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11 pages, 7800 KiB  
Communication
Lens-Free On-Chip Quantitative Phase Microscopy for Large Phase Objects Based on a Biplane Phase Retrieval Method
by Yufan Chen, Xuejuan Wu, Yang Chen, Wenhui Lin, Haojie Gu, Yuzhen Zhang and Chao Zuo
Sensors 2025, 25(1), 3; https://doi.org/10.3390/s25010003 - 24 Dec 2024
Viewed by 357
Abstract
Lens-free on-chip microscopy (LFOCM) is a powerful computational imaging technology that combines high-throughput capabilities with cost efficiency. However, in LFOCM, the phase recovered by iterative phase retrieval techniques is generally wrapped into the range of −π to π, necessitating phase unwrapping [...] Read more.
Lens-free on-chip microscopy (LFOCM) is a powerful computational imaging technology that combines high-throughput capabilities with cost efficiency. However, in LFOCM, the phase recovered by iterative phase retrieval techniques is generally wrapped into the range of −π to π, necessitating phase unwrapping to recover absolute phase distributions. Moreover, this unwrapping process is prone to errors, particularly in areas with large phase gradients or low spatial sampling, due to the absence of reliable initial guesses. To address these challenges, we propose a novel biplane phase retrieval (BPR) method that integrates phase unwrapping results obtained at different propagation distances to achieve accurate absolute phase reconstruction. The effectiveness of BPR is validated through live-cell imaging of HeLa cells, demonstrating improved quantitative phase imaging (QPI) accuracy when compared to conventional off-axis digital holographic microscopy. Furthermore, time-lapse imaging of COS-7 cells in vitro highlights the method’s robustness and capability for long-term quantitative analysis of large cell populations. Full article
(This article belongs to the Special Issue Digital Holography in Optics: Techniques and Applications)
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Figure 1

Figure 1
<p>The BPR of lens-free on-chip quantitative phase microscopy. (<b>a</b>) Schematic of the LFOCM system, including the CMOS sensor, color LED matrix, and sample. (<b>b</b>) Schematic comparison of BPR and traditional single-plane phase retrieval (PR) method under multi-wavelength illumination.</p>
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<p>Flowchart of the BPR algorithm, illustrating four stages: initialization of two planes, iterative phase retrieval, wavelength conversion and fusion, and multi-wavelength phase recovery.</p>
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<p>Experimental results of C166 cell slide. (<b>a1</b>,<b>b1</b>) The holograms of two selected regions. (<b>a2</b>,<b>b2</b>) Phase reconstruction of two regions under the CMW algorithm. (<b>a3</b>,<b>b3</b>) Zoomed-in comparison of the reconstructed phases of cell1 and cell2 using the CMW algorithm and the BPR algorithm. (<b>a4</b>,<b>a5</b>) and (<b>b4</b>,<b>b5</b>) Phase recovery of object plane 1 and object plane 2 in the selected region by the BPR algorithm. (<b>a6</b>,<b>b6</b>) Phase reconstruction of two regions under BPR algorithm. (<b>a7</b>,<b>b7</b>) Comparison of 3D rendering of cell1 and cell2 under CMW algorithm and BPR algorithm. (<b>a8</b>,<b>b8</b>) Phase values along the vertical line in (<b>a3</b>,<b>b3</b>).</p>
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<p>Experimental results of HeLa cells. (<b>a1</b>–<b>a3</b>) Phase reconstructed using DHM, CMW, and BPR methods. (<b>b1</b>–<b>b3</b>) Magnified regions of ROI 1. (<b>c1</b>–<b>c3</b>) Magnified regions of ROI 2. (<b>b4</b>,<b>c4</b>) Error maps between the BPR method and the DHM method in ROI 1 and ROI 2. (<b>d1</b>,<b>d2</b>) Phase value curves along the white solid line in ROI 1 and ROI 2. (<b>e</b>) A 3D rendering of the reconstructed phases by the DHM, CMW, and BPR methods.</p>
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<p>Dynamic phase imaging of COS-7 cells. (<b>a</b>) The hologram and phase reconstruction of the full FOV. (<b>b1</b>,<b>b2</b>) The 3D renderings corresponding to the cell in (<b>d1</b>,<b>d2</b>). (<b>c</b>) Cell dry mass computed under the BPR algorithm versus the CMW algorithm over time. (<b>d1</b>–<b>d6</b>,<b>e1</b>–<b>e6</b>) Six selected time-lapse phase images of ROI under the CMW algorithm and BPR algorithm. (<b>f1</b>–<b>f6</b>) Phase values along the white lines in (<b>d1</b>–<b>e6</b>).</p>
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14 pages, 3513 KiB  
Article
Digital Holographic Microscopy in Veterinary Medicine—A Feasibility Study to Analyze Label-Free Leukocytes in Blood and Milk of Dairy Cows
by Sabine Farschtschi, Manuel Lengl, Stefan Röhrl, Christian Klenk, Oliver Hayden, Klaus Diepold and Michael W. Pfaffl
Animals 2024, 14(21), 3156; https://doi.org/10.3390/ani14213156 - 3 Nov 2024
Viewed by 1123
Abstract
For several years, the determination of a differential cell count of a raw milk sample has been proposed as a more accurate tool for monitoring the udder health of dairy cows compared with using the absolute somatic cell count. However, the required sample [...] Read more.
For several years, the determination of a differential cell count of a raw milk sample has been proposed as a more accurate tool for monitoring the udder health of dairy cows compared with using the absolute somatic cell count. However, the required sample preparation and staining process can be labor- and cost-intensive. Therefore, the aim of our study was to demonstrate the feasibility of analyzing unlabeled blood and milk leukocytes from dairy cows by means of digital holographic microscopy (DHM). For this, we trained three different machine learning methods, i.e., k-Nearest Neighbor, Random Forests, and Support Vector Machine, on sorted leukocyte populations (granulocytes, lymphocytes, and monocytes/macrophages) isolated from blood and milk samples of three dairy cows by using fluorescence-activated cell sorting. Afterward, those classifiers were applied to differentiate unlabeled blood and milk samples analyzed by DHM. A total of 70 blood and 70 milk samples were used. Those samples were collected from five clinically healthy cows at 14-time points within a study period of 26 days. The outcome was compared with the results of the same samples analyzed by flow cytometry and (in the case of blood samples) also to routine analysis in an external laboratory. Moreover, a standard vaccination was used as an immune stimulus during the study to check for changes in cell morphology or cell counts. When applied to isolated leukocytes, Random Forests performed best, with a specificity of 0.93 for blood and 0.84 for milk cells and a sensitivity of 0.90 and 0.81, respectively. Although the results of the three analytical methods differed, it could be demonstrated that a DHM analysis is applicable for blood and milk leukocyte samples with high reliability. Compared with the flow cytometric results, Random Forests showed an MAE of 0.11 (SD = 0.04), an RMSE of 0.13 (SD = 0.14), and an MRE of 1.00 (SD = 1.11) for all blood leukocyte counts and an MAE of 0.20 (SD = 0.11), an RMSE of 0.21 (SD = 0.11) and an MRE of 1.95 (SD = 2.17) for all milk cell populations. Further studies with larger sample sizes and varying immune cell compositions are required to establish method-specific reference ranges. Full article
(This article belongs to the Section Cattle)
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Figure 1

Figure 1
<p>Schematic overview of the workflow.</p>
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<p>Sampling scheme of blood and milk samples. Blood and milk samples were collected from each of the five cows at 14 time points. All cows were vaccinated on day 8 after sampling.</p>
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<p>Representative false-color phase images of different populations of unlabeled leukocytes, analyzed in DHM. (<b>A</b>) Blood granulocyte; (<b>B</b>) Blood lymphocyte; (<b>C</b>) Blood monocyte; (<b>D</b>) Milk granulocyte; (<b>E</b>) Milk lymphocyte; (<b>F</b>) Milk macrophage.</p>
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<p>Exemplary scatter plots of flow cytometric and digital holographic microscopy analyses. (<b>A</b>) Blood leukocyte populations analyzed by FACS; (<b>B</b>) Blood leukocyte populations analyzed by DHM; (<b>C</b>) Milk leukocyte populations analyzed by FACS; (<b>D</b>) Milk leukocyte populations analyzed by DHM.</p>
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<p>Confusion matrix showing the results of Random Forest classification of sorted blood cells.</p>
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<p>Confusion matrix showing the results of Random Forest classification of sorted milk cells.</p>
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<p>Cell count progression over time, DHM results obtained using k-Nearest Neighbor. (<b>A</b>) Blood cells of cow #963, analyzed by DHM, FACS and external laboratory; (<b>B</b>) Milk cells of cow #963, analyzed by DHM and FACS.</p>
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17 pages, 5605 KiB  
Review
Imaging of Live Cells by Digital Holographic Microscopy
by Emilia Mitkova Mihaylova
Photonics 2024, 11(10), 980; https://doi.org/10.3390/photonics11100980 - 18 Oct 2024
Viewed by 1157
Abstract
Imaging of microscopic objects is of fundamental importance, especially in life sciences. Recent fast progress in electronic detection and control, numerical computation, and digital image processing, has been crucial in advancing modern microscopy. Digital holography is a new field in three-dimensional imaging. Digital [...] Read more.
