Fast and robust single particle reconstruction in 3D fluorescence microscopy

TL;DR

This paper introduces a new 3D fluorescence microscopy reconstruction method that jointly estimates particle poses and improves resolution, overcoming limitations of previous techniques with enhanced robustness and efficiency.

Contribution

It presents a novel multilevel optimization approach for joint pose estimation and reconstruction in 3D fluorescence microscopy, reducing bias and computational cost.

Findings

Outperforms standard methods in resolution and error on synthetic data

Achieves low computational cost compared to existing approaches

Successfully reconstructs real centriole datasets

Abstract

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 multiple views with unknown poses. Current methods are limited either by template bias, restriction to 2D data, high computational cost or a 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 overcome 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.