Single-shot experimental-numerical twin-image removal in lensless digital holographic microscopy

Lensless digital holographic microscopy (LDHM) offers very large field-of-view label-free imaging crucial, e.g., in high-throughput particle tracking and biomedical examination of cells and tissues. Compact layouts promote point-of-case and out-of-laboratory applications. The LDHM, based on the Gabor in-line holographic principle, is inherently spoiled by the twin-image effect, which complicates the quantitative analysis of reconstructed phase and amplitude maps. Popular family of solutions consists of numerical methods, which tend to minimize twin-image upon iterative process based on data redundancy. Additional hologram recordings are needed, and final results heavily depend on the algorithmic parameters, however. In this contribution we present a novel single-shot experimental-numerical twin-image removal technique for LDHM. It leverages two-source off-axis hologram recording deploying simple fiber splitter. Additionally, we introduce a novel phase retrieval numerical algorithm specifically tailored to the acquired holograms, that provides twin-image-free reconstruction without compromising the resolution. We quantitatively and qualitatively verify proposed method employing phase test target and cheek cells biosample. The results demonstrate that the proposed technique enables low-cost, out-of-laboratory LDHM imaging with enhanced precision, achieved through the elimination of twin-image errors. This advancement opens new avenues for more accurate technical and biomedical imaging applications using LDHM, particularly in scenarios where cost-effective and portable imaging solutions are desired

Hypertension Awareness and Health Care Access/Use in Black Women with Hypertension

Black women in the United States have a high prevalence of hypertension and suffer the most complications of cardiovascular disease. Black women, though aware of the dangers associated with hypertension, have limited opportunity to access health care and or change their lifestyles. The purpose of this quantitative cross-sectional study was to test if there was a significant difference in hypertension awareness, health care access/use, and lifestyle modifications in Black women prior to and post implementation of The Patient Protection and Affordable Care Act, as compared to women of other races. The behavior modification theory guided this study. Secondary data from the National Health Interview Survey for the years 2009 to 2013 for women ages 20 - 65 were analyzed using logistic regression analysis. According to the study results, there was no association (p values \u3e 0.05) among variables age, education, income, length of employment, and hypertension awareness, health care access/use, and life style modification among Black women in the United States, as compared to women of other races. The findings from this study may allow researchers and policy makers to develop more culturally significant health services for Black women. These findings could create positive social change by targeting programs that promote hypertension awareness leading to effective lifestyle changes in Black women

A Microfluidic Assay for Single Cell Bacterial Adhesion Studies Under Shear Stress

The study of bacterial adhesion to host cells is important in understanding bacterial pathogenesis and developing new therapeutic approaches. Here, we studied bacterial adhesion under shear stress using a novel microfluidic method. Specifically, the adhesion of a uropathogenic E. coli strain (FimHOn, ATCC 700928/CFT073) to mannose-modified substrates was studied under flow conditions. The FimHOn E. coli strain expresses FimH which is a mannose-specific adhesin found on the fimbriae that binds to glycoproteins on the epithelium. We developed a microfluidic method that mimics bacterial adhesion to urothelial cells. First, the microfluidic channels were modified by sequentially adsorbing BSA-mannose and BSA onto channel surfaces. Bacterial solutions were then introduced to the microfluidic channels and bacterial interactions with the modified surface were imaged at 5 fps for 2 minutes using phase contrast microscopy under flow conditions. Manual tracking and TrackMate extensions of ImageJ were used to analyze and quantify surface adhesion of bacteria on the simulated epithelial surface. Bacteria-surface interactions were studied with substrates modified using 8.3µg/mL, 16.7µg/mL, and 25.0µg/mL BSA-mannose solutions. Through image analysis, the percentage of bacteria interacting with the surface and the total interaction times were determined. The results indicated that as mannose concentration increased the average transient adhesion time and percentage of bacteria adhered to the surface also increased. It was also observed that bacteria permanently attached to the surface increased with time. Overall, our results show that FimHOn E. coli specifically and transiently interacts with the mannose-modified surface. By mimicking molecular interactions and flow-induced shear stress within the gastrointestinal, respiratory, and urogenital tracts, our microfluidic platform may help explain mechanisms underlying bacterial infections at the mucosal epithelium. Overall, our microfluidic approach provides a favorable platform to study bacterial host cell interactions to enable drug discovery and testing

