Fast and Robust Single Particle Reconstruction in 3D Fluorescence Microscopy

Single particle reconstruction has recently emerged in 3D fluorescence microscopy as a powerful technique to improve the axial resolution and the degree of fluorescent labeling. It is based on the reconstruction of an average volume of a biological particle from the acquisition of multiple views with unknown poses. Current methods are limited either by template bias, restriction to 2D data, high computational cost or lack of robustness to low fluorescent labeling. In this work, we propose a single particle reconstruction method dedicated to convolutional models in 3D fluorescence microscopy that overcomes these issues. We address the joint reconstruction and estimation of the poses of the particles, which translates into a challenging non-convex optimization problem. Our approach is based on a multilevel reformulation of this problem, and the development of efficient optimization techniques at each level. We demonstrate on synthetic data that our method outperforms the standard approaches in terms of resolution and reconstruction error, while achieving a low computational cost. We also perform successful reconstruction on real datasets of centrioles to show the potential of our method in concrete applications.

Automated summary

Single particle reconstruction is a powerful technique in 3D fluorescence microscopy, which improves the axial resolution and fluorescence labeling degree by reconstructing the average volume of a biological particle from multiple views with unknown poses. This paper proposes a dedicated single particle reconstruction method for convolutional models, overcoming issues such as template bias, restriction to 2D data, high computational cost, and lack of robustness to low fluorescent labeling. The method achieves better resolution and reconstruction error with low computational cost, as demonstrated on synthetic data and real datasets of centrioles.