Imaging of microscopic objects is of fundamental importance, especially in life sciences. Recent fast progress in electronic detection and control, numerical computation, and digital image processing, has been crucial in advancing modern microscopy. Digital holography is a new field in three-dimensional imaging. Digital reconstruction of a hologram offers the remarkable capability to refocus at different depths inside a transparent or semi-transparent object. Thus, this technique is very suitable for biological cell studies in vivo and could have many biomedical and biological applications. A comprehensive review of the research carried out in the area of digital holographic microscopy (DHM) for live-cell imaging is presented. The novel microscopic technique is non-destructive and label-free and offers unmatched imaging capabilities for biological and bio-medical applications. It is also suitable for imaging and modelling of key metabolic processes in living cells, microbial communities or multicellular plant tissues. Live-cell imaging by DHM allows investigation of the dynamic processes underlying the function and morphology of cells. Future applications of DHM can include real-time cell monitoring in response to clinically relevant compounds. The effect of drugs on migration, proliferation, and apoptosis of abnormal cells is an emerging field of this novel microscopic technique. Full article
(This article belongs to the Special Issue Technologies and Applications of Digital Holography)
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Figure 1

Figure 1
<p>Interference on the screen of a CCD camera of a plane reference wave R(x,y) and an object wave O(x,y).</p>
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<p>Optical set-up of a digital in-line holographic microscope.</p>
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<p>Basic schematic of a digital holographic microscope based on a Match-Zehnder interferometric configuration.</p>
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<p>Images of (<b>a</b>) digital hologram of algae <span class="html-italic">Pseudokirchneriella subcapitata</span>; (<b>b</b>–<b>d</b>) the reconstructed intensities at four consecutive planes. The distance between the planes changes by 2 μm.</p>
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<p>Images of algae <span class="html-italic">Tetraselmis suecica</span>: (<b>a</b>,<b>c</b>,<b>e</b>) digital holograms; (<b>b</b>,<b>d</b>,<b>f</b>) the wave front intensities of the corresponding images. Cell size is 10.3 μm ± 9.5%.</p>
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<p>Healthy, fresh human erythrocytes as captured using digital holographic microscopy. The cells are 2–3 μm thick (reprinted from [<a href="#B33-photonics-11-00980" class="html-bibr">33</a>]).</p>
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<p>Determination of the refractive index of stenotic and non-stenotic intestinal tissue of Crohn’s disease patients using digital holographic microscopy (DHM). Histological evaluation of HE-staining and the corresponding quantitative DHM phase contrast image show certain fibrotic changes of the submucosal layer of stenotic (<b>C</b>,<b>D</b>) compared to non-stenotic bowel tissue (<b>A</b>,<b>B</b>) (reprinted from [<a href="#B38-photonics-11-00980" class="html-bibr">38</a>]).</p>
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<p>Lund human mesencephalic neurons (LUHMES), which have been induced to differentiate, can be analyzed for area and optical thickness. (<b>A</b>) represents cells before the differentiation process has started, while (<b>B</b>) represents cells at the end of the differentiation process. The y-axis represents the peak thickness of the cells while the x-axis represents the area in μm<sup>2</sup> of each individual object segmented in the image. Each square represents one cell. (<b>C</b>) shows the cells before the differentiation process started while (<b>D</b>) shows the cells at the end of the differentiation process (reprinted from [<a href="#B33-photonics-11-00980" class="html-bibr">33</a>]).</p>
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<p>Images of cell suspension culture A: (<b>a</b>) digital hologram; (<b>b</b>) the numerically reconstructed wave front intensity of (<b>a</b>).</p>
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<p>Images of cell suspension culture D: (<b>a</b>) digital hologram; (<b>b</b>) the numerically reconstructed wave front intensity of (<b>a</b>).</p>
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<p>Images of cell suspension culture MSD: (<b>a</b>) digital hologram; (<b>b</b>) the numerically reconstructed wave front intensity of (<b>a</b>).</p>
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<p>Examples of phase images of HeLa, A549 and 3T3 cells in three states: live, apoptotic and necrotic, obtained using digital holography (reprinted from [<a href="#B51-photonics-11-00980" class="html-bibr">51</a>]).</p>
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<p>Measurement of the spatial phase sensitivity of QPM for direct laser and pseudo-thermal light sources. (<b>a</b>,<b>d</b>) are the interferograms obtained with healthy sperm cell as a test specimen, (<b>b</b>,<b>e</b>) reconstructed phase map of the sperm cell corresponding to (<b>a</b>,<b>d</b>), respectively and (<b>c</b>,<b>f</b>) spatial phase noise of the experimental setup for laser and pseudo-thermal light sources, respectively. Note that the scale of the color bars used in (<b>c</b>,<b>f</b>) having different values (reprinted from [<a href="#B55-photonics-11-00980" class="html-bibr">55</a>]).</p>
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<p>3D pseudo-coloured phase plots of HeLa cells obtained before PDT (<b>a</b>,<b>c</b>) and 60 min after irradiation at 22.1 mW/cm<sup>2</sup> (<b>b</b>) and 93 mW/cm<sup>2</sup> (<b>d</b>) (reprinted from [<a href="#B57-photonics-11-00980" class="html-bibr">57</a>]).</p>
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22 pages, 8501 KiB  
Article
Antioxidants Hydroxytyrosol and Thioredoxin-Mimetic Peptide CB3 Protect Irradiated Normal Tissue Cells
by Katrin Borrmann, Fabian Martin Troschel, Kathrin Annemarie Brücksken, Nancy Adriana Espinoza-Sánchez, Maryam Rezaei, Kai Moritz Eder, Björn Kemper, Hans Theodor Eich and Burkhard Greve
Antioxidants 2024, 13(8), 961; https://doi.org/10.3390/antiox13080961 - 7 Aug 2024
Viewed by 1161
Abstract
Reducing side effects in non-cancerous tissue is a key aim of modern radiotherapy. Here, we assessed whether the use of the antioxidants hydroxytyrosol (HT) and thioredoxin-mimetic peptide CB3 (TMP) attenuated radiation-induced normal tissue toxicity in vitro. We used primary human umbilical vein endothelial [...] Read more.
Reducing side effects in non-cancerous tissue is a key aim of modern radiotherapy. Here, we assessed whether the use of the antioxidants hydroxytyrosol (HT) and thioredoxin-mimetic peptide CB3 (TMP) attenuated radiation-induced normal tissue toxicity in vitro. We used primary human umbilical vein endothelial cells (HUVECs) and human epidermal keratinocytes (HaCaT) as normal tissue models. Cells were treated with HT and TMP 24 h or immediately prior to irradiation. Reactive oxygen species (ROS) were assessed via luminescent- and fluorescence-based assays, migration was investigated using digital holographic microscopy, and clonogenic survival was quantified by colony formation assays. Angiogenesis and wound healing were evaluated via time-dependent microscopy. Secreted cytokines were validated in quantitative polymerase chain reaction (qPCR) studies. Treatment with HT or TMP was well tolerated by cells. The application of either antioxidant before irradiation resulted in reduced ROS formation and a distinct decrease in cytokines compared to similarly irradiated, but otherwise untreated, controls. Antioxidant treatment also increased post-radiogenic migration and angiogenesis while accelerating wound healing. HT or TMP treatment immediately before radiotherapy increased clonogenic survival after radiotherapy, while treatment 24 h before radiotherapy enhanced baseline proliferation. Both antioxidants may decrease radiation-induced normal tissue toxicity and deserve further pre-clinical investigation. Full article
(This article belongs to the Special Issue Radioprotective Effects of Antioxidants)
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Graphical abstract

Graphical abstract
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<p>Skeletal formulars of antioxidants. Skeletal formulars of HT (<b>A</b>) and TMP (<b>B</b>). Carbon atoms are shown in grey, oxygen atoms in red, nitrogen atoms in blue, sulfur atoms in yellow, and hydrogen atoms in white. The dashed line represents the conjugated double bonds in the benzene ring (<b>A</b>).</p>
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<p>Antioxidants modulate cell viability. Treatment with HT and TMP initially led to a slight reduction in the viability of HaCaT (<b>A</b>) at low concentrations, but viability increased again at higher concentrations. An increase in viability was observed for HUVECs (<b>B</b>), especially for 100 µM HT. Values represent the mean of three independent experiments with error bars indicating the standard error of the mean (s.e.m.). <span class="html-italic">p</span> values &lt; 0.05 were deemed significant (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001) and represent the significant difference to the control.</p>
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<p>Antioxidant supplementation reduces radiation-induced ROS levels and DNA double-strand breaks in HaCaT cells and HUVEC cultures. Irradiation leads to significantly elevated ROS levels in HaCaT cells (<b>A</b>,<b>B</b>) and HUVECs (<b>C</b>,<b>D</b>). Incubation with HT or TMP, respectively, 24 h before (<b>A</b>,<b>C</b>) or during radiation treatment (<b>B</b>,<b>D</b>) decreases the amount of radiation-induced ROS significantly. ROS levels in HaCaT cells and HUVECs treated with HT and TMP during irradiation (<b>B</b>,<b>D</b>) remained almost at the level of unirradiated, treated cells. After irradiation, the number of DNA double-strand breaks increased significantly in otherwise untreated cell cultures (<b>E</b>–<b>H</b>). Treatment with HT and TMP before irradiation reduced DNA damage in both HaCaT cells (<b>E</b>) and HUVECs (<b>G</b>), while treatment during irradiation only decreased the number of DNA double-strand breaks in HaCaT cells (<b>F</b>) but not in HUVECs (<b>H</b>). HUVECs were more susceptible to irradiation-induced double-strand breaks than HaCaT cells. Values represent the mean of three independent experiments, with error bars indicating the standard error of the mean (s.e.m.). <span class="html-italic">p</span> values &lt; 0.05 were deemed significant (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001).</p>
Full article ">Figure 3 Cont.
