Amortized Pose Estimation for X-Ray Single Particle Imaging

X-ray single particle imaging (SPI) is a nascent technique that can capture the dynamics of biomolecules at room temperature. SPI experiments will one day collect tens of millions of images of the same molecule in order to overcome the weak scattering of individual proteins. Existing reconstruction algorithms will be unable to scale to datasets of this size because they perform computationally expensive search steps to estimate the orientation of the molecule in each image. In this work, we propose a reconstruction algorithm that amortizes the estimation of pose via an autoencoder framework. Our approach consists of a convolutional encoder that maps X-ray images to predicted poses and a physics-based decoder that implicitly fuses all the 2D scattering images into a volumetric representation of the molecule. We validate our method on 6 synthetic datasets of 2 distinct proteins, showing that for the largest datasets containing 5 million images, our technique can reconstruct the electron density in a single pass.