General Defocusing Particle Tracking: fundamentals and uncertainty assessment

General Defocusing Particle Tracking (GDPT) is a single-camera, three-dimensional particle tracking method that determines the particle depth positions from the defocusing patterns of the corresponding particle images. GDPT relies on a reference set of experimental particle images which is used to predict the depth position of measured particle images of similar shape. While several implementations of the method are possible, its accuracy is ultimately limited by some intrinsic properties of the acquired data, such as the signal-to-noise ratio, the particle concentration, as well as the characteristics of the defocusing patterns. GDPT has been applied in different fields by different research groups, however, a deeper description and analysis of the method fundamentals has hitherto not been available. In this work, we first identity the fundamental elements that characterize a GDPT measurement. Afterwards, we present a standardized framework based on synthetic images to assess the performance of GDPT implementations in terms of measurement uncertainty and relative number of measured particles. Finally, we provide guidelines to assess the uncertainty of experimental GDPT measurements, where true values are not accessible and additional image aberrations can lead to bias errors. The data were processed using DefocusTracker, an open-source GDPT software. The datasets were created using the synthetic image generator MicroSIG and have been shared in a freely-accessible repository

Diving with microparticles in acoustic fields

Sound can move particles. A good example of this phenomenon is the Chladni plate, in which an acoustic wave is induced in a metallic plate and particles migrate to the nodes of the acoustic wave. For several years, acoustophoresis has been used to manipulate microparticles in microscopic scales. In this fluid dynamics video, submitted to the 30th Annual Gallery of Fluid Motion, we show the basic mechanism of the technique and a simple way of visualize it. Since acoustophoretic phenomena is essentially a three-dimensional effect, we employ a simple technique to visualize the particles in 3D. The technique is called Astigmatism Particle Tracking Velocimetry and it consists in the use of cylindrical lenses to induce a deformation in the particle shape, which will be then correlated with its distance from the observer. With this method we are able to dive with the particles and observe in detail particle motion that would otherwise be missed. The technique not only permits visualization but also precise quantitative measurements that can be compared with theory and simulations.Comment: Fluid dynamics video for the 30th Annual Gallery of Fluid Motion, Entry #84160 65th Annual Meeting of the American Physical Society, Division of Fluid Dynamics San Diego, CA, Nov 201

Ultrasound-induced acoustophoretic motion of microparticles in three dimensions

We derive analytical expressions for the three-dimensional (3D) acoustophoretic motion of spherical microparticles in rectangular microchannels. The motion is generated by the acoustic radiation force and the acoustic streaming-induced drag force. In contrast to the classical theory of Rayleigh streaming in shallow, infinite, parallel-plate channels, our theory does include the effect of the microchannel side walls. The resulting predictions agree well with numerics and experimental measurements of the acoustophoretic motion of polystyrene spheres with nominal diameters of 0.537 um and 5.33 um. The 3D particle motion was recorded using astigmatism particle tracking velocimetry under controlled thermal and acoustic conditions in a long, straight, rectangular microchannel actuated in one of its transverse standing ultrasound-wave resonance modes with one or two half-wavelengths. The acoustic energy density is calibrated in situ based on measurements of the radiation dominated motion of large 5-um-diam particles, allowing for quantitative comparison between theoretical predictions and measurements of the streaming induced motion of small 0.5-um-diam particles.Comment: 13 pages, 8 figures, Revtex 4.

Acoustic radiation- and streaming-induced microparticle velocities determined by micro-PIV in an ultrasound symmetry plane

We present micro-PIV measurements of suspended microparticles of diameters from 0.6 um to 10 um undergoing acoustophoresis in an ultrasound symmetry plane in a microchannel. The motion of the smallest particles are dominated by the Stokes drag from the induced acoustic streaming flow, while the motion of the largest particles are dominated by the acoustic radiation force. For all particle sizes we predict theoretically how much of the particle velocity is due to radiation and streaming, respectively. These predictions include corrections for particle-wall interactions and ultrasonic thermoviscous effects, and they match our measurements within the experimental uncertainty. Finally, we predict theoretically and confirm experimentally that the ratio between the acoustic radiation- and streaming-induced particle velocities is proportional to the square of the particle size, the actuation frequency and the acoustic contrast factor, while it is inversely proportional to the kinematic viscosity.Comment: 11 pages, 9 figures, RevTex 4-

DefocusTracker:A Modular Toolbox for Defocusing-based, Single-Camera, 3D Particle Tracking

The need for single-camera 3D particle tracking methods is growing, among others, due to the increasing focus in biomedical research often relying on single-plane microscopy imaging. Defocusing-based methods are ideal for a wide-spread use as they rely on basic microscopy imaging rather than requiring additional non-standard optics. However, a wide-spread use has been limited by the lack of accessible and easy-to-use software. DefocusTracker is an open-source toolbox based on the universal principles of General Defocusing Particle Tracking (GDPT) relying solely on a reference look-up table and image recognition to connect a particle's image and its respective out-of-plane depth coordinate. The toolbox is built in a modular fashion, allowing for easy addition of new image recognition methods, while maintaining the same workflow and external user interface. DefocusTracker is implemented in MATLAB, while a parallel implementation in Python is in the preparation.Comment: 11 pages, 3 figures, submitted for Journal of Open Research Software (JORS