<p>Antioxidant supplementation reduces radiation-induced ROS levels and DNA double-strand breaks in HaCaT cells and HUVEC cultures. Irradiation leads to significantly elevated ROS levels in HaCaT cells (<b>A</b>,<b>B</b>) and HUVECs (<b>C</b>,<b>D</b>). Incubation with HT or TMP, respectively, 24 h before (<b>A</b>,<b>C</b>) or during radiation treatment (<b>B</b>,<b>D</b>) decreases the amount of radiation-induced ROS significantly. ROS levels in HaCaT cells and HUVECs treated with HT and TMP during irradiation (<b>B</b>,<b>D</b>) remained almost at the level of unirradiated, treated cells. After irradiation, the number of DNA double-strand breaks increased significantly in otherwise untreated cell cultures (<b>E</b>–<b>H</b>). Treatment with HT and TMP before irradiation reduced DNA damage in both HaCaT cells (<b>E</b>) and HUVECs (<b>G</b>), while treatment during irradiation only decreased the number of DNA double-strand breaks in HaCaT cells (<b>F</b>) but not in HUVECs (<b>H</b>). HUVECs were more susceptible to irradiation-induced double-strand breaks than HaCaT cells. Values represent the mean of three independent experiments, with error bars indicating the standard error of the mean (s.e.m.). <span class="html-italic">p</span> values &lt; 0.05 were deemed significant (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>A cytokine array was used to quantify radiation-induced release of pro-inflammatory cytokines. A Cytokine array was performed with supernatants from HaCaT cells (<b>A</b>–<b>C</b>) and HUVECs (<b>D</b>–<b>F</b>) treated with HT or TMP 24 h before (<b>B</b>,<b>E</b>) and during (<b>C</b>,<b>F</b>) irradiation with 2 Gy (<span class="html-italic">n</span> = 1). The supernatants were collected 48 h after irradiation. Chemiluminescence was used to measure and quantify cytokine binding to the membrane. Intensity was evaluated with the help of ImageJ software. Eighty different cytokines were tested, and cytokines with relevant changes are shown in (<b>B</b>,<b>C</b>,<b>E</b>,<b>F</b>).</p>
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<p>Antioxidants modulate the mRNA levels of different pro-inflammatory cytokines in HaCaT cells and HUVEC cultures. A heatmap of gene expression changes shown by the qRT-PCR of pro-inflammatory cytokines for HaCaT cells and HUVECs after treatment with HT and TMP and irradiation. All experiments were repeated at least three times in triplicate.</p>
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<p>Colony formation assay of HaCaT cells after treatment with HT and TMP 24 h before and during irradiation, respectively. (<b>A</b>–<b>D</b>): Treatment with antioxidants 24 h before irradiation leads to a significantly higher plating efficiency compared to untreated controls at 0 Gy, indicating the higher proliferative potential of the treated cells (<b>A</b>). Untreated cells show changes in morphology after irradiation with 2, 4, and 6 Gy, while HT- and TMP-treated cells each maintain the original morphology with decreased post-irradiation changes (<b>B</b>). Incubation with HT results in slight but significant radioresistance for a dose of 4 Gy (<b>C</b>). TMP treatment has no effect on radioresistance (<b>D</b>). (<b>E</b>–<b>H</b>) show treatment with antioxidants during irradiation. The use of HT or TMP during irradiation has no substantial effect on the plating efficiency of HaCaT at 0 Gy (<b>E</b>). We observed the same changes in the morphology of control cells after irradiation, while antioxidant-treated cells largely preserved the original morphology (<b>F</b>). Both antioxidants enhance the radioresistance of cells. The effects increase with higher irradiation doses (<b>G</b>,<b>H</b>). All experiments were repeated at least three times in triplicate. <span class="html-italic">p</span> values &lt; 0.05 were deemed significant (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; error bars indicate the standard error of the mean (s.e.m.)).</p>
Full article ">Figure 6 Cont.
<p>Colony formation assay of HaCaT cells after treatment with HT and TMP 24 h before and during irradiation, respectively. (<b>A</b>–<b>D</b>): Treatment with antioxidants 24 h before irradiation leads to a significantly higher plating efficiency compared to untreated controls at 0 Gy, indicating the higher proliferative potential of the treated cells (<b>A</b>). Untreated cells show changes in morphology after irradiation with 2, 4, and 6 Gy, while HT- and TMP-treated cells each maintain the original morphology with decreased post-irradiation changes (<b>B</b>). Incubation with HT results in slight but significant radioresistance for a dose of 4 Gy (<b>C</b>). TMP treatment has no effect on radioresistance (<b>D</b>). (<b>E</b>–<b>H</b>) show treatment with antioxidants during irradiation. The use of HT or TMP during irradiation has no substantial effect on the plating efficiency of HaCaT at 0 Gy (<b>E</b>). We observed the same changes in the morphology of control cells after irradiation, while antioxidant-treated cells largely preserved the original morphology (<b>F</b>). Both antioxidants enhance the radioresistance of cells. The effects increase with higher irradiation doses (<b>G</b>,<b>H</b>). All experiments were repeated at least three times in triplicate. <span class="html-italic">p</span> values &lt; 0.05 were deemed significant (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; error bars indicate the standard error of the mean (s.e.m.)).</p>
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<p>Colony formation assay of HUVECs after treatment with HT and TMP 24 h before and during irradiation, respectively. (<b>A</b>–<b>C</b>): Treatment with antioxidants 24 h before irradiation leads to no changes in plating efficiency compared to untreated controls at 0 Gy (<b>A</b>). Incubation with HT and TMP results in slight but significant radioresistance compared to similarly irradiated but otherwise untreated controls (<b>B</b>,<b>C</b>). (<b>D</b>–<b>F</b>): Treatment with antioxidants during irradiation. The use of HT or TMP during irradiation has no substantial effect on the plating efficiency of HUVECs at 0 Gy (<b>D</b>). Both antioxidants enhance the radioresistance of cells. The effects increase with higher irradiation doses (<b>E</b>,<b>F</b>). All experiments were repeated at least three times in triplicate. <span class="html-italic">p</span> values &lt; 0.05 were deemed significant (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; error bars indicate the standard error of the mean (s.e.m.)).</p>
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<p>Angiogenic potential of HUVECs after radiation treatment. The angiogenesis of irradiated HUVECs was measured in cells treated with HT and TMP, respectively, 24 h before (<b>A</b>,<b>B</b>) and during irradiation (<b>C</b>,<b>D</b>) with 2 Gy. Single branches are shown in green, master junctions in red, master segments in yellow and vessel network is displayed in teal (<b>A</b>,<b>C</b>). Pictures for evaluation were taken 3 h after seeding and irradiation. Radiation treatment reduces the angiogenic potential of HUVECs. Angiogenesis was increased after the administration of antioxidants, regardless of the time of antioxidant treatment. All experiments were repeated at least three times in quintuplicate. <span class="html-italic">p</span> values &lt; 0.05 were deemed significant (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; error bars indicate standard error of the mean (s.e.m.)).</p>
Full article ">Figure 8 Cont.
