Abstract
Spatiotemporal tracking of tracer particles or objects of interest can reveal localized behaviors in biological and physical systems. However, existing tracking algorithms are most effective for relatively low numbers of particles that undergo displacements smaller than their typical interparticle separation distance. Here, we demonstrate a single particle tracking algorithm to reconstruct large complex motion fields with large particle numbers, orders of magnitude larger than previously tractably resolvable, thus opening the door for attaining very high Nyquist spatial frequency motion recovery in the images. Our key innovations are feature vectors that encode nearest neighbor positions, a rigorous outlier removal scheme, and an iterative deformation warping scheme. We test this technique for its accuracy and computational efficacy using synthetically and experimentally generated 3D particle images, including non-affine deformation fields in soft materials, complex fluid flows, and cell-generated deformations. We augment this algorithm with additional particle information (e.g., color, size, or shape) to further enhance tracking accuracy for high gradient and large displacement fields. These applications demonstrate that this versatile technique can rapidly track unprecedented numbers of particles to resolve large and complex motion fields in 2D and 3D images, particularly when spatial correlations exist.
Highlights
Comprehensive tracking of tracer particle trajectories can elucidate complex behaviors in soft materials, fluid flows, and biological motion by resolving local inhomogeneities in space and time
Single particle tracking (SPT) methods are typically based on a two step process: 1) particle positions are identified at each time frame, 2) particle positions are linked together into trajectories over consecutive frames
Particles are linked between consecutive image frames using a new particle descriptor, which encodes the spatial positions of their nearest neighbor particles for each time frame
Summary
Comprehensive tracking of tracer particle trajectories can elucidate complex behaviors in soft materials, fluid flows, and biological motion by resolving local inhomogeneities in space and time. Tracking large particle motions presents a significant challenge due to the uncertainty of assigning new positions to identical particles They are not computationally efficient when tracking large numbers of particles undergoing large amplitude motion fields. Increasing the overall particle seeding density reduces the interparticle separation, which limits the relative displacements that can be accurately and efficiently resolved using existing algorithms. This establishes a tradeoff whereby higher particle densities permit high spatial resolution but lower particle densities are needed to resolve large displacements. We present a new particle tracking scheme called Topology-based Particle Tracking (T-PT) to address the challenges of current SPT methods in reconstructing complex, large motion fields with high computational efficiency and spatial resolution. We characterize the performance and versatility of our algorithm using a combination of simulated and experimental images, including non-affine deformations of soft materials, complex fluid flows, and cell-generated substrate deformations
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