Accurate Particle Tracking Research Articles

A Facile Approach to Producing Liposomal J-Aggregates of Indocyanine Green with Diagnostic and Therapeutic Potential.

Liposomal J-Aggregates of Indocyanine Green (L-JA) can serve as a biocompatible and biodegradable nanoparticle for photoacoustic imaging and photothermal therapy. When compared to monomeric IcG, L-JA are characterized by longer circulation, improved photostability, elevated absorption at longer wavelengths, and increased photoacoustic signal generation. However, the documented methods for production of L-JA vary widely. We developed an approach to efficiently form IcG J-aggregates (IcG-JA) directly in liposomes at elevated temperatures. Aggregating within fully formed liposomes ensures particle uniformity and allows for control of J-aggregate size. L-JA have unique properties compared to IcG. L-JA provide significant contrast enhancement in photoacoustic images for up to 24 hours after injection, while IcG and unencapsulated IcG-JA are cleared within an hour. L-JA allow for more accurate photoacoustic-based sO2 estimation and particle tracking compared to IcG. Furthermore, photothermal heating of L-JA with an 852nm laser is demonstrated to be more effective at lower laser powers than conventional 808nm lasers for the first time. The presented technique offers an avenue for formulating a multi-faceted contrast agent for photoacoustic imaging and photothermal therapy that offers significant advantages over other conventional agents.

Deep learning-based multiple particle tracking in complex system

This paper presents an innovative approach for multiple particle tracking within complex systems, utilizing convolutional neural networks in conjunction with multi-output models. Accurate particle tracking is a critical prerequisite for unraveling the dynamic behaviors of particles in a myriad of research domains, encompassing colloidal particles, biological cells, and molecular dynamics. Different from conventional methodologies, our approach combines data preprocessing, multilayer perceptron model training, and multi-output model integration to yield precise and efficient particle tracking results. The significance of this research lies in the adaptability and versatility of the trained models, which are designed to surmount challenges, including crowded and noisy environments. This work represents a substantial step forward in particle tracking methodologies, providing a robust and efficient alternative to conventional methods, promising more profound investigations into particle dynamics within complex systems, and contributing to a deeper understanding of the microscale world.

Pattern recognition of quartz sand particles with PointConv network

Particle kinematics plays a significant role in the mechanical response of granular soils. Accurate particle identification and tracking holds the key to the investigation of particle kinematics during the soil lab test. This paper introduces a novel pattern recognition method for identifying and tracking intact Leighton Buzzard sand (LBS) particles in a miniature triaxial sample with a neural network, called the PointConv network. Firstly, the image processing techniques are applied on the 2D slices to reconstruct the realistic morphology of LBS particles. Secondly, 100 LBS particles are randomly selected as the objects of particle tracking and are handled by an operation of sampling and grouping to prepare the training and testing datasets for the PointConv network. A set of Gaussian noise is generated and injected into the particle point subsets to increase the robustness of the training model. Next, the PointConv network is implemented to learn morphological features of the LBS particles in multiple dimensions and successfully recognized and tracked all LBS particles. Finally, the discussions about the effects of various parameters of the model on the prediction results are made. The merits of the proposed method are highlighted by comprehensively comparing with several existing particle tracking methods.

Correlation Tracking: Using simulations to interpolate highly correlated particle tracks.

Despite significant advances in particle imaging technologies over the past two decades, few advances have been made in particle tracking, i.e., linking individual particle positions across time series data. The state-of-the-art tracking algorithm is highly effective for systems in which the particles behave mostly independently. However, these algorithms become inaccurate when particle motion is highly correlated, such as in dense or strongly interacting systems. Accurate particle tracking is essential in the study of the physics of dense colloids, such as the study of dislocation formation, nucleation, and shear transformations. Here, we present a method for particle tracking that incorporates information about the correlated motion of the particles. We demonstrate significant improvement over the state-of-the-art tracking algorithm in simulated data on highly correlated systems.

An experimental framework to capture the flow dynamics of droplets expelled by a sneeze.