<p>Angiogenic potential of HUVECs after radiation treatment. The angiogenesis of irradiated HUVECs was measured in cells treated with HT and TMP, respectively, 24 h before (<b>A</b>,<b>B</b>) and during irradiation (<b>C</b>,<b>D</b>) with 2 Gy. Single branches are shown in green, master junctions in red, master segments in yellow and vessel network is displayed in teal (<b>A</b>,<b>C</b>). Pictures for evaluation were taken 3 h after seeding and irradiation. Radiation treatment reduces the angiogenic potential of HUVECs. Angiogenesis was increased after the administration of antioxidants, regardless of the time of antioxidant treatment. All experiments were repeated at least three times in quintuplicate. <span class="html-italic">p</span> values &lt; 0.05 were deemed significant (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001; error bars indicate standard error of the mean (s.e.m.)).</p>
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<p>Wound healing, proliferation, and migration of irradiated HaCaT cells. Wound healing, migration, and dry mass measurements after the treatment of HaCaT with HT or TMP 24 h before (<b>A</b>–<b>E</b>) and during irradiation (<b>F</b>–<b>J</b>) with 2 Gy. (<b>A</b>,<b>B</b>) Wound closure after radiation treatment was accelerated in HT- and TMP-treated HaCaT cells with treatment 24 h before irradiation. (<b>C</b>,<b>E</b>) Cell speed, distance traveled over 24 h, and dry mass increment 24 h after irradiation in ng per field of view as a readout for proliferation were also increased. (<b>F</b>,<b>G</b>) Wound closure was also improved in cells treated with TMP immediately before irradiation. (<b>H</b>,<b>J</b>) Cell speed, distance traveled over 24 h, and dry mass increment 24 h after irradiation in ng per field of view also tended to be increased. All experiments were repeated at least three times in triplicate. <span class="html-italic">p</span> values &lt; 0.05 were deemed significant (* <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001; error bars indicate the standard error of the mean (s.e.m.)).</p>
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13 pages, 16935 KiB  
Article
Improvement of Fresnel Diffraction Convolution Algorithm
by Cong Ge, Qinghe Song, Weinan Caiyang, Jinbin Gui, Junchang Li, Xiaofan Qian, Qian Li and Haining Dang
Appl. Sci. 2024, 14(9), 3632; https://doi.org/10.3390/app14093632 - 25 Apr 2024
Cited by 1 | Viewed by 1065
Abstract
With the development of digital holography, the accuracy requirements for the reconstruction phase are becoming increasingly high. The transfer function of the double fast transform (D-FFT) algorithm is distorted when the diffraction distance is larger than the criterion distance dt, which [...] Read more.
With the development of digital holography, the accuracy requirements for the reconstruction phase are becoming increasingly high. The transfer function of the double fast transform (D-FFT) algorithm is distorted when the diffraction distance is larger than the criterion distance dt, which reduces the accuracy of solving the phase. In this paper, the Fresnel diffraction integration algorithm is improved by using the low-pass Tukey window to obtain more accurate reconstructed phases. The improved algorithm is called the D-FFT (Tukey) algorithm. The D-FFT (Tukey) algorithm adjusts the degree of edge smoothing of the Tukey window, using the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) to remove the ringing effect and obtain a more accurate reconstructed phase. In a simulation of USAF1951, the longitudinal resolution of the reconstructed phase obtained by D-FFT (Tukey) reached 1.5 μm, which was lower than the 3 μm obtained by the T-FFT algorithm. The results of Fresnel holography experiments on lung cancer cell slices also demonstrated that the phase quality obtained by the D-FFT (Tukey) algorithm was superior to that of the T-FFT algorithm. D-FFT (Tukey) algorithm has potential applications in phase correction, structured illumination digital holographic microscopy, and microscopic digital holography. Full article
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<p>(<b>A</b>) Tukey windows for different β values; (<b>B</b>) phase of <math display="inline"><semantics> <mrow> <mi mathvariant="normal">H</mi> <mo stretchy="false">(</mo> <mi mathvariant="normal">u</mi> <mo>,</mo> <mi mathvariant="normal">v</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> before improvement; (<b>C</b>) phase of <math display="inline"><semantics> <mrow> <mi mathvariant="normal">H</mi> <mo stretchy="false">(</mo> <mi mathvariant="normal">u</mi> <mo>,</mo> <mi mathvariant="normal">v</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> after improvement.</p>
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<p>Intensity profiles through center of Young’s double−hole experimental fringes obtained by (<b>A</b>) D-FFT; (<b>B</b>) T-FFT; (<b>C</b>) D-FFT (rect); and (<b>D</b>) D-FFT (Tukey) (<math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">β</mi> <mo>=</mo> <mn>0.2</mn> </mrow> </semantics></math>).</p>
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<p>Intensity profiles through center of Young’s double-hole experiment fringes obtained by D-FFT (Tukey): (<b>A</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">β</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>; (<b>B</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">β</mi> <mo>=</mo> <mn>0.3</mn> </mrow> </semantics></math>; (<b>C</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">β</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>; (<b>D</b>) <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">β</mi> <mo>=</mo> <mn>0.7</mn> </mrow> </semantics></math>.</p>
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<p>Different algorithms for USAF1951’s reconstruction results of the PSNR curves and SSIM curves: (<b>A</b>) PSNR curves; (<b>B</b>) SSIM curves.</p>
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<p>Intensity and phase of reconstruction images obtained by (<b>A</b>) D-FFT (Tukey) (<math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">β</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>) and (<b>B</b>) T-FFT.</p>
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<p>Profiles of normalized phase obtained by D-FFT (Tukey) (<math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">β</mi> <mo>=</mo> <mn>0.9</mn> </mrow> </semantics></math>) and T-FFT: (<b>A</b>) located in group 7, element 2 (in the blue box in <a href="#applsci-14-03632-f005" class="html-fig">Figure 5</a>); (<b>B</b>) located in the rectangular window of group 7 (in the red box in <a href="#applsci-14-03632-f005" class="html-fig">Figure 5</a>).</p>
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<p>Light path diagram of holographic microscopy experiment on lung cancer cells.</p>
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<p>Different algorithms for lung cancer slice’s reconstruction results of the PSNR curves and SSIM curves: (<b>A</b>) PSNR curves; (<b>B</b>) SSIM curves.</p>
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<p>Intensity of reconstruction images obtained by different algorithms: (<b>A</b>) D-FFT; (<b>B</b>) D-FFT (rect); (<b>C</b>) T-FFT; (<b>D</b>) Tukey (<math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">β</mi> <mo>=</mo> <mn>0.81</mn> </mrow> </semantics></math>).</p>
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<p>Profiles of reconstructed intensity obtained by different algorithms: (<b>A</b>) intensity profile plots (dashed line in yellow box); (<b>B</b>) intensity profile plots (solid line in red box).</p>
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<p>Phases reconstructed by (<b>A</b>) D-FFT; (<b>B</b>) D-FFT (rect); (<b>C</b>) T-FFT; and (<b>D</b>) D-FFT (Tukey) (<math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">β</mi> <mo>=</mo> <mn>0.81</mn> </mrow> </semantics></math>).</p>
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15 pages, 8445 KiB  
Article
Improving the Signal-to-Noise Ratio of Axial Displacement Measurements of Microspheres Based on Compound Digital Holography Microscopy Combined with the Reconstruction Centering Method
by Yanan Zeng, Qihang Guo, Xiaodong Hu, Junsheng Lu, Xiaopan Fan, Haiyun Wu, Xiao Xu, Jun Xie and Rui Ma
Sensors 2024, 24(9), 2723; https://doi.org/10.3390/s24092723 - 24 Apr 2024
Cited by 1 | Viewed by 1370
Abstract
In 3D microsphere tracking, unlike in-plane motion that can be measured directly by a microscope, axial displacements are resolved by optical interference or a diffraction model. As a result, the axial results are affected by the environmental noise. The immunity to environmental noise [...] Read more.
