and the electron
scattering potential from single-particle cryo-EM data. CryoAI combines a
learned encoder that predicts the poses of each particle image with a
physics-based decoder to aggregate each particle image into an implicit
representation of the scattering potential volume. This volume is stored in the
Fourier domain for computational efficiency and leverages a modern coordinate
network architecture for memory efficiency. Combined with a symmetrized loss
function, this framework achieves results of a quality on par with
state-of-the-art cryo-EM solvers for both simulated and experimental data, one
order of magnitude faster for large datasets and with significantly lower
memory requirements than existing methods.