Respiratory activities such as sneezing generate pathogen laden droplets that can deposit in the respiratory tract of a susceptible host to initiate infection. The extent of spread of these droplets determines the safe distance between a patient and health care worker. Here, we have presented a method to visualize the droplets expelled by a sneeze using light-sheet illumination. This method of visualization provides images that clearly resolve the velocities of droplets with minimal overlapping trajectories, towards understanding their flow dynamics. Furthermore, we present the image processing techniques required to perform accurate Particle Tracking Velocimetry to understand the motion of expelled droplets. Flow fields are presented from applying this methodology over multiple sneezes which reveal that less than 1% of droplets expelled travel at velocities greater than 10 m/s and almost 80% of droplets travel at velocities less than 5 m/s. Furthermore, we observe that some droplets are generated by ligament breakup outside the mouth and some are generated within the respiratory tract.Graphic abstract

Towards automated in vivo tracheal mucociliary transport measurement: Detecting and tracking particle movement in synchrotron phase-contrast x-ray images

Accurate in vivo quantification of airway mucociliary transport (MCT) in animal models is important for understanding diseases such as cystic fibrosis, as well as for developing therapies. A non-invasive method of measuring MCT behaviour, based on tracking the position of micron sized particles using synchrotron x-ray imaging, has previously been described. In previous studies, the location (and path) of each particle was tracked manually, which is a time consuming and subjective process.Here we describe particle tracking methods that were developed to reduce the need for manual particle tracking. The MCT marker particles were detected in the synchrotron x-ray images using cascade classifiers. The particle trajectories along the airway surface were generated by linking the detected locations between frames using a modified particle linking algorithm. The developed methods were compared with the manual tracking method on simulated x-ray images, as well as on in vivo images of rat airways acquired at the SPring-8 Synchrotron.The results for the simulated and in vivo images showed that the semi-automatic algorithm reduced the time required for particle tracking when compared with the manual tracking method, and was able to detect MCT marker particle locations and measure particle speeds more accurately than the manual tracking method. Future work will examine the modification of methods to improve particle detection and particle linking algorithms to allow for more accurate fully-automatic particle tracking.

Deep-learning method for data association in particle tracking.

Biological studies of dynamic processes in living cells often require accurate particle tracking as a first step toward quantitative analysis. Although many particle tracking methods have been developed for this purpose, they are typically based on prior assumptions about the particle dynamics, and/or they involve careful tuning of various algorithm parameters by the user for each application. This may make existing methods difficult to apply by non-expert users and to a broader range of tracking problems. Recent advances in deep-learning techniques hold great promise in eliminating these disadvantages, as they can learn how to optimally track particles from example data. Here, we present a deep-learning-based method for the data association stage of particle tracking. The proposed method uses convolutional neural networks and long short-term memory networks to extract relevant dynamics features and predict the motion of a particle and the cost of linking detected particles from one time point to the next. Comprehensive evaluations on datasets from the particle tracking challenge demonstrate the competitiveness of the proposed deep-learning method compared to the state of the art. Additional tests on real-time-lapse fluorescence microscopy images of various types of intracellular particles show the method performs comparably with human experts. The software code implementing the proposed method as well as a description of how to obtain the test data used in the presented experiments will be available for non-commercial purposes from https://github.com/yoyohoho0221/pt_linking. Supplementary data are available at Bioinformatics online.

Postprocessing of standard finite element velocity fields for accurate particle tracking applied to groundwater flow

Particle tracking is a computationally advantageous and fast scheme to determine travel times and trajectories in subsurface hydrology. Accurate particle tracking requires element-wise mass-conservative, conforming velocity fields. This condition is not fulfilled by the standard linear Galerkin finite element method (FEM). We present a projection, which maps a non-conforming, element-wise given velocity field, computed on triangles and tetrahedra, onto a conforming velocity field in lowest-order Raviart-Thomas-Nédélec (mathcal {RTN}_{0}) space, which meets the requirements of accurate particle tracking. The projection is based on minimizing the difference in the hydraulic gradients at the element centroids between the standard FEM solution and the hydraulic gradients consistent with the mathcal {RTN}_{0} velocity field imposing element-wise mass conservation. Using the conforming velocity field in mathcal {RTN}_{0} space on triangles and tetrahedra, we present semi-analytical particle tracking methods for divergent and non-divergent flow. We compare the results with those obtained by a cell-centered finite volume method defined for the same elements, and a test case considering hydraulic anisotropy to an analytical solution. The velocity fields and associated particle trajectories based on the projection of the standard FEM solution are comparable to those resulting from the finite volume method, but the projected fields are smoother within zones of piecewise uniform hydraulic conductivity. While the mathcal {RTN}_{0}-projected standard FEM solution is thus more accurate, the computational costs of the cell-centered finite volume approach are considerably smaller.