In 3D microsphere tracking, unlike in-plane motion that can be measured directly by a microscope, axial displacements are resolved by optical interference or a diffraction model. As a result, the axial results are affected by the environmental noise. The immunity to environmental noise increases with measurement accuracy and the signal-to-noise ratio (SNR). In compound digital holography microscopy (CDHM)-based measurements, precise identification of the tracking marker is critical to ensuring measurement precision. The reconstruction centering method (RCM) was proposed to suppress the drawbacks caused by installation errors and, at the same time, improve the correct identification of the tracking marker. The reconstructed center is considered to be the center of the microsphere, rather than the center of imaging in conventional digital holographic microscopy. This method was verified by simulation of rays tracing through microspheres and axial moving experiments. The axial displacements of silica microspheres with diameters of 5 μm and 10 μm were tested by CDHM in combination with the RCM. As a result, the SNR of the proposed method was improved by around 30%. In addition, the method was successfully applied to axial displacement measurements of overlapped microspheres with a resolution of 2 nm. Full article
(This article belongs to the Special Issue Digital Holography in Optics: Techniques and Applications)
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<p>The beams transmitted through the microsphere.</p>
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<p>The marginal rays and central ray of the <span class="html-italic">PO<sub>i</sub>O<sub>i</sub></span>′ plane in <a href="#sensors-24-02723-f001" class="html-fig">Figure 1</a>.</p>
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<p>The rays tracing in sample microsphere.</p>
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<p>The RPOPL difference simulation of the reconstruction center pixel and the imaging center pixel. (<b>a</b>) RPOPL differences between various rings (ring 4, 5, 6, respectively) and reconstruction center pixel; (<b>b</b>) RPOPL differences between various rings (ring 4, 5, 6, respectively) and imaging center pixel; (<b>c</b>) second derivative of the RPOPL difference between the 4th, 5th, and 6th ring pixels and reconstruction center pixel; (<b>d</b>) second derivative of the RPOPL difference between the 4th, 5th and 6th ring pixels and imaging center pixel.</p>
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<p>The RPOPL difference simulation of point sources at different locations (<b>a</b>) The RPOPL differences of the reconstruction center pixel; (<b>b</b>) second derivative of the RPOPL difference between the 6<sup>th</sup> ring pixels and the reconstruction center pixel.</p>
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<p>Diagram of CDHM combined with the RCM.</p>
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<p>Experimental setup to measure displacement of microsphere movement.</p>
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<p>Off-axis digital hologram of an out-of-focus microsphere.</p>
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<p>Hologram (the interference fringes are removed) and the reconstructed phase of the microsphere.</p>
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<p>Reconstructed intensity of the hologram processed by IFRM.</p>
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<p>Displacement along the optical axis of an out-of-focus microsphere.</p>
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<p>Off-axis hologram of a nearly-in-focus microsphere. (<b>a</b>) Digital hologram; (<b>b</b>) hologram without the interference fringes; (<b>c</b>) phase of microsphere.</p>
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<p>Second derivative of RPOPL difference between 6th ring pixels. (<b>a</b>) Reconstructed center pixel; (<b>b</b>) imaging center pixel for frame 120 and frame 170.</p>
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<p>Displacement along the optical axis of a nearly-in-focus microsphere.</p>
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<p>Digital hologram of overlapped microspheres. (<b>a</b>) Digital hologram; (<b>b</b>) reconstructed intensity.</p>
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<p>Displacement along the optical axis of overlapped microspheres.</p>
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19 pages, 4088 KiB  
Article
Quantitative Phase Imaging as Sensitive Screening Method for Nanoparticle-Induced Cytotoxicity Assessment
by Anne Marzi, Kai Moritz Eder, Álvaro Barroso, Björn Kemper and Jürgen Schnekenburger
Cells 2024, 13(8), 697; https://doi.org/10.3390/cells13080697 - 17 Apr 2024
Viewed by 1333
Abstract
The assessment of nanoparticle cytotoxicity is challenging due to the lack of customized and standardized guidelines for nanoparticle testing. Nanoparticles, with their unique properties, can interfere with biochemical test methods, so multiple tests are required to fully assess their cellular effects. For a [...] Read more.
The assessment of nanoparticle cytotoxicity is challenging due to the lack of customized and standardized guidelines for nanoparticle testing. Nanoparticles, with their unique properties, can interfere with biochemical test methods, so multiple tests are required to fully assess their cellular effects. For a more reliable and comprehensive assessment, it is therefore imperative to include methods in nanoparticle testing routines that are not affected by particles and allow for the efficient integration of additional molecular techniques into the workflow. Digital holographic microscopy (DHM), an interferometric variant of quantitative phase imaging (QPI), has been demonstrated as a promising method for the label-free assessment of the cytotoxic potential of nanoparticles. Due to minimal interactions with the sample, DHM allows for further downstream analyses. In this study, we investigated the capabilities of DHM in a multimodal approach to assess cytotoxicity by directly comparing DHM-detected effects on the same cell population with two downstream biochemical assays. Therefore, the dry mass increase in RAW 264.7 macrophages and NIH-3T3 fibroblast populations measured by quantitative DHM phase contrast after incubation with poly(alkyl cyanoacrylate) nanoparticles for 24 h was compared to the cytotoxic control digitonin, and cell culture medium control. Viability was then determined using a metabolic activity assay (WST-8). Moreover, to determine cell death, supernatants were analyzed for the release of the enzyme lactate dehydrogenase (LDH assay). In a comparative analysis, in which the average half-maximal effective concentration (EC50) of the nanocarriers on the cells was determined, DHM was more sensitive to the effect of the nanoparticles on the used cell lines compared to the biochemical assays. Full article
(This article belongs to the Special Issue Research Advances in Cell Methods)
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<p>Experimental design and workflow for comparison of PACA nanoparticle in vitro cytotoxicity assessment by DHM with downstream WST-8 and LDH assays. (<b>A</b>) Seeding of NIH-3T3 and RAW 264.7 cells into 96-well plates. (<b>B</b>) Incubation of cells with PACA, cbz-loaded PACA nanoparticles and controls. (<b>C</b>) Label-free DHM QPI proliferation assay. (<b>D</b>) WST-8 cell viability assay. (<b>E</b>) LDH cell death assay. (<b>F</b>) Determination of EC<sub>50</sub> values.</p>
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<p>DHM QPI images of RAW 264.7 macrophages and NIH-3T3 fibroblasts incubated with unloaded PACA nanoparticles in five representatively selected concentrations (0.2, 2, 8, 32 and 256 µg/mL) vs. cell culture medium controls (0 µg/mL) at time points t = 0 and t = 24 h. For both cell lines, viable proliferated cells were observed after incubation with cell culture medium control and 0.2 and 2 µg/mL of unloaded PACA nanoparticles. RAW 264.7 cells with 8 µg/mL showed cell debris at t = 0, and after 24 h; NIH-3T3 cells showed cell detachment at t = 0 and proliferated cells after 24 h. For 32 and 256 µg/mL of unloaded PACA nanoparticles, cell debris was observed for RAW 264.7 macrophages after 24 h, and proliferated cells, detached cells and cell debris were observed for NIH-3T3 with 32 µg/mL. Corresponding bright-field images (<a href="#app1-cells-13-00697" class="html-app">Figure S1</a>) and enlarged areas of DHM QPI and bright-field images (<a href="#app1-cells-13-00697" class="html-app">Figure S2</a>), which allow for a more detailed investigation of the cellular morphology alterations, are provided in the <a href="#app1-cells-13-00697" class="html-app">Supplementary Materials</a>.</p>
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<p>DHM QPI images of RAW 264.7 macrophages and NIH-3T3 fibroblasts after incubation with cbz-loaded PACA nanoparticles in five representatively selected concentrations (0.002, 0.2, 8, 32 and 256 µg/mL) vs. cell culture medium controls (0 µg/mL) at time points t = 0 and t = 24 h. For both cell lines, after incubation with cell culture medium control and 0.002 µg/mL of cbz-loaded PACA nanoparticles, viable cells were detected after 24 h. For 0.2 µg/mL, cell debris could be observed for RAW 264.7, and detached and swollen cells could be observed for NIH-3T3. Macrophages incubated with 8 µg/mL of cbz-loaded PACA showed a swollen but viable cell morphology after 24 h, and for NIH-3T3, detached cells and cell debris were visible at t = 0 24 h, but after 24 h, proliferated cells were visible. Cell debris was observed in both cell lines with 32 and 256 µg/mL of cbz-loaded nanoparticles after 24 h. Corresponding bright-field images (<a href="#app1-cells-13-00697" class="html-app">Figure S3</a>) and enlarged areas of DHM QPI and bright-field images (<a href="#app1-cells-13-00697" class="html-app">Figure S4</a>), which allow for a more detailed investigation of the cellular morphology alterations, are provided in the <a href="#app1-cells-13-00697" class="html-app">Supplementary Materials</a>.</p>
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<p>Dose–response relationship for unloaded PACA and cbz-loaded PACA nanoparticles on cell proliferation (DHM, green, (<b>A</b>) unloaded (<b>D</b>) cbz-loaded PACA), viability (WST-8, gray, (<b>B</b>) unloaded (<b>E</b>) cbz loaded PACA) and death (LDH, red, (<b>C</b>) unloaded (<b>F</b>) cbz loaded PACA) of RAW 264.7 macrophages and NIH-3T3 fibroblasts. Mouse RAW 264.7 macrophages and NIH-3T3 fibroblasts were seeded in 96-well plates incubated with unloaded and cbz-loaded PACA, and dry mass increments of cell populations were analyzed with DHM. Subsequently, the viability of the same cell populations was determined with a WST-8 metabolic activity assay, and the supernatants were analyzed in parallel for the release of LDH to detect cell death. The mean values ± SD from three independent experiments are shown (<span class="html-italic">n</span> = 3). Significance levels were given as <span class="html-italic">p</span> &lt; 0.001 (***), <span class="html-italic">p</span> &lt; 0.01 (**) and <span class="html-italic">p</span> &lt; 0.05 (*).</p>
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3 pages, 2560 KiB  
Abstract
A Comprehensive Characterization Procedure for Resonant MEMS Scanning Mirrors
by Clement Fleury, Markus Bainschab, Gianluca Mendicino, Roberto Carminati, Pooja Thakkar, Dominik Holzmann, Sara Guerreiro and Adrien Piot
Proceedings 2024, 97(1), 144; https://doi.org/10.3390/proceedings2024097144 - 3 Apr 2024
Viewed by 3176
Abstract
We demonstrate an experimental assessment of a high-Q, high-angle piezoelectric (2 µm PZT) MEMS scanning micromirror featuring distributed backside reinforcement, suitable for applications demanding energy-efficient and high-quality image projection. Frequency response measurements at 10 different vacuum levels ranging from atmospheric pressure to 10 [...] Read more.
