Trade-offs between spatial and temporal resolutions in stochastic super-resolution microscopy techniques

TL;DR

This paper develops a theoretical framework for understanding the trade-offs between spatial and temporal resolutions in stochastic super-resolution microscopy, highlighting a logarithmic scaling law for image completion time.

Contribution

It introduces a novel analytical model predicting minimal imaging time and proposes a method to estimate reconstruction risk, advancing optimization in super-resolution microscopy.

Findings

Image completion time scales logarithmically with image size and resolution.

The model applies to 1D, 2D, and 3D structural imaging.

A method to estimate reconstruction risk from experimental data.

Abstract

Widefield stochastic microscopy techniques such as PALM or STORM rely on the progressive accumulation of a large number of frames, each containing a scarce number of super-resolved point images. We justify that the redundancy in the localization of detected events imposes a specific limit on the temporal resolution. Based on a theoretical model, we derive analytical predictions for the minimal time required to obtain a reliable image at a given spatial resolution, called image completion time. In contrast to standard assumptions, we find that the image completion time scales logarithmically with the ratio of the image size by the spatial resolution volume. We justify that this non-linear relation is the hallmark of a random coverage problem. We propose a method to estimate the risk that the image reconstruction is not complete, which we apply to an experimental data set. Our results provide a theoretical framework to quantify the pattern detection efficiency and to optimize the trade-off between image coverage and acquisition time, with applications to $1$, $2$ or $3$ dimension structural imaging.