Optical Diffraction Tomography Research Articles

Single-exposure Multi-wavelength Optical Diffraction Tomography based on Space-Angle Dual Multiplexing Holography

Single-exposure Multi-wavelength Optical Diffraction Tomography based on Space-Angle Dual Multiplexing Holography

K-space holographic multiplexing for synthetic aperture diffraction tomography

Optical diffraction tomography can be performed with low phototoxicity and photobleaching to analyze 3D cells and tissues. It is desired to develop high throughput and powerful data processing capabilities. We propose high bandwidth holographic microscopy (HBHM). Based on the analyticity of complex amplitudes, the unified holographic multiplexing transfer function is established. A high bandwidth scattering field is achieved via the k-space optical origami of two 2D wavefronts from one interferogram. Scanning illumination modulates the high-horizontal and axial k-space to endow synthetic-aperture from 2D high space-bandwidth product (SBP) scattering fields. The bright-field counterpart SBP of a single scattering field from HBHM is 14.6 megapixels, while the number of pixels is only 13.7 megapixels. It achieves an eight-fold SBP enhancement under the same number of pixels and diffraction limit. The HBHM paves the way toward the performance of high throughput, large-scale, and non-invasive histopathology, cell biology, and industrial inspection.

Non-steady state thermometry with optical diffraction tomography.

Label-free thermometry is a pivotal tool for many disciplines. However, most current approaches are only suitable for planar heat sources in steady state, thereby restricting the range of systems that can be reliably studied. Here, we introduce pump probe-based optical diffraction tomography (ODT) as a method to map temperature precisely and accurately in three dimensions (3D) at the single-particle level. To do so, we first systematically characterize the thermal landscape in a model system consisting of gold nanorods in a microchamber and then benchmark the results against simulations and quantitative phase imaging thermometry. We then apply ODT thermometry to resolve thermal landscapes inaccessible to other label-free approaches in the form of nonplanar heat sources embedded in complex environments and freely diffusing gold nanorods in a microchamber. Last, we foresee that our approach will find many applications where routine thermal characterization of heterogeneous nanoparticles samples in 3D or in non-steady state is required.

Alternating projection combined with fast gradient projection (FGP-AP) method for intensity-only measurement optical diffraction tomography in LED array microscopy.

Optical diffraction tomography (ODT) is a powerful label-free measurement tool that can quantitatively image the three-dimensional (3D) refractive index (RI) distribution of samples. However, the inherent "missing cone problem," limited illumination angles, and dependence on intensity-only measurements in a simplified imaging setup can all lead to insufficient information mapping in the Fourier domain, affecting 3D reconstruction results. In this paper, we propose the alternating projection combined with the fast gradient projection (FGP-AP) method to compensate for the above problem, which effectively reconstructs the 3D RI distribution of samples using intensity-only images captured from LED array microscopy. The FGP-AP method employs the alternating projection (AP) algorithm for gradient descent and the fast gradient projection (FGP) algorithm for regularization constraints. This approach is equivalent to incorporating prior knowledge of sample non-negativity and smoothness into the 3D reconstruction process. Simulations demonstrate that the FGP-AP method improves reconstruction quality compared to the original AP method, particularly in the presence of noise. Experimental results, obtained from mouse kidney cells and label-free blood cells, further affirm the superior 3D imaging efficacy of the FGP-AP method.

Monitoring the Interaction Between Solid Lipid Nanoparticles and Alveolar Macrophages via the Label-Free Technique

Macrophages are employed as targets for delivering genes, drugs, or lipid nanoparticles into tumors or other specific sites. Studying the interaction between solid lipid nanoparticles (SLNs) and macrophages is essential for assessing nanotoxicity and advancing the development of nanomedicines. However, limited data are currently available on the membrane microstructure and biochemical changes that occur when macrophages interact with SLNs. We conducted a label-free morphological and biochemical investigation of NR8383 macrophages using optical diffraction tomography (ODT), which validated the efficiency of the SLNs as a drug delivery system. ODT provided intracellular holotomography to characterize the macrophages and fluorescence imaging to analyze delivery efficiency. ODT analysis revealed the responses of phagocytic macrophages. Additionally, a quantitative analysis of lipid droplets using refractive indices revealed that, compared with incubation with normal cells, incubation with SLNs significantly increased the lipid droplet volume and surface area. The uptake of SLNs into macrophages resulted in increased cell volume, surface area, and concentration, which indicated greater morphological and biochemical variability in the treated cells than in the control cells. The results suggest that ODT imaging is promising for understanding the intracellular distribution of SLNs and useful for validating the efficacy of delivery of SLNs to macrophages.

