Two Datasets Are Better Than One: Method of Double Moments for 3-D Reconstruction in Cryo-EM

TL;DR

This paper presents the method of double moments (MoDM), a new data fusion approach that improves 3-D molecular structure reconstruction in cryo-EM by combining two datasets with different orientation distributions, using second-order moments.

Contribution

The paper introduces MoDM, a novel framework that leverages two datasets with distinct orientation distributions to enhance cryo-EM 3-D reconstruction accuracy.

Findings

Proves unique determination of structures from double moments.

Develops a convex-relaxation algorithm for accurate recovery.

Shows dataset diversity improves reconstruction quality.

Abstract

Cryo-electron microscopy (cryo-EM) is a powerful imaging technique for reconstructing three-dimensional molecular structures from noisy tomographic projection images of randomly oriented particles. We introduce a new data fusion framework, termed the method of double moments (MoDM), which reconstructs molecular structures from two instances of the second-order moment of projection images obtained under distinct orientation distributions: one uniform, the other non-uniform and unknown. We prove that these moments generically uniquely determine the underlying structure, up to a global rotation and reflection, and we develop a convex-relaxation-based algorithm that achieves accurate recovery using only second-order statistics. Our results demonstrate the advantage of collecting and modeling multiple datasets under different experimental conditions, illustrating that leveraging dataset diversity can substantially enhance reconstruction quality in computational imaging tasks.