Method of moments for 3-D single particle ab initio modeling with non-uniform distribution of viewing angles
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Abstract (arXiv)
Single-particle reconstruction in cryo-electron microscopy (cryo-EM) is an increasingly popular technique for determining the 3-D structure of a molecule from several noisy 2-D projections images taken at unknown viewing angles. Most reconstruction algorithms require a low-resolution initialization for the 3-D structure, which is the goal of ab initio modeling. Suggested by Zvi Kam in 1980, the method of moments (MoM) offers one approach, wherein low-order statistics of the 2-D images are computed and a 3-D structure is estimated by solving a system of polynomial equations. Unfortunately, Kam's method suffers from restrictive assumptions, most notably that viewing angles should be distributed uniformly. Often unrealistic, uniformity entails the computation of higher-order correlations, as in this case first and second moments fail to determine the 3-D structure. In the present paper, we remove this hypothesis, by permitting an unknown, non-uniform distribution of viewing angles in MoM. Perhaps surprisingly, we show that this case is statistically easier than the uniform case, as now first and second moments generically suffice to determine low-resolution expansions of the molecule. In the idealized setting of a known, non-uniform distribution, we find an efficient provable algorithm inverting first and second moments. For unknown, non-uniform distributions, we use non-convex optimization methods to solve for both the molecule and distribution.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Jul 2019.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 2; 2024: 0; 2025: 1.
8 papers.
- 2022Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopycited by 19
- 2022Tensor Moments of Gaussian Mixture Models: Theory and Applicationscited by 7
- 2021Learning to recover orientations from projections in single-particle cryo-EMcited by 2
- 2022Orthogonal Matrix Retrieval with Spatial Consensus for 3D Unknown-View Tomographycited by 2
- 2021Multi-target detection with rotationscited by 1
- 2023The G-invariant graph Laplaciancited by 0
- 2023Deep Neural-network Prior for Orbit Recovery from Method of Momentscited by 0
- 2025Two Datasets Are Better Than One: Method of Double Moments for 3-D Reconstruction in Cryo-EMcited by 0
- 2025Two Datasets Are Better Than One: Method of Double Moments for 3-D Reconstruction in Cryo-EM2 Nov 2025
- 2023Deep Neural-network Prior for Orbit Recovery from Method of Moments28 Apr 2023
- 2023The G-invariant graph Laplacian29 Mar 2023
- 2022Orthogonal Matrix Retrieval with Spatial Consensus for 3D Unknown-View Tomography6 Jul 2022
- 2022Tensor Moments of Gaussian Mixture Models: Theory and Applications14 Feb 2022
- 2022Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopy8 Jan 2022
- 2021Learning to recover orientations from projections in single-particle cryo-EM13 Apr 2021
- 2021Multi-target detection with rotations19 Jan 2021