Temporal phase unwrapping using deep learning

The multi-frequency temporal phase unwrapping (MF-TPU) method, as a classical phase unwrapping algorithm for fringe projection profilometry (FPP), is capable of eliminating the phase ambiguities even in the presence of surface discontinuities or spatially isolated objects. For the simplest and most efficient case, two sets of 3-step phase-shifting fringe patterns are used: the high-frequency one is for 3D measurement and the unit-frequency one is for unwrapping the phase obtained from the high-frequency pattern set. The final measurement precision or sensitivity is determined by the number of fringes used within the high-frequency pattern, under the precondition that the phase can be successfully unwrapped without triggering the fringe order error. Consequently, in order to guarantee a reasonable unwrapping success rate, the fringe number (or period number) of the high-frequency fringe patterns is generally restricted to about 16, resulting in limited measurement accuracy. On the other hand, using additional intermediate sets of fringe patterns can unwrap the phase with higher frequency, but at the expense of a prolonged pattern sequence. Inspired by recent successes of deep learning techniques for computer vision and computational imaging, in this work, we report that the deep neural networks can learn to perform TPU after appropriate training, as called deep-learning based temporal phase unwrapping (DL-TPU), which can substantially improve the unwrapping reliability compared with MF-TPU even in the presence of different types of error sources, e.g., intensity noise, low fringe modulation, and projector nonlinearity. We further experimentally demonstrate for the first time, to our knowledge, that the high-frequency phase obtained from 64-period 3-step phase-shifting fringe patterns can be directly and reliably unwrapped from one unit-frequency phase using DL-TPU

The Role of Iron in the Arctic Carbon Cycle

Interactions between iron and organic carbon (OC) in soils influence the amount of soil OC that is oxidized to carbon dioxide (CO2), a greenhouse gas warming our planet. Although both microbial and abiotic iron redox reactions can oxidize soil OC to CO2, the role of abiotic iron redox reactions in the oxidation of soil OC to CO2 remains poorly understood. Oxidation of reduced ferrous iron (Fe(II)) by dissolved oxygen produces hydroxyl radical (•OH), a reactive oxidant that may oxidize dissolved OC (DOC) to CO2. Production of •OH from Fe(II) oxidation has been well-studied in controlled laboratory experiments, but it is unknown whether this process is an important pathway for the oxidation of DOC to CO2 in soils. To address this knowledge gap, the oxidation of Fe(II) and the subsequent •OH and CO2 production were measured in arctic soil waters. •OH was produced in all soil waters studied in the Arctic, and the oxidation of Fe(II) by dissolved oxygen was found to be the main source of •OH. The •OH produced from this reaction oxidized DOC to CO2 in controlled laboratory experiments and in soil waters. The production yield of CO2 from the oxidation of DOC by •OH varied by 2- to 50- fold possibly due to differences in DOC chemical composition. On a broader, landscape scale, Fe(II) production rates, and thus •OH and CO2 production rates, varied by landscape age and vegetation type. For example, Fe(II) production rates were higher in the upland, older mineral-rich soils with tussock vegetation than the lowland, younger organic-rich soils with wet sedge vegetation. In all soils, the magnitude of •OH and CO2 production depended on the balance of (i) the rates of Fe(II) oxidation by dissolved oxygen and (ii) the rates of Fe(II) production. Dissolved oxygen supplied to the soils with rainfall oxidized Fe(II), resulting in higher •OH and CO2 production than under static, waterlogged conditions. During rainfall events, Fe(II) was continuously detected despite oxidizing conditions, suggesting that Fe(II) production exceeded its oxidation. Under static, waterlogged conditions, Fe(II) oxidation, and thus •OH and CO2 production, was limited by the supply of dissolved oxygen to the soils. On a landscape scale in the Arctic, the rates of CO2 production from DOC oxidation by •OH in soils were comparable to the rates of CO2 production from microbial respiration of DOC in surface waters. Thus, this dissertation research demonstrated a novel pathway for soil OC oxidation where abiotic interactions between iron and OC can be an important source of CO2 to the atmosphere. As the Arctic warms, permafrost soils are thawing and releasing high concentrations of iron and OC that are susceptible to oxidation. The conversion of this permafrost OC to CO2 will result in positive and accelerating feedback to climate change. The results from this thesis improve our ability to predict this feedback by identifying the controls on the magnitude of the CO2 produced from iron-mediated OC oxidation in soils.PHDEarth and Environmental SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155098/1/atrusiak_1.pd

Numerically Enhanced Stimulated Emission Depletion Microscopy with Adaptive Optics for Deep-Tissue Super-Resolved Imaging

