# Joint Angular Refinement and Reconstruction for Single-Particle Cryo-EM

arXiv: https://arxiv.org/abs/2003.10062 · HTML: https://mlsanity.com/paper/2003.10062/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2020.

## Built on by (most cited first, 5 of 5)

- [CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images](https://mlsanity.com/paper/2203.08138/) (2022)
- [End-to-End Simultaneous Learning of Single-particle Orientation and 3D Map Reconstruction from Cryo-electron Microscopy Data](https://mlsanity.com/paper/2107.02958/) (2021)
- [Learning to recover orientations from projections in single-particle cryo-EM](https://mlsanity.com/paper/2104.06237/) (2021)
- [Mechanical Artifacts in Optical Projection Tomography: Classification and Automatic Calibration](https://mlsanity.com/paper/2309.16677/) (2023)
- [SAMCIRT: A Simultaneous Reconstruction and Affine Motion Compensation Technique for Four Dimensional Computed Tomography (4DCT)](https://mlsanity.com/paper/2402.04480/) (2024)

## Abstract (arXiv)

Single-particle cryo-electron microscopy (cryo-EM) reconstructs the three-dimensional (3D) structure of bio-molecules from a large set of 2D projection images with random and unknown orientations. A crucial step in the single-particle cryo-EM pipeline is 3D refinement, which resolves a high-resolution 3D structure from an initial approximate volume by refining the estimation of the orientation of each projection. In this work, we propose a new approach that refines the projection angles on the continuum. We formulate the optimization problem over the density map and the orientations jointly. The density map is updated using the efficient alternating-direction method of multipliers, while the orientations are updated through a semi-coordinate-wise gradient descent for which we provide an explicit derivation of the gradient. Our method eliminates the requirement for a fine discretization of the orientation space and does away with the classical but computationally expensive template-matching step. Numerical results demonstrate the feasibility and performance of our approach compared to several baselines.