Convolutional neural network model for automatic recognition and classification of pancreatic cancer cell based on analysis of lipid droplet on unlabeled sample by 3D optical diffraction tomography

IntroductionPancreatic cancer cells generally accumulate large numbers of lipid droplets (LDs), which regulate lipid storage. To promote rapid diagnosis, an automatic pancreatic cancer cell recognition system based on a deep convolutional neural network was proposed in this study using quantitative images of LDs from stain-free cytologic samples by optical diffraction tomography. MethodsWe retrieved 3D refractive index tomograms and reconstructed 37 optical images of one cell. From the four cell lines, the obtained fields were separated into training and test datasets with 10,397 and 3,478 images, respectively. Furthermore, we adopted several machine learning techniques based on a single image-based prediction model to improve the performance of the computer-aided diagnostic system. ResultsPancreatic cancer cells had a significantly lower total cell volume and dry mass than did normal pancreatic cells and were accompanied by greater numbers of lipid droplets (LDs). When evaluating multitask learning techniques utilizing the EfficientNet-b3 model through confusion matrices, the overall 2-category accuracy for cancer classification reached 96.7 %. Simultaneously, the overall 4-category accuracy for individual cell line classification achieved a high accuracy of 96.2 %. Furthermore, when we added the core techniques one by one, the overall performance of the proposed technique significantly improved, reaching an area under the curve (AUC) of 0.997 and an accuracy of 97.06 %. Finally, the AUC reached 0.998 through the ablation study with the score fusion technique. DiscussionOur novel training strategy has significant potential for automating and promoting rapid recognition of pancreatic cancer cells. In the near future, deep learning-embedded medical devices will substitute laborious manual cytopathologic examinations for sustainable economic potential.

Optical diffraction tomography for assessing single cell models in angular light scattering.

Angularly resolved light scattering (ALS) has become a useful tool for assessing the size and refractive index of biological scatterers at cellular and organelle length scales. Sizing organelle populations with ALS relies on Mie scattering theory models, which require significant assumptions about the object, including spherical scatterers and a homogeneous medium. These assumptions may incur greater error at the single cell level, where there are fewer scatterers to be averaged over. We investigate the validity of these assumptions using 3D refractive index (RI) tomograms measured via optical diffraction tomography (ODT). We compute the angular scattering on digitally manipulated tomograms with increasingly strong model assumptions, including RI-matched immersion media, homogeneous cytosol, and spherical organelles. We also compare the tomogram-computed angular scattering to experimental measurements of angular scattering from the same cells to ensure that the ODT-based approach accurately models angular scattering. We show that enforced RI-matching with the immersion medium and a homogeneous cytosol significantly affects the angular scattering intensity shape, suggesting that these assumptions can reduce the accuracy of size distribution estimates.

High‐Speed Multi‐Modal Extended Depth‐of‐Field Microscopy with an Electrically Tunable Lens

AbstractHigh spatial resolution imaging over the entire volume for intravital imaging of biological specimens has long been vital. However, the depth‐of‐field (DOF) and spatial resolution are intrinsically interdependent. Here, a new extended DOF (EDOF) microscopy technique based on partially coherent annular illumination is proposed, termed AI‐EDOF, by combining an electrically tunable lens with a conventional microscope with modified illumination, for motion‐free, high spatial resolution real‐time all‐in‐focus imaging over thick volumes. EDOF imaging is obtained with spatial resolution up to near the incoherent diffraction limit (∼388 nm, 20× / 0.8NA) and temporal resolution ∼30 fps. Moreover, it is demonstrated that the coupled phase and absorption components of the complex refractive index in optical diffraction tomography microscopy can be solved by focus scanning. The Richardson‐Lucy deconvolution with total variation regularization is adopted for deblurring and suppressing noise under low light efficiency high‐speed exposure. To demonstrate the capabilities of the proposed method, experiments are conducted using fixed transgenic zebrafish larvae, Drosophila larvae and dynamic Caenorhabditis elegans under both transmissive and fluorescent imaging modalities, revealing the proposed approach is prospective to be adopted by broad applications such as pharmacokinetics and tumor immunology.

Quantitative real-time phase microscopy for extended depth-of-field imaging based on the 3D single-shot differential phase contrast (ssDPC) imaging method.

