Search

Digital in-line holographic microscopy (DIHM) is a non-invasive, real-time, label-free technique that captures three-dimensional (3D) positional, orientational, and morphological information from digital holographic images of living biological cells. Unlike conventional microscopies, the DIHM technique enables precise measurements of dynamic behaviors exhibited by living cells within a 3D volume. This review outlines the fundamental principles and comprehensive digital image processing procedures employed in DIHM-based cell tracking methods. In addition, recent applications of DIHM technique for label-free identification and digital tracking of various motile biological cells, including human blood cells, spermatozoa, diseased cells, and unicellular microorganisms, are thoroughly examined. Leveraging artificial intelligence has significantly enhanced both the speed and accuracy of digital image processing for cell tracking and identification. The quantitative data on cell morphology and dynamics captured by DIHM can effectively elucidate the underlying mechanisms governing various microbial behaviors and contribute to the accumulation of diagnostic databases and the development of clinical treatments., (© 2024. The Author(s).)

The morphological and mechanical characteristics of red blood cells (RBCs) largely vary depending on the occurrence of hematologic disorders. Variations in the rheological properties of RBCs affect the dynamic motions of RBCs, especially their rotational behavior. However, conventional techniques for measuring the orientation of biconcave-shaped RBCs still have some technical limitations, including complicated optical setups, complex post data processing, and low throughput. In this study, we propose a novel image-based technique for measuring 3D position and orientation of normal RBCs using digital in-line holographic microscopy (DIHM) and artificial intelligence (AI). Formaldehyde-fixed RBCs are immobilized in coagulated polydimethylsiloxane (PDMS). Holographic images of RBCs positioned at various out-of-plane angles are acquired by precisely manipulating the PDMS-trapped RBC sample attached to a 4-axis optical stage. With the aid of deep learning algorithms for data augmentation and regression analysis, the out-of-plane angle of RBCs is directly predicted from the captured holographic images. The 3D position and in-plane angle of RBCs are acquired by employing numerical reconstruction and ellipse detection methods. Combining these digital image processing techniques, the 3D positional and orientational information of each RBC recorded in a single holographic image is measured within 23.5 and 3.07 s, respectively. The proposed AI-based DIHM technique that can extract the 3D position, orientation, and morphology of individual RBCs would be utilized to analyze the dynamic translational and rotational motions of abnormal RBCs with hematologic disorders in shear flows through further research., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

We present a wide-angle, nonmechanical laser beam steerer based on polymer polarization gratings with an optimal design approach for maximizing field-of-regard (F\!O\!R ). The steering design offers exponential scaling of the number of steering angles, called suprabinary steering. The design approach can be easily adapted for any 1-D or 2-D (e.g., symmetric or asymmetric FOR) beam steering. We simulate a system using a finite difference and ray tracing tools and fabricate coarse beam steerer with 65^{\circ} F\!O\!R with \sim 8^{\circ} resolution at 1550 nm. We demonstrate high optical throughput (84\%$ –87$\%$) that can be substantially improved by optimizing substrates and electrode materials. This beam steerer can achieve very low sidelobes and supports comparatively large beam diameters paired with a very thin assembly and low beam walk-off. We also demonstrate using a certain type of LC variable retarder that the total switching time from any steering angle to another can be 1.7 ms or better. [ABSTRACT FROM PUBLISHER]

Airborne particulate matter (PM) has become a global environmental issue. This PM has harmful effects on public health and precision industries. Conventional air-quality monitoring methods usually utilize expensive equipment, and they are cumbersome to handle for accurate and high throughput measurements. In addition, commercial particle counters have technical limitations in high-concentration measurement, and data fluctuations are induced during air sampling. In this study, a novel smartphone-based technique for monitoring airborne PM concentrations was developed using smartphone-based digital holographic microscopy (S-DHM) and deep learning network called Holo-SpeckleNet. Holographic speckle images of various PM concentrations were recorded by the S-DHM system. The recorded speckle images and the corresponding ground truth PM concentrations were used to train deep learning algorithms consisting of a deep autoencoder and regression layers. The performance of the proposed smartphone-based PM monitoring technique was validated through hyperparameter optimization. The developed S-DHM integrated with Holo-SpeckleNet can be smartly and effectively utilized for portable PM monitoring and safety alarm provision under perilous environmental conditions. [Display omitted] • Smartphone-based PM monitoring device was developed. • PM concentration was predicted from holographic speckles of PMs by deep learning. • Volumetric monitoring of PMs was demonstrated without procedure of air intake. • Quasi-real-time PM monitoring was demonstrated. [ABSTRACT FROM AUTHOR]

Plants are considered as a possible modality to reduce particulate matter (PM) particles from ambient air in an ecofriendly manner. A new precise monitoring technique that can explore interactions between individual PM particles and a leaf surface is necessary to understand the underlying mechanisms of PM removal of plant leaves. In this study, a digital in-line holographic microscopy (DIHM) was employed to experimentally investigate the settling motions of PM particles over the leaf surface. The in-plane positions and sizes of opaque PMs with irregular shapes were obtained from the projection images of numerically reconstructed holographic images. The depth positions of PMs were determined by using proper selection of an autofocusing criterion with automatic segmentation method. The edge of a hairy Perilla frutescens leaf was detected by adopting several digital imaging processing techniques. The DIHM technique was applied in this study to accurately detect 3D settling trajectories of PMs with velocity information of PMs in the midair and near leaf surface, simultaneously. ga1 • DHM based monitoring technique for 3D volumetric analysis of PM is proposed. • Sizes and 3D motions of hundreds of PMs can be extracted at the same time. • Settling behaviors of PMs in midair and near a real plant leaf are investigated. [ABSTRACT FROM AUTHOR]