Learning an optimal PSF-pair for ultra-dense 3D localization microscopy

TL;DR

This paper introduces a method for dense 3D particle localization in microscopy by engineering and using multiple PSFs simultaneously, improving accuracy in high-density conditions through innovative design and learning techniques.

Contribution

It proposes a novel approach to engineer multiple PSFs for dense 3D localization, utilizing a bifurcated optical system and end-to-end learning for optimized PSF design.

Findings

Successfully implemented dual-PSF system for 3D localization

Enhanced localization accuracy at high particle densities

Validated approach with cellular telomere imaging

Abstract

A long-standing challenge in multiple-particle-tracking is the accurate and precise 3D localization of individual particles at close proximity. One established approach for snapshot 3D imaging is point-spread-function (PSF) engineering, in which the PSF is modified to encode the axial information. However, engineered PSFs are challenging to localize at high densities due to lateral PSF overlaps. Here we suggest using multiple PSFs simultaneously to help overcome this challenge, and investigate the problem of engineering multiple PSFs for dense 3D localization. We implement our approach using a bifurcated optical system that modifies two separate PSFs, and design the PSFs using three different approaches including end-to-end learning. We demonstrate our approach experimentally by volumetric imaging of fluorescently labelled telomeres in cells.