Optical diffraction tomography (ODT) is a promising label-free imaging method capable of quantitatively measuring the three-dimensional (3D) refractive index distribution of transparent samples. In recent years, partially coherent ODT (PC-ODT) has attracted increasing attention due to its system simplicity and absence of laser speckle noise. Quantitative phase imaging (QPI) technologies represented by Fourier ptychographic microscopy (FPM), differential phase contrast (DPC) imaging and intensity diffraction tomography (IDT) need to collect several or hundreds of intensity images, which usually introduce motion artifacts when shooting fast-moving targets, leading to a decrease in image quality. Hence, a quantitative real-time phase microscopy (qRPM) for extended depth of field (DOF) imaging based on 3D single-shot differential phase contrast (ssDPC) imaging method is proposed in this research study. qRPM incorporates a microlens array (MLA) to simultaneously collect spatial information and angular information. In subsequent optical information processing, a deconvolution method is used to obtain intensity stacks under different illumination angles in a raw light field image. Importing the obtained intensity stack into the 3D DPC imaging model is able to finally obtain the 3D refractive index distribution. The captured four-dimensional light field information enables the reconstruction of 3D information in a single snapshot and extending the DOF of qRPM. The imaging capability of the proposed qRPM system is experimental verified on different samples, achieve single-exposure 3D label-free imaging with an extended DOF for 160 µm which is nearly 30 times higher than the traditional microscope system.

Multi-harmonic structured illumination-based optical diffraction tomography

Imaging speed and spatial resolution are key factors in optical diffraction tomography (ODT), while they are mutually exclusive in 3D refractive index imaging. This paper presents a multi-harmonic structured illumination-based optical diffraction tomography (MHSI-ODT) to acquire 3D refractive index (RI) maps of transparent samples. MHSI-ODT utilizes a digital micromirror device (DMD) to generate structured illumination containing multiple harmonics. For each structured illumination orientation, four spherical spectral crowns are solved from five phase-shifted holograms, meaning that the acquisition of each spectral crown costs 1.25 raw images. Compared to conventional SI-ODT, which retrieves two spectral crowns from three phase-shifted raw images, MHSI-ODT enhances the imaging speed by 16.7% in 3D RI imaging. Meanwhile, MHSI-ODT exploits both the 1st-order and the 2nd-order harmonics; therefore, it has a better intensity utilization of structured illumination. We demonstrated the performance of MHSI-ODT by rendering the 3D RI distributions of 5 µm polystyrene (PS) microspheres and biological samples.

Comparative analysis of machine learning algorithms on myoelectric signal from intact and transradial amputated limbs

<p>Control strategies of smart hand prosthesis-based myoelectric signals in<br />recent years don't provide the patients with the sensation of biological<br />control of prostheses hand fingers. Therefore, in current work<br />hyperparameters optimization in machine learning algorithm and hand<br />gesture recognition techniques were applied to the myoelectric signal-based<br />on residual muscles contraction of the amputees corresponding to intact<br />forearm limb movement to improve their biological control. In this paper,<br />myoelectric signals are extracted using the MYO armband to recognize ten<br />gestures from ten volunteers (healthy and transradial amputation) on the<br />forearm, thereafter the noise of myoelectric signals using a notch filter (NF)<br />is removed. The proposed classification system involved two machine<br />learning algorithms: (1) the decision tree (DT), tri-layered neural network<br />(TLNN), k-nearest-neighbor (KNN), support vector machine (SVM) and<br />ensemble boosted tree (EBT) classifiers. (2) the optimized machine learning<br />classifiers, i.e., OKNN, OSVM, OEBT with optical diffraction tomography<br />(ODT) and ommatidia detecting algorithm (ODA). The experimental results<br />of classifiers comparison pointed out an algorithm that outperformed with<br />high accuracy is OEBT closely followed by OKNN achieves an accuracy of<br />97.8% and 97.1% for intact forearm limb, while for transradial amputation<br />with an accuracy of 91.9% and 91.4%, respectively.</p>

Optical Diffraction Tomography and Raman Confocal Microscopy for the Investigation of Vacuoles Associated with Cancer Senescent Engulfing Cells.