CALET Results after Three Years on Orbit on the International Space Station

The CALorimetric Electron Telescope (CALET) is an astroparticle physics experiment installed on the International Space Station since August 2015. The CALET mission was conceived to address several outstanding questions of high-energy astroparticle physics, like indirect detection of dark matter, the origin of cosmic rays (CRs), their mechanisms of acceleration and galactic propagation, the presence of possible nearby astrophysical CR sources. That can be achieved by precise measurements of the fluxes of CR electrons and γ rays up to the unexplored region above 1 TeV, and the energy spectra and composition of CR nuclei from a few tens of GeV to hundreds of TeV. In order to perform these observations, the instrument combines a thick total absorption PWO crystal calorimeter for energy measurement, a scintillator hodoscope for charge identification and thin imaging tungsten-scintillating fiber calorimeter providing accurate particle tracking and complementary charge measurement. In this paper, we will present an overview of the main CALET results based on the data collected in the first three years of the mission.

Nanoscale Resolution 3D Snapshot Particle Tracking by Multifocal Microscopy.

Accurate, precise, and rapid particle tracking in three dimensions remains a challenge; yet, its achievement will significantly enhance our understanding of living systems. We developed amultifocal microscopy (MFM) that allows snapshot acquisition of the imaging data, and an associated image processing approach, that together allow simultaneous 3D tracking of many fluorescent particles with nanoscale resolution. The 3D tracking was validated by measuring a known trajectory of a fluorescent bead with an axial accuracy of 19 nm through an image depth (axial range) of 3 μm and 4 nm precision of axial localization through an image depth of 4 μm. A second test obtained a uniform axial probability distribution and Brownian dynamics of beads diffusing in solution. We also validated the MFM approach by imaging fluorescent beads immobilized in gels and comparing the 3D localizations to their "ground truth" positions obtained from a confocal microscopy z-stack of finely spaced images. Finally, we applied our MFM and image processing approach to obtain 3D trajectories of insulin granules in pseudoislets of MIN6 cells to demonstrate its compatibility with complex biological systems. Our study demonstrates that multifocal microscopy allows rapid (video rate) and simultaneous 3D tracking of many "particles" with nanoscale accuracy and precision in a wide range of systems, including over spatial scales relevant to whole live cells.

Measurement of cosmic-ray carbon and oxygen energy spectra with CALET

The CALorimetric Electron Telescope (CALET) is a space mission installed on the International Space Station (ISS) in August 2015. In addition to high precision measurements of the electron spectrum up to TeV scale, CALET will also investigate the mechanism of cosmic-ray (CR) acceleration and propagation in the Galaxy, by performing direct measurements of the energy spectra and elemental composition of CR nuclei from H to Fe, and the abundance of trans-iron elements up to about Z = 40. The instrument consists of two layers of segmented plastic scintillators to identify the particle charge, a thin (3 radiation lengths) tungsten-scintillating fiber calorimeter providing accurate particle tracking, and a thick (27 radiation lengths) calorimeter made of lead-tungstate crystal logs. In this paper, we discuss the analysis procedure developed to reconstruct and select carbon and oxygen nuclei in cosmic rays and present the preliminary measurement of their energy spectra based on the analysis of the data collected from October 2015 to February 2018.

Particle tracking by repetitive phase-shift interferometric super resolution microscopy.

Accurate and rapid particle tracking is essential for addressing many research problems in single molecule and cellular biophysics and colloidal soft condensed matter physics. We developed a novel three-dimensional interferometric fluorescent particle tracking approach that does not require any sample scanning. By periodically shifting the interferometer phase, the information stored in the interference pattern of the emitted light allows localizing particles positions with nanometer resolution. This tracking protocol was demonstrated by measuring a known trajectory of a fluorescent bead with sub-5nm axial localization error at 5Hz. The interferometric microscopy was used to track the RecA protein in Bacillus subtilis bacteria to demonstrate its compatibility with biological systems.

