Abstract
Cryo-electron microscopy (Cryo-EM) is a popular imaging modality used to visualize a wide range of bio-molecules in their 3D form. The goal in Cryo-EM is to reconstruct the 3D density map of a molecule from projection images taken from random and unknown orientations. A critical step in the Cryo-EM pipeline is 3D refinement. In this procedure, an initial 3D map and a set of estimated projection orientations is refined to obtain higher resolution maps. State-of-the-art refinement techniques rely on projection matching steps in order to refine the initial projection orientations. Unfortunately projection matching is computationally inefficient and it requires a finite discretization of the space of orientations. To avoid repeated projection matching steps, in this work we consider the orientation variables in their continuous form. This enables us to formulate the refinement problem as a joint optimization problem that refines the underlying density map and orientations. We use alternating direction method of multipliers (ADMM) and gradient descent steps in order to update the density map and the orientations, respectively. Our results and their comparison with several baselines demonstrate the feasibility and performance of the proposed refinement framework.
Original language | English (US) |
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Article number | COIMG-133 |
Journal | IS and T International Symposium on Electronic Imaging Science and Technology |
Volume | 2019 |
Issue number | 13 |
DOIs | |
State | Published - Jan 13 2019 |
Event | 17th Computational Imaging Conference, CI 2019 - Burlingame, United States Duration: Jan 13 2019 → Jan 17 2019 |
Keywords
- 3D refinement
- continuous latent variables
- cryo-electron microscopy (Cryo-EM)
- density map reconstruction
- joint optimization
ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Science Applications
- Human-Computer Interaction
- Software
- Electrical and Electronic Engineering
- Atomic and Molecular Physics, and Optics