Wild-type p53 cancer therapy-induced senescent cells frequently engulf and degrade neighboring ones inside a massive vacuole in their cytoplasm. After clearance of the internalized cell, the vacuole persists, seemingly empty, for several hours. Despite large vacuoles being associated with cell death, this process is known to confer a survival advantage to cancer engulfing cells, leading to therapy resistance and tumor relapse. Previous attempts to resolve the vacuolar structure and visualize their content using dyes were unsatisfying for lack of known targets and ineffective dye penetration and/or retention. Here, we overcame this problem by applying optical diffraction tomography and Raman spectroscopy to MCF7 doxorubicin-induced engulfing cells. We demonstrated a real ability of cell tomography and Raman to phenotype complex microstructures, such as cell-in-cells and vacuoles, and detect chemical species in extremely low concentrations within live cells in a completely label-free fashion. We show that vacuoles had a density indistinguishable to the medium, but were not empty, instead contained diluted cell-derived macromolecules, and we could discern vacuoles from medium and cells using their Raman fingerprint. Our approach is useful for the noninvasive investigation of senescent engulfing (and other peculiar) cells in unperturbed conditions, crucial for a better understanding of complex biological processes.

Volumetric segmentation of biological cells and subcellular structures for optical diffraction tomography images.

Three-dimensional, quantitative imaging of biological cells and their internal structures performed by optical diffraction tomography (ODT) is an important part of biomedical research. However, conducting quantitative analysis of ODT images requires performing 3D segmentation with high accuracy, often unattainable with available segmentation methods. Therefore, in this work, we present a new semi-automatic method, called ODT-SAS, which combines several non-machine-learning techniques to segment cells and 2 types of their organelles: nucleoli and lipid structures (LS). ODT-SAS has been compared with Cellpose and slice-by-slice manual segmentation, respectively, in cell segmentation and organelles segmentation. The comparison shows superiority of ODT-SAS over Cellpose and reveals the potential of our technique in detecting cells, nucleoli and LS.

Three-dimensional refractive index estimation based on deep-inverse non-interferometric optical diffraction tomography (ODT-Deep).

Optical diffraction tomography (ODT) solves an inverse scattering problem to obtain label-free, 3D refractive index (RI) estimation of biological specimens. This work demonstrates 3D RI retrieval methods suitable for partially-coherent ODT systems supported by intensity-only measurements consisting of axial and angular illumination scanning. This framework allows for access to 3D quantitative RI contrast using a simplified non-interferometric technique. We consider a traditional iterative tomographic solver based on a multiple in-plane representation of the optical scattering process and gradient descent optimization adapted for focus-scanning systems, as well as an approach that relies solely on 3D convolutional neural networks (CNNs) to invert the scattering process. The approaches are validated using simulations of the 3D scattering potential for weak phase 3D biological samples.

Influence of Chemical and Genetic Manipulations on Cellular Organelles Quantified by Label-Free Optical Diffraction Tomography.

Label-free optical diffraction tomography provides three-dimensional imaging of cells and organelles, along with their refractive index (RI) and volume. These physical parameters are valuable for quantitative and accurate analysis of the subcellular microenvironment and its connections to intracellular biological properties. In biological and biochemical cell analysis, various invasive cell manipulations are used, such as temperature change, chemical fixation, live cell staining with fluorescent dye, and gene overexpression of exogenous proteins. However, it is not fully understood how these various manipulations affect the physicochemical properties of different organelles. In this study, we investigated the impact of these manipulations on the cellular properties of single HeLa cells. We found that after cell fixation and an increase in temperature, the RI value of organelles, such as the nucleus and cytoplasm, significantly decreased overall. Interestingly, unlike the cell nuclei, cytoplasmic RI values were hardly detected after membrane permeation, indicating that only intracytoplasmic components were largely lost. Additionally, our findings revealed that the expression of GFP and GFP-tagged proteins significantly increased the RI values of organelles in living cells compared to the less effective RI changes observed with chemical fluorescence staining for cell organelles. The result demonstrates that distinct types of invasive manipulations can alter the microenvironment of organelles in different ways. Our study sheds new light on how chemical and genetic manipulations affect organelles.

Deep learning-based label-free hematology analysis framework using optical diffraction tomography

Hematology analysis, a common clinical test for screening various diseases, has conventionally required a chemical staining process that is time-consuming and labor-intensive. To reduce the costs of chemical staining, label-free imaging can be utilized in hematology analysis. In this work, we exploit optical diffraction tomography and the fully convolutional one-stage object detector or FCOS, a deep learning architecture for object detection, to develop a label-free hematology analysis framework. Detected cells are classified into four groups: red blood cell, abnormal red blood cell, platelet, and white blood cell. In the results, the trained object detection model showed superior detection performance for blood cells in refractive index tomograms (0.977 mAP) and also showed high accuracy in the four-class classification of blood cells (0.9708 weighted F1 score, 0.9712 total accuracy). For further verification, mean corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH) were compared with values obtained from reference hematology equipment, with our results showing reasonable correlation in both MCV (0.905) and MCH (0.889). This study provides a successful demonstration of the proposed framework in detecting and classifying blood cells using optical diffraction tomography for label-free hematology analysis.