We demonstrate an experimental assessment of a high-Q, high-angle piezoelectric (2 µm PZT) MEMS scanning micromirror featuring distributed backside reinforcement, suitable for applications demanding energy-efficient and high-quality image projection. Frequency response measurements at 10 different vacuum levels ranging from atmospheric pressure to 10−6 mbar allow for the quantitative separation of damping mechanisms (air and structural). Stroboscopic digital holographic microscopy was used to assess the static and dynamic deformation of the mirror surface. The experimental results are in good agreement with simulations and models. Full article
(This article belongs to the Proceedings of XXXV EUROSENSORS Conference)
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<p>(<b>a</b>) Measured Q factor vs. pressure, (<b>b</b>) 1/Q vs. optical amplitude in the vacuum regime.</p>
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<p>Measured static (<b>a</b>), measured total (<b>b</b>), and simulated dynamic (<b>c</b>) mirror deformation at 14° mech. angle (56° opt.).</p>
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15 pages, 13016 KiB  
Article
Image Processing Techniques for Improving Quality of 3D Profile in Digital Holographic Microscopy Using Deep Learning Algorithm
by Hyun-Woo Kim, Myungjin Cho and Min-Chul Lee
Sensors 2024, 24(6), 1950; https://doi.org/10.3390/s24061950 - 19 Mar 2024
Cited by 5 | Viewed by 1392
Abstract
Digital Holographic Microscopy (DHM) is a 3D imaging technology widely applied in biology, microelectronics, and medical research. However, the noise generated during the 3D imaging process can affect the accuracy of medical diagnoses. To solve this problem, we proposed several frequency domain filtering [...] Read more.
Digital Holographic Microscopy (DHM) is a 3D imaging technology widely applied in biology, microelectronics, and medical research. However, the noise generated during the 3D imaging process can affect the accuracy of medical diagnoses. To solve this problem, we proposed several frequency domain filtering algorithms. However, the filtering algorithms we proposed have a limitation in that they can only be applied when the distance between the direct current (DC) spectrum and sidebands are sufficiently far. To address these limitations, among the proposed filtering algorithms, the HiVA algorithm and deep learning algorithm, which effectively filter by distinguishing between noise and detailed information of the object, are used to enable filtering regardless of the distance between the DC spectrum and sidebands. In this paper, a combination of deep learning technology and traditional image processing methods is proposed, aiming to reduce noise in 3D profile imaging using the Improved Denoising Diffusion Probabilistic Models (IDDPM) algorithm. Full article
(This article belongs to the Special Issue Digital Holography Imaging Techniques and Applications Using Sensors)
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<p>Wavefront scattering in the specimen. (<b>a</b>) the wavefront of the reference image and (<b>b</b>) the wavefront of the object image, where <math display="inline"><semantics> <msub> <mi>n</mi> <mi>a</mi> </msub> </semantics></math>: refractive index of air, <math display="inline"><semantics> <msub> <mi>n</mi> <mi>M</mi> </msub> </semantics></math>: refractive index of the surrounding medium, <math display="inline"><semantics> <msub> <mi>n</mi> <mi>S</mi> </msub> </semantics></math>: refractive index of the object.</p>
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<p>Acquired object and reference holograms of the red blood cells (RBCs).</p>
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<p>Fourier shift process of DHM. (<b>a</b>) Fourier domain of the recorded hologram and (<b>b</b>) the windowed sideband from (<b>a</b>), where <math display="inline"><semantics> <mi mathvariant="italic">H</mi> </semantics></math>: horizontal resolution, <math display="inline"><semantics> <mi mathvariant="italic">V</mi> </semantics></math>: vertical resolution.</p>
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<p>Phase information of (<b>a</b>) the reference image and (<b>b</b>) the object image after inverse Fourier transform.</p>
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<p>Phase difference.</p>
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<p>3D profile of the red blood cell.</p>
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<p>Concept of the Diffusion Model.</p>
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<p>Frequency domain that (<b>a</b>) can be filtered with HiVA (<b>b</b>) cannot be filtered with HiVA.</p>
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<p>Concept of the proposed method. (<b>a</b>) The original 3D profile and (<b>b</b>) the filtered 3D profile by HiVA algorithm.</p>
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<p>Experimental setup. (<span class="html-italic">L</span> : Lens, <span class="html-italic">P</span>: pinhole, <span class="html-italic">M</span>: mirror, <math display="inline"><semantics> <mrow> <mi>B</mi> <mi>S</mi> </mrow> </semantics></math>: beam splitter, and <math display="inline"><semantics> <mrow> <mi>O</mi> <mi>L</mi> </mrow> </semantics></math>: objective lens).</p>
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<p>Comparison of filtering results. (<b>a</b>) Unfiltered image, (<b>b</b>) Gaussian filtering (<math display="inline"><semantics> <mrow> <mi>σ</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>), and (<b>c</b>) HiVA filtering.</p>
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<p>Reconstructed microsphere 3D profile of (<b>a</b>) the unfiltered image, (<b>b</b>) images filtered with the Gaussian method and (<b>c</b>) images filtered with the proposed method.</p>
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<p>Peak signal-to-noise ratio (PSNR) value of the reconstructed 3D profiles of the unfiltered image, the Gaussian-filtered image, and the image filtered by our proposed method.</p>
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<p>Structural similarity (SSIM) value of the reconstructed 3D profiles of the unfiltered image, the Gaussian-filtered image, and the image filtered by our proposed method.</p>
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12 pages, 3293 KiB  
Article
Live Cell Imaging by Single-Shot Common-Path Wide Field-of-View Reflective Digital Holographic Microscope
by Manoj Kumar, Takashi Murata and Osamu Matoba
Sensors 2024, 24(3), 720; https://doi.org/10.3390/s24030720 - 23 Jan 2024
Cited by 7 | Viewed by 1634
Abstract
Quantitative phase imaging by digital holographic microscopy (DHM) is a nondestructive and label-free technique that has been playing an indispensable role in the fields of science, technology, and biomedical imaging. The technique is competent in imaging and analyzing label-free living cells and investigating [...] Read more.