Copyright © 2019 American Chemical Society. In stimulated emission depletion (STED) nanoscopy, the major origin of decreased signal-to-noise ratio within images can be attributed to sample photobleaching and strong optical aberrations. This is due to STED utilizing a high-power depletion laser (increasing the risk of photodamage), while the depletion beam is very sensitive to sample-induced aberrations. Here, we demonstrate a custom-built STED microscope with automated aberration correction that is capable of 3D super-resolution imaging through thick, highly aberrating tissue. We introduce and investigate a state of the art image denoising method by block-matching and collaborative 3D filtering (BM3D) to numerically enhance fine object details otherwise mixed with noise and further enhance the image quality. Numerical denoising provides an increase in the final effective resolution of the STED imaging of 31% using the well established Fourier ring correlation metric. Results achieved through the combination of aberration correction and tailored image processing are experimentally validated through super-resolved 3D imaging of axons in differentiated induced pluripotent stem cells growing under an 80 μm thick layer of tissue with lateral and axial resolution of 204 and 310 nm, respectively

DeepOrientation: convolutional neural network for fringe pattern orientation map estimation

Fringe pattern based measurement techniques are the state-of-the-art in full-field optical metrology. They are crucial both in macroscale, e.g., fringe projection profilometry, and microscale, e.g., label-free quantitative phase microscopy. Accurate estimation of the local fringe orientation map can significantly facilitate the measurement process on various ways, e.g., fringe filtering (denoising), fringe pattern boundary padding, fringe skeletoning (contouring/following/tracking), local fringe spatial frequency (fringe period) estimation and fringe pattern phase demodulation. Considering all of that the accurate, robust and preferably automatic estimation of local fringe orientation map is of high importance. In this paper we propose novel numerical solution for local fringe orientation map estimation based on convolutional neural network and deep learning called DeepOrientation. Numerical simulations and experimental results corroborate the effectiveness of the proposed DeepOrientation comparing it with the representative of the classical approach to orientation estimation called combined plane fitting/gradient method. The example proving the effectiveness of DeepOrientation in fringe pattern analysis, which we present in this paper is the application of DeepOrientation for guiding the phase demodulation process in Hilbert spiral transform. In particular, living HeLa cells quantitative phase imaging outcomes verify the method as an important asset in label-free microscopy

Deep learning enabled single-shot absolute phase recovery in high-speed composite fringe pattern profilometry of separated objects

A recent article in the Opto-Electronic Advances (OEA) journal from Prof. Qian Chen and Prof. Chao Zuo’s group introduced a new and efficient 3D imaging system that captures high-speed images using deep learning-enabled fringe projection profilometry (FPP). In this News & Views article, we explore potential avenues for future advancements, including expanding the measurement range through an extended number-theoretical approach, enhancing quality through the incorporation of horizontal fringes, and integrating data from other modalities to broaden the system's applications

Single-shot fringe pattern phase retrieval using improved period-guided bidimensional empirical mode decomposition and Hilbert transform

Fringe pattern analysis is the central aspect of numerous optical measurement methods, e.g., interferometry, fringe projection, digital holography, quantitative phase microscopy. Experimental fringe patterns always contain significant features originating from fluctuating environment, optical system and illumination quality, and the sample itself that severely affect analysis outcome. Before the stage of phase retrieval (information decoding) interferogram needs proper filtering, which minimizes the impact of mentioned issues. In this paper we propose fully automatic and adaptive fringe pattern pre-processing technique - improved period guided bidimensional empirical mode decomposition algorithm (iPGBEMD). It is based on our previous work about PGBEMD which eliminated the mode-mixing phenomenon and made the empirical mode decomposition fully adaptive. In present work we overcame key problems of original PGBEMD – we have considerably increased algorithm’s application range and shortened computation time several-fold. We proposed three solutions to the problem of erroneous decomposition for very low fringe amplitude images, which limited original PGBEMD significantly and we have chosen the best one among them after comprehensive analysis. Several acceleration methods were also proposed and merged to ensure the best results. We combined our improved pre-processing algorithm with the Hilbert Spiral Transform to receive complete, consistent, and versatile fringe pattern analysis path. Quality and effectiveness evaluation, in comparison with selected reference methods, is provided using numerical simulations and experimental fringe data

Versatile optimization-based speed-up method for autofocusing in digital holographic microscopy

We propose a speed-up method for the in-focus plane detection in digital holographic microscopy that can be applied to a broad class of autofocusing algorithms that involve repetitive propagation of an object wave to various axial locations to decide the in-focus position. The classical autofocusing algorithms apply a uniform search strategy, i.e., they probe multiple, uniformly distributed axial locations, which leads to heavy computational overhead. Our method substantially reduces the computational load, without sacrificing the accuracy, by skillfully selecting the next location to investigate, which results in a decreased total number of probed propagation distances. This is achieved by applying the golden selection search with parabolic interpolation, which is the gold standard for tackling single-variable optimization problems. The proposed approach is successfully applied to three diverse autofocusing cases, providing up to 136-fold speed-up