Direct Imaging of Vibrations in Colloidal Crystals: In Equilibrium and in a Steady Drift

Crystals of colloids, micron-size particles in a solvent, typically contain high concentrations of structural defects, limiting their applicability in self-assembly of metamaterials. Defects and grain boundaries play an important role for most properties of these crystals. Most previous research of colloidal crystals, by experiment and theory, focused on spatially averaged vibrational spectra: the differences in local environment between the bulk crystal particles and those at a grain boundary were typically neglected. We employ direct confocal microscopy and recent more accurate particle tracking algorithms to study the potential wells of individual particles in thermally vibrating quasi-two-dimensional colloidal crystals. We demonstrate that the energy landscape probed by a particle sensitively depends on its local environment. Furthermore, we emphasize the commonly neglected role of slight out-of-equilibrium drift of colloidal crystals, demonstrating that particle vibrations depend significantly on the...

Transition to a labyrinthine phase in a driven granular medium.

Labyrinthine patterns arise in two-dimensional physical systems submitted to competing interactions, in fields ranging from solid-state physics to hydrodynamics. For systems of interacting particles, labyrinthine and stripe phases were studied in the context of colloidal particles confined into a monolayer, both numerically by means of Monte Carlo simulations and experimentally using superparamagnetic particles. Here we report an experimental observation of a labyrinthine phase in an out-of-equilibrium system constituted of macroscopic particles. Once sufficiently magnetized, they organize into short chains of particles in contact and randomly orientated. We characterize the transition from a granular gas state towards a solid labyrinthine phase, as a function of the ratio of the interaction strength to the kinetic agitation. The spatial local structure is analyzed by means of accurate particle tracking. Moreover, we explain the formation of these chains using a simple model.

Accurate and consistent particle tracking on unstructured grids

SummaryA new numerical method for particle tracking (Lagrangian particle advection) on 2‐D unstructured grids with triangular cells is presented and tested. This method combines key attributes of published methods, including streamline closure for steady flows and local mass conservation (uniformity preservation). The subgrid‐scale velocity reconstruction is linear, and this linear velocity field is integrated analytically to obtain particle trajectories. A complete analytic solution to the 2‐D system of ordinary differential equations (ODEs) governing particle trajectories within a grid cell is provided. The analytic solution to the linear system of locally mass‐conserving constraints that must be enforced to obtain the coefficients in the ODEs is also provided. Numerical experiments are performed to demonstrate that the new method has substantial advantages in accuracy over previously published methods and that it does not suffer from unphysical particle clustering. The method can be used not only in particle‐tracking applications but also as part of a semi‐Lagrangian advection scheme.Copyright © 2015 John Wiley & Sons, Ltd.

Development of a graded index microlens based fiber optical trap and its characterization using principal component analysis.

We demonstrate a miniaturized single beam fiber optical trapping probe based on a high numerical aperture graded index (GRIN) micro-objective lens. This enables optical trapping at a distance of 200μm from the probe tip. The fiber trapping probe is characterized experimentally using power spectral density analysis and an original approach based on principal component analysis for accurate particle tracking. Its use for biomedical microscopy is demonstrated through optically mediated immunological synapse formation.

Numerical simulation of aerosol deposition from turbulent flows using three-dimensional RANS and LES turbulence models