Quantitative phase imaging by digital holographic microscopy (DHM) is a nondestructive and label-free technique that has been playing an indispensable role in the fields of science, technology, and biomedical imaging. The technique is competent in imaging and analyzing label-free living cells and investigating reflective surfaces. Herein, we introduce a new configuration of a wide field-of-view single-shot common-path off-axis reflective DHM for the quantitative phase imaging of biological cells that leverages several advantages, including being less-vibration sensitive to external perturbations due to its common-path configuration, also being compact in size, simple in optical design, highly stable, and cost-effective. A detailed description of the proposed DHM system, including its optical design, working principle, and capability for phase imaging, is presented. The applications of the proposed system are demonstrated through quantitative phase imaging results obtained from the reflective surface (USAF resolution test target) as well as transparent samples (living plant cells). The proposed system could find its applications in the investigation of several biological specimens and the optical metrology of micro-surfaces. Full article
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<p>(<b>a</b>) Schematic of the experimental setup of common-path reflective DHM. (<b>b</b>) Scheme for the preparation of (semi) transparent specimens.</p>
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<p>Temporal stability of the proposed setup. Histogram of the standard deviation of the reconstructed phase distributions for a defined spatial location. The red fitting curve appears to be approximately Gaussian type.</p>
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<p>Experimental results of the negative USAF resolution test target: (<b>a</b>) recorded digital hologram; (<b>b</b>) Fourier transform of (<b>a</b>); (<b>c</b>) retrieved intensity; (<b>d</b>) 2D phase; (<b>e</b>) pseudo-3D phase map.</p>
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<p>Experimental results of the micro-lenslet array: (<b>a</b>) retrieved wrapped phase, (<b>b</b>) unwrapped phase, (<b>c</b>) pseudo-3D unwrapped phase, and (<b>d</b>) 3D thickness profile. <span class="html-italic">x-</span> and <span class="html-italic">y</span>-axes: 1200 pixels × 1200 pixels.</p>
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<p>Experimental results of tobacco plant cells at different instants of time: (<b>a</b>,<b>a1</b>) at t = 0 min, (<b>b</b>,<b>b1</b>) at t = 50 min, (<b>c</b>,<b>c1</b>) at t = 100 min, (<b>d</b>,<b>d1</b>) at t = 150 min, and (<b>e</b>,<b>e1</b>) at t = 200 min.</p>
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13 pages, 4751 KiB  
Article
Flexible Measurement of High-Slope Micro-Nano Structures with Tilted Wave Digital Holographic Microscopy
by Xinyang Ma, Rui Xiong, Wei Wang and Xiangchao Zhang
Sensors 2023, 23(23), 9526; https://doi.org/10.3390/s23239526 - 30 Nov 2023
Cited by 2 | Viewed by 1091
Abstract
Digital holographic microscopy is an important measurement method for micro-nano structures. However, when the structured features are of high-slopes, the interference fringes can become too dense to be recognized. Due to the Nyquist’s sampling limit, reliable wavefront restoration and phase unwrapping are not [...] Read more.
Digital holographic microscopy is an important measurement method for micro-nano structures. However, when the structured features are of high-slopes, the interference fringes can become too dense to be recognized. Due to the Nyquist’s sampling limit, reliable wavefront restoration and phase unwrapping are not feasible. To address this problem, the interference fringes are proposed to be sparsified by tilting the reference wavefronts. A data fusion strategy including region extraction and tilt correction is developed for reconstructing the full-area surface topographies. Experimental results of high-slope elements demonstrate the validity and reliability of the proposed method. Full article
(This article belongs to the Special Issue Precision Optical Metrology and Smart Sensing)
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<p>Schematic of the principle of simultaneous phase shifting.</p>
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<p>Set-up of digital holographic microscope.</p>
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<p>Effect of discretization on reconstruction error: (<b>a</b>–<b>c</b>) height error maps associated with different maximum terrain inclination angles; (<b>d</b>) quantitative relationship.</p>
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<p>Flowchart of the proposed method for measuring high-slope objects.</p>
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<p>Wrapped phase maps for the convex mirror: (<b>a</b>–<b>d</b>) phase maps associated with different tilt angles.</p>
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<p>Procedure of region extraction: (<b>a</b>) gradient map of complex amplitude; (<b>b</b>) binarization map; (<b>c</b>) result of region extraction.</p>
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<p>Comparison of the measured results: (<b>a</b>) reconstructed topography; (<b>b</b>) profile of proposed method; (<b>c</b>) profile of <a href="#sensors-23-09526-f005" class="html-fig">Figure 5</a>a.</p>
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<p>Wrapped phase maps for the concave surface: (<b>a</b>–<b>d</b>) phase maps associated with different tilt angles.</p>
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<p>Unwrapped phase maps: (<b>a</b>–<b>d</b>) phase maps associated with different tilt angles.</p>
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<p>Reconstruction results for the concave surface: (<b>a</b>) fused topography; (<b>b</b>) profile of proposed method; (<b>c</b>) profile of <a href="#sensors-23-09526-f008" class="html-fig">Figure 8</a>a.</p>
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<p>Uncertainty analysis of measurements: (<b>a</b>) simulated rotationally symmetric paraboloid; (<b>b</b>) the probability density function of ∆z.</p>
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12 pages, 10570 KiB  
Article
Parallel Phase-Shifting Digital Holographic Phase Imaging of Micro-Optical Elements with a Polarization Camera
by Bingcai Liu, Xinmeng Fang, Ailing Tian, Siqi Wang, Ruixuan Zhang, Hongjun Wang and Xueliang Zhu
Photonics 2023, 10(12), 1291; https://doi.org/10.3390/photonics10121291 - 23 Nov 2023
Cited by 3 | Viewed by 1770
Abstract
In this paper, we propose a measurement method of micro-optical elements with parallel phase-shifting digital holographic phase imaging. This method can record four phase-shifting holograms with a phase difference of π/2 in a single shot and correct the pixel mismatch error of the [...] Read more.
In this paper, we propose a measurement method of micro-optical elements with parallel phase-shifting digital holographic phase imaging. This method can record four phase-shifting holograms with a phase difference of π/2 in a single shot and correct the pixel mismatch error of the polarization camera using a bilinear interpolation algorithm, thereby producing high-resolution four-step phase-shifting holograms. This method reconstructs the real phase information of the object to be measured through a four-step phase-shifting algorithm. The reproduced image eliminates the interference of zero-order images and conjugate images, overcoming the problem that traditional phase-shifting digital holography cannot be measured in real time. A simulation analysis showed that the relative error of this measurement method could reach 0.0051%. The accurate surface topography information of the object was reconstructed from an experimental measurement through a microlens array. Multiple measurements yielded a mean absolute error and a mean relative error for the vertical height of the microlens array down to 5.9500 nm and 0.0461%, respectively. Full article
(This article belongs to the Special Issue Photodetector Materials and Optoelectronic Devices)
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<p>Schematic diagram of the optical path of the parallel phase−shifting digital holography experiment.</p>
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<p>Process flow of polarization phase−shift simulation.</p>
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<p>Phase object simulation processing flow for microlens array shapes. (<b>a</b>) Original phase; (<b>b</b>) four phase−shifting holograms; (<b>c</b>) polarization image; (<b>d</b>) low−resolution holograms; (<b>e</b>) high−resolution holograms; (<b>f</b>) reconstruction phase; (<b>g</b>) residuals.</p>
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<p>Selection of phase object cross-sections of the shape of a single microlens.</p>
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<p>Picture of the parallel phase−shifting digital holography experimental apparatus.</p>
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<p>Original polarization image and four−step phase−shifting holograms.</p>
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<p>Phase processing results of microlens array. (<b>a</b>) Wrapping phase; (<b>b</b>) unwrapping phase; (<b>c</b>) 2−D phase; (<b>d</b>) 3−D phase.</p>
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<p>Selection of section lines of a single microlens.</p>
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13 pages, 18520 KiB  
Article
A Novel Image Processing Method for Obtaining an Accurate Three-Dimensional Profile of Red Blood Cells in Digital Holographic Microscopy
by Hyun-Woo Kim, Myungjin Cho and Min-Chul Lee
Biomimetics 2023, 8(8), 563; https://doi.org/10.3390/biomimetics8080563 - 22 Nov 2023
Cited by 6 | Viewed by 1555
Abstract
Recently, research on disease diagnosis using red blood cells (RBCs) has been active due to the advantage that it is possible to diagnose many diseases with a drop of blood in a short time. Representatively, there are disease diagnosis technologies that utilize deep [...] Read more.