Motion detection in diffraction tomography by common circle methods

The method of common lines is a well-established reconstruction technique in cryogenic electron microscopy (cryo-EM), which can be used to extract the relative orientations of an object given tomographic projection images from different directions. In this paper, we deal with an analogous problem in optical diffraction tomography. Based on the Fourier diffraction theorem, we show that rigid motions of the object, i.e., rotations and translations, can be determined by detecting common circles in the Fourier-transformed data. We introduce two methods to identify common circles. The first one is motivated by the common line approach for projection images and detects the relative orientation by parameterizing the common circles in the two images. The second one assumes a smooth motion over time and calculates the angular velocity of the rotational motion via an infinitesimal version of the common circle method. Interestingly, using the stereographic projection, both methods can be reformulated as common line methods, but these lines are, in contrast to those used in cryo-EM, not confined to pass through the origin and allow for a full reconstruction of the relative orientations. Numerical proof-of-the-concept examples demonstrate the performance of our reconstruction methods.

Real-Time Monitoring of Multitarget Antimicrobial Mechanisms of Peptoids Using Label-Free Imaging with Optical Diffraction Tomography.

Antimicrobial peptides (AMPs) are promising therapeutics in the fight against multidrug-resistant bacteria. As a mimic of AMPs, peptoids with N-substituted glycine backbone have been utilized for antimicrobials with resistance against proteolytic degradation. Antimicrobial peptoids are known to kill bacteria by membrane disruption; however, the nonspecific aggregation of intracellular contents is also suggested as an important bactericidal mechanism. Here,structure-activity relationship (SAR) of a library of indole side chain-containing peptoids resulting in peptoid 29 as a hit compound is investigated. Then, quantitative morphological analyses of live bacteria treated with AMPs and peptoid 29 in a label-free manner using optical diffraction tomography (ODT) are performed. It is unambiguously demonstrated that both membrane disruption and intracellular biomass flocculation are primary mechanisms of bacterial killing by monitoring real-time morphological changes of bacteria. These multitarget mechanisms and rapid action can be a merit for the discovery of a resistance-breaking novel antibiotic drug.

Machine-learning-based diagnosis of thyroid fine-needle aspiration biopsy synergistically by Papanicolaou staining and refractive index distribution

We developed a machine learning algorithm (MLA) that can classify human thyroid cell clusters by exploiting both Papanicolaou staining and intrinsic refractive index (RI) as correlative imaging contrasts and evaluated the effects of this combination on diagnostic performance. Thyroid fine-needle aspiration biopsy (FNAB) specimens were analyzed using correlative optical diffraction tomography, which can simultaneously measure both, the color brightfield of Papanicolaou staining and three-dimensional RI distribution. The MLA was designed to classify benign and malignant cell clusters using color images, RI images, or both. We included 1535 thyroid cell clusters (benign: malignancy = 1128:407) from 124 patients. Accuracies of MLA classifiers using color images, RI images, and both were 98.0%, 98.0%, and 100%, respectively. As information for classification, the nucleus size was mainly used in the color image; however, detailed morphological information of the nucleus was also used in the RI image. We demonstrate that the present MLA and correlative FNAB imaging approach has the potential for diagnosing thyroid cancer, and complementary information from color and RI images can improve the performance of the MLA.

Label-free histological analysis of retrieved thrombi in acute ischemic stroke using optical diffraction tomography and deep learning.

For patients with acute ischemic stroke, histological quantification of thrombus composition provides evidence for determining appropriate treatment. However, the traditional manual segmentation of stained thrombi is laborious and inconsistent. In this study, we propose a label-free method that combines optical diffraction tomography (ODT) and deep learning (DL) to automate the histological quantification process. The DL model classifies ODT image patches with 95% accuracy, and the collective prediction generates a whole-slide map of red blood cells and fibrin. The resulting whole-slide composition displays an average error of 1.1% and does not experience staining variability, facilitating faster analysis with reduced labor. The present approach will enable rapid and quantitative evaluation of blood clot composition, expediting the preclinical research and diagnosis of cardiovascular diseases.