In this study, three-dimensional computational fluid dynamics (CFD) simulations of particle deposition from turbulent flows in a vertical straight pipe were carried out, using the STAR-CCM+ v5.04 CFD package. The highlight of the study is the development of a post-processing approach for quantitatively assessing the accuracy of the Lagrangian particle tracking scheme. Three Reynolds-averaged Navier–Stokes (RANS) models and a large eddy simulation (LES) model were employed in the simulations, in conjunction with two wall treatment schemes and several near-wall mesh conditions. The particle deposition velocity was obtained based on the CFD simulation results, and compared to the experimental results. In post-processing, a “particle responsiveness factor,” defined as the ratio of particle mean square velocity fluctuation to fluid mean square velocity fluctuation for the wall-adjacent cells, was quantified using an Eulerian particle transport formulation. The particle responsiveness factor of the CFD simulations was then compared with that obtained using an empirical equation. The most accurate aerosol deposition results were obtained with a fine near-wall mesh (y+≈1) and resolved near-wall flow (no wall function). The LES model and the Reynolds stress model (RSM) produced the most accurate deposition velocity results, but the computation time of the former was up to ten times longer than that of the latter. The lower accuracy of the isotropic RANS models was attributed to their overprediction of the near-wall turbulence intensity gradient, and less accurate particle tracking as suggested by the particle responsiveness factor. The particle responsiveness factor, introduced for the first time in this study, was shown to be a useful index for evaluating accuracy of the Lagrangian particle tracking scheme due to its independence of specific knowledge of CFD algorithm and coding.

Fate and pathways of dredged estuarine sediment spoil in response to variable sediment size and baroclinic coastal circulation

Most of the world's megacities are located in estuarine regions supporting commercial ports. Such locations are subject to sedimentation and require dredging to maintain activities. Liverpool Bay, northwest UK, is a region of freshwater influence and hypertidal conditions used to demonstrate the impact of baroclinicity when considering sediment disposal. Although tidal currents dominate the time-varying current and onshore sediment movement, baroclinic processes cause a 2-layer residual circulation that influences the longer-term sediment transport. A nested modelling system is applied to accurately simulate the circulation during a three month period. The hydrodynamic model is validated using coastal observations, and a Lagrangian particle tracking model is used to determine the pathways of 2 sediment mixtures representative of locally dredged material: a mix of 70% silt and 30% medium sand and a mix of 50% fine sand and 50% medium sand. Sediments are introduced at 3 active disposal sites within the Mersey Estuary in 2 different quantities (500 and 1500 Tonnes). Following release the majority (83% or more) of the particles remain within the estuary due to baroclinic influence. However, particles able to leave follow 2 distinct pathways, which primarily depend on the sediment grain size. Typically the finer sediment moves north and the coarser sediment west. Under solely barotropic conditions larger sediment volumes (up to 5 times more) can leave the estuary in a diffuse plume moving north. This demonstrates the necessity of considering baroclinic influence even within a hypertidal region with low freshwater inflow for accurate particle tracking studies.

A model for tracking inertial particles in a lattice Boltzmann turbulent flow simulation

A computationally inexpensive model for tracking inertial particles through a turbulent flow is presented and applied to the turbulent flow through a square duct having a friction Reynolds number of Reτ=300. Prior to introducing particles into the model, the flow is simulated using a lattice Boltzmann computation, which is allowed to evolve until a steady state turbulent flow is achieved. A snapshot of the flow is then stored, and the trajectories of particles are computed through the flow domain under the influence of this static probability field. Although the flow is not computationally evolving during the particle tracking simulation, the local velocity is obtained stochastically from the local probability function, thus allowing the dynamics of the turbulent flow to be resolved from the point of view of the suspended particles. Particle inertia is modeled by using a relaxation parameter based on the particle Stokes number that allows for a particle velocity history to be incorporated during each time step. Wall deposition rates and deposition patterns are obtained and exhibit a high level of agreement with previously obtained DNS computational results and experimental results for a wide range of particle inertia. These results suggest that accurate particle tracking through complex turbulent flows may be feasible given a suitable probability field, such as one obtained from a lattice Boltzmann simulation. This in turn presents a new paradigm for the rapid acquisition of particle transport statistics without the need for concurrent computations of fluid flow evolution.

Rapid, accurate particle tracking by calculation of radial symmetry centers

I introduce an algorithm for subpixel localization of imaged objects based on an analytic, non-iterative calculation of the best-fit radial symmetry center. This approach yields tracking accuracies that are near theoretical limits, similarly to Gaussian fitting, but with orders-of-magnitude faster execution time, lower sensitivity to nearby particles and applicability to any radially symmetric intensity distribution. I demonstrate the method with several types of data, including super-resolution microscopy images.