Multi-dimensional confocal laser scanning microscopy image correlation for nanoparticle flow velocimetry

Abstract

We present a new multi-dimensional confocal laser scanning microscopy (CLSM) image correlation for nanoparticle flow velocimetry that is robust to sources of decorrelating errors. Random and bias errors from nanoparticle flow measurements exacerbate with increased dimensionality in CLSM images, rendering measurements unusable. Our new algorithm tackles these measurement limitations in twofold. First, we model and correct for the bias errors introduced by the effects of the volumetric laser scanning image acquisition. Second, we developed a new spectral filter using a phase-quality masking technique that optimizes its size for the spectral content of CLSM images, without requiring a priori knowledge of displacement fields or flow tracer properties. We validated our algorithm using synthetic images and experimentally obtained 2D and 3D CLSM images of nanoparticle flow through a micro-channel. We show that our technique significantly outperforms the standard cross-correlation (SCC) in reducing both the random and bias errors and accelerated the convergence of ensemble correlation velocity measurements from CLSM images.