Bayesian MINFLUX localization microscopy

TL;DR

This paper introduces a Bayesian approach to MINFLUX microscopy that optimizes fluorophore localization, significantly reducing photon requirements for high-resolution imaging.

Contribution

A rigorous Bayesian method is proposed to enhance MINFLUX microscopy, improving resolution efficiency over heuristic scanning patterns.

Findings

Estimated reduction of photon usage by a factor of four for 1 nm resolution.

Bayesian approach achieves maximal resolution with fewer detected photons or exposures.

Simulated runs validate the efficiency of the proposed method.

Abstract

MINFLUX microscopy allows for localization of fluorophores with nanometer precision using targeted scanning with an illumination profile with a minimum. However, current scanning patterns and the overall procedure are based on heuristics, and may therefore be suboptimal. Here we present a rigorous Bayesian that offers maximal resolutions from either minimal detected photons or minimal exposures. We estimate using simulated localization runs that this approach should reduce the number of photons required for 1 nm resolution by a factor of about four.