Over the last years structured illumination digital holographic microscopy (SI-DHM) has been experimentally proved to double the resolution limit in conventional DHM. In SI-DHM, the underlying specimen is illuminated using a spatially varying structured illumination (SI) pattern, which enables super-resolution (SR) images to be retrieved using the proper computational reconstruction process. All these reconstruction methods require the acquisition of at least a couple phase-shifted DHM images. In particular, for a pure sinusoidal pattern, there is a need of recording two phase-shifted DHM images per orientation of the pattern (e.g., 6 images per isotropic SR improvement). Taking advantage of the simultaneous recording of the virtual (e.g., conjugated) image in the raw DHM image, here we present a novel computational method to reconstruct an isotropic SR image using one acquisition per pattern’s orientation (e.g. total 3 images per isotropic improvement). Because our proposed method shows a 50% reduction in the data acquisition and, therefore, acquisition time, we believe that our method should increase the utility of SI-DHM in live-cell imaging. We have validated our method using simulated and results.
We overview our recently published multi-dimensional integral imaging-based system for underwater optical signal detection. For robust signal detection, an optical signal propagating through the turbid water is encoded using multiple light sources and coded with spread spectrum techniques. An array of optical sensors captures video sequences of elemental images, which are reconstructed using multi-dimensional integral imaging followed by a 4D correlation to detect the transmitted signal. The area under the curve (AUC) and the number of detection errors were used as metrics to assess the performance of the system. The overviewed system successfully detects an optical signal under higher turbidity conditions than possible using conventional sensing and detection approaches.
In this keynote address paper, we overview recently published works on the current techniques and methods for automated cell identification with 3D optical imaging using compact and field portable systems. 3D imaging systems including digital holographic microscopy systems as well as lensless pseudorandom phase encoding systems are capable of capturing 3D information of microscopic objects such as biological cells which allows for highly accurate automated cell identification. Systems based on digital holography enable reconstruction of the cell’s 3D optical path length profile. The reconstructed 3D profiles can be used to extract morphological and spatio-temporal cell features from biological samples for classification and cell identification. Similarly, pseudorandom encoding techniques such as single random phase encoding (SRPE) and double random phase encoding (DRPE) can be used to encode 3D cell information into opto-biological signatures which can be used for cell identification tasks. Recent advancements in these areas are presented including compact and field-portable 3D-printed shearing digital holographic microscopy systems, integration of digital holographic microscopy with head mounted augmented reality devices, and the use of spatio-temporal features extracted from cell membrane fluctuations for sickle cell disease diagnosis.
We overview a previously reported system for automated diagnosis of sickle cell disease based on red blood cell (RBC) membrane fluctuations measured via digital holographic microscopy. A low-cost, compact, 3D-printed shearing interferometer is used to record video holograms of RBCs. Each hologram frame is reconstructed in order to form a spatio-temporal data cube from which features regarding membrane fluctuations are extracted. The motility-based features are combined with static morphology-based cell features and inputted into a random forest classifier which outputs the disease state of the cell with high accuracy.
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