Recently, research on disease diagnosis using red blood cells (RBCs) has been active due to the advantage that it is possible to diagnose many diseases with a drop of blood in a short time. Representatively, there are disease diagnosis technologies that utilize deep learning techniques and digital holographic microscope (DHM) techniques. However, three-dimensional (3D) profile obtained by DHM has a problem of random noise caused by the overlapping DC spectrum and sideband in the Fourier domain, which has the probability of misjudging diseases in deep learning technology. To reduce random noise and obtain a more accurate 3D profile, in this paper, we propose a novel image processing method which randomly selects the center of the high-frequency sideband (RaCoHS) in the Fourier domain. This proposed algorithm has the advantage of filtering while using only recorded hologram information to maintain high-frequency information. We compared and analyzed the conventional filtering method and the general image processing method to verify the effectiveness of the proposed method. In addition, the proposed image processing algorithm can be applied to all digital holography technologies including DHM, and in particular, it is expected to have a great effect on the accuracy of disease diagnosis technologies using DHM. Full article
(This article belongs to the Special Issue Biomimetic and Bioinspired Computer Vision and Image Processing)
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<p>Fourier domain of the recorded hologram. (<b>a</b>) 2D and (<b>b</b>) 1D Fourier spectrums.</p>
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<p>Method for randomly selecting the center of the high-frequency sideband in the Fourier domain. (<b>a</b>) Parameters for determining the maximum range of the windowed sideband, (<b>b</b>) the maximum range for windowing the sideband, (<b>c</b>) the maximum range for selecting the center of the windowed sidebands, and (<b>d</b>) the result of selecting 20 pixels randomly after setting the range of pixels.</p>
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<p>Experimental setup. M: mirror, BS: beam splitter, and OL: objective lens.</p>
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<p>Recorded hologram images. (<b>a</b>) Reference and (<b>b</b>) Object images.</p>
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<p>Reconstructed 3D profile obtained by (<b>a</b>) the conventional image processing method, (<b>b</b>) the proposed method (<math display="inline"><semantics> <msub> <mi>N</mi> <mi>R</mi> </msub> </semantics></math> = 20) and (<b>c</b>) Gaussian filtering (<math display="inline"><semantics> <mi>σ</mi> </semantics></math> = 2).</p>
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<p>Reconstructed 3D profiles obtained by the proposed method. (<b>a</b>) <math display="inline"><semantics> <msub> <mi>N</mi> <mi>R</mi> </msub> </semantics></math> = 1, (<b>b</b>) <math display="inline"><semantics> <msub> <mi>N</mi> <mi>R</mi> </msub> </semantics></math> = 5, (<b>c</b>) <math display="inline"><semantics> <msub> <mi>N</mi> <mi>R</mi> </msub> </semantics></math> = 10 and (<b>d</b>) <math display="inline"><semantics> <msub> <mi>N</mi> <mi>R</mi> </msub> </semantics></math> = 20.</p>
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<p>Reconstructed 3D profile using microsphere. (<b>a</b>) The conventional method, (<b>b</b>) the proposed method (<math display="inline"><semantics> <msub> <mi>N</mi> <mi>R</mi> </msub> </semantics></math> = 20), and (<b>c</b>) the ideal comparison model of the microsphere in DHM.</p>
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<p>Result of the numerical analysis. (<b>a</b>) SSIM and (<b>b</b>) MSE.</p>
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15 pages, 10083 KiB  
Article
Aberration Estimation for Synthetic Aperture Digital Holographic Microscope Using Deep Neural Network
by Hosung Jeon, Minwoo Jung, Gunhee Lee and Joonku Hahn
Sensors 2023, 23(22), 9278; https://doi.org/10.3390/s23229278 - 20 Nov 2023
Viewed by 1226
Abstract
Digital holographic microscopy (DHM) is a valuable technique for investigating the optical properties of samples through the measurement of intensity and phase of diffracted beams. However, DHMs are constrained by Lagrange invariance, compromising the spatial bandwidth product (SBP) which relates resolution and field [...] Read more.
Digital holographic microscopy (DHM) is a valuable technique for investigating the optical properties of samples through the measurement of intensity and phase of diffracted beams. However, DHMs are constrained by Lagrange invariance, compromising the spatial bandwidth product (SBP) which relates resolution and field of view. Synthetic aperture DHM (SA-DHM) was introduced to overcome this limitation, but it faces significant challenges such as aberrations in synthesizing the optical information corresponding to the steering angle of incident wave. This paper proposes a novel approach utilizing deep neural networks (DNNs) for compensating aberrations in SA-DHM, extending the compensation scope beyond the numerical aperture (NA) of the objective lens. The method involves training a DNN from diffraction patterns and Zernike coefficients through a circular aperture, enabling effective aberration compensation in the illumination beam. This method makes it possible to estimate aberration coefficients from the only part of the diffracted beam cutoff by the circular aperture mask. With the proposed technique, the simulation results present improved resolution and quality of sample images. The integration of deep neural networks with SA-DHM holds promise for advancing microscopy capabilities and overcoming existing limitations. Full article
(This article belongs to the Special Issue Advanced Optical Sensors Based on Machine Learning)
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<p>The structure of a synthetic aperture digital holographic microscope.</p>
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<p>Implemented synthetic aperture digital holographic microscope.</p>
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<p>Experimental measurements of intensity phase profiles in focal plane and in Fourier domain. The measurements from the illumination beams are (<b>a</b>) <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <msub> <mi>k</mi> <mi>i</mi> </msub> </mrow> <mo stretchy="true">→</mo> </mover> </mrow> </semantics></math> = (122.1, −163.6, 11,808.3) and (<b>b</b>) (187.6, −164.6, 11,807.9).</p>
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<p>Structure of ResNet50 for Zernike coefficients. The ResNet consists of identity blocks and convolution blocks, and a fully connected layer is added to estimate the ten lowest Zernike coefficients.</p>
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<p>Simplified layout of SA-DHM for generating training and validation data.</p>
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<p>Examples of the training data. The intensity patterns on (<b>a</b>) the Fourier domain, (<b>b</b>) the focal plane, and (<b>c</b>) the out-of-focus plane. (<b>d</b>) They are, respectively, occupied in R, G, B channels for training data. The pictures on the first line were numerically generated in the bright field condition and the others were numerically generated in the dark field condition.</p>
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<p>Training history of the loss graph. Red and blue lines represent training loss and validation loss, respectively.</p>
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<p>Estimation errors in nine Zernike coefficients of aberration of illumination beams.</p>
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<p>SA-DHM reconstruction results of synthesized intensity profiles in Fourier domain, reconstruction images in focal plane, and their enlarged images. (<b>a</b>) Ground truth and (<b>b</b>) reconstruction without compensating aberration. Reconstructions with compensating the aberration (<b>c</b>) within and (<b>d</b>) outside the range of training data.</p>
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<p>Resolution analysis of the SA-DHM. (<b>a</b>) Resolution target. (<b>b</b>) MTF chart with and without compensating aberration by using the proposed DNN.</p>
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<p>SA-DHM reconstruction results with the spoke resolution chart, and their enlarged images. (<b>a</b>) Ground truth and (<b>b</b>) reconstruction without compensating aberration. (<b>c</b>) Reconstructions with compensating the aberration.</p>
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<p>Piston phase matching and numerical reconstruction results. (<b>a</b>) Piston phase matching at a specific point in overlapped region. Numerical reconstruction results (<b>b</b>) without and (<b>c</b>) with piston phase matching.</p>
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11 pages, 2525 KiB  
Article
Label-Free Analysis of Urine Samples with In-Flow Digital Holographic Microscopy
by Lucia Gigli, Nicoletta Braidotti, Maria Augusta do R. B. F. Lima, Catalin Dacian Ciubotaru and Dan Cojoc
Biosensors 2023, 13(8), 789; https://doi.org/10.3390/bios13080789 - 4 Aug 2023
Cited by 2 | Viewed by 1397
Abstract
Urinary tract infections are among the most frequent infectious diseases and require screening a great amount of urine samples from patients. However, a high percentage of samples result as negative after urine culture plate tests (CPTs), demanding a simple and fast preliminary technique [...] Read more.
Urinary tract infections are among the most frequent infectious diseases and require screening a great amount of urine samples from patients. However, a high percentage of samples result as negative after urine culture plate tests (CPTs), demanding a simple and fast preliminary technique to screen out the negative samples. We propose a digital holographic microscopy (DHM) method to inspect fresh urine samples flowing in a glass capillary for 3 min, recording holograms at 2 frames per second. After digital reconstruction, bacteria, white and red blood cells, epithelial cells and crystals were identified and counted, and the samples were classified as negative or positive according to clinical cutoff values. Taking the CPT as reference, we processed 180 urine samples and compared the results with those of urine flow cytometry (UFC). Using standard evaluation metrics for our screening test, we found a similar performance for DHM and UFC, indicating DHM as a suitable and fast screening technique retaining several advantages. As a benefit of DHM, the technique is label-free and does not require sample preparation. Moreover, the phase and amplitude images of the cells and other particles present in urine are digitally recorded and can serve for further investigation afterwards. Full article
(This article belongs to the Special Issue Advanced Optical Sensing Techniques for Applications in Biomedicine)
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<p>DHM Setup: laser beam (red) is split in two and recombined by two cube beam splitters, which are being directed to the CMOS; the urine is flowing in the capillary and imaged by the objective lens and tube lens on CMOS.</p>
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<p>Example of recorded hologram (<b>top</b> image) and reconstructed phase images of a leukocyte (<b>bottom-left</b> yellow inset) and mucus (<b>bottom-right</b> blue inset) with their respective height profiles.</p>
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<p>Optical phase difference (OPD) functions reconstructed numerically from recorded holograms of silica (blue) and polystyrene (red) microbeads: 2 μm polystyrene and 2 μm silica beads (top); 2 μm polystyrene and 1 μm silica beads (down).</p>
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<p>Examples of phase images for different components of the urine samples: (<b>a</b>) <span class="html-italic">Streptococcus</span> spp. chain (left) and leukocyte (right), (<b>b</b>) <span class="html-italic">Escherichia coli</span> (left) and red blood cell (right), (<b>c</b>) macrophage cell, (<b>d</b>) epithelial (squamous) cell, (<b>e</b>) red blood cell (down-left) and spermatozoa cell (up-right), (<b>f</b>) fungi and (<b>g</b>) crystal. Scale bar 10 μm.</p>
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