Distortion correction for particle image velocimetry using multiple-input deep convolutional neural network and Hartmann-Shack sensing

Micro-Scale Particle Tracking: From Conventional to Data-Driven Methods

Micro-scale positioning techniques have become essential in numerous engineering systems. In the field of fluid mechanics, particle tracking velocimetry (PTV) stands out as a key method for tracking individual particles and reconstructing flow fields. Here, we present an overview of the micro-scale particle tracking methodologies that are predominantly employed for particle detection and flow field reconstruction. It covers various methods, including conventional and data-driven techniques. The advanced techniques, which combine developments in microscopy, photography, image processing, computer vision, and artificial intelligence, are making significant strides and will greatly benefit a wide range of scientific and engineering fields.

AI-driven projection tomography with multicore fibre-optic cell rotation

Optical tomography has emerged as a non-invasive imaging method, providing three-dimensional insights into subcellular structures and thereby enabling a deeper understanding of cellular functions, interactions, and processes. Conventional optical tomography methods are constrained by a limited illumination scanning range, leading to anisotropic resolution and incomplete imaging of cellular structures. To overcome this problem, we employ a compact multi-core fibre-optic cell rotator system that facilitates precise optical manipulation of cells within a microfluidic chip, achieving full-angle projection tomography with isotropic resolution. Moreover, we demonstrate an AI-driven tomographic reconstruction workflow, which can be a paradigm shift from conventional computational methods, often demanding manual processing, to a fully autonomous process. The performance of the proposed cell rotation tomography approach is validated through the three-dimensional reconstruction of cell phantoms and HL60 human cancer cells. The versatility of this learning-based tomographic reconstruction workflow paves the way for its broad application across diverse tomographic imaging modalities, including but not limited to flow cytometry tomography and acoustic rotation tomography. Therefore, this AI-driven approach can propel advancements in cell biology, aiding in the inception of pioneering therapeutics, and augmenting early-stage cancer diagnostics.