Analysis and correction of errors in nanoscale particle tracking using the Single-pixel interior filling function (SPIFF) algorithm

TL;DR

This paper identifies a common pixel locking bias in nanoscale particle tracking and introduces the SPIFF algorithm to correct these errors, significantly improving measurement accuracy in various experimental conditions.

Contribution

The paper presents the SPIFF algorithm, a novel correction method for systematic pixel locking errors in nanoscale particle tracking.

Findings

SPIFF reduces errors in particle position and interparticle measurements.

Application of SPIFF improves accuracy in experimental and simulated data.

Pixel locking errors are widespread and correctable with SPIFF.

Abstract

Particle tracking, which is an essential tool in many fields of scientific research, uses algorithms that retrieve the centroid of tracked particles with sub-pixel accuracy. However, images in which the particles occupy a small number of pixels on the detector, are in close proximity to other particles or suffer from background noise, show a systematic error in which the particle sub-pixel positions are biased towards the center of the pixel. This pixel locking effect greatly reduces particle tracking accuracy. In this report, we demonstrate the severity of these errors by tracking experimental (and simulated) imaging data of optically trapped silver nanoparticles and single fluorescent proteins. We show that errors in interparticle separation, angle and mean square displacement are significantly reduced by applying the corrective Single- pixel interior filling function (SPIFF) algorithm. Our work demonstrates the potential ubiquity of such errors and the general applicability of SPIFF correction to many experimental fields.