A Wearable Fiberless Optical Sensor for Continuous Monitoring of Cerebral Blood Flow in Mice

Observing perineuronal nets like structures via coaxial scattering quantitative interference imaging at multiple wavelengths

Perineuronal nets (PNNs) are important functional structures on the surface of nerve cells. Observation of PNNs usually requires dyeing or fluorescent labeling. As a network structure with a micron grid and sub-wavelength thickness but no special optical properties, quantitative phase imaging (QPI) is the only purely optical method for high-resolution imaging of PNNs. We proposed a Scattering Quantitative Interference Imaging (SQII) method which measures the geometric rather than transmission or reflection phase during the scattering process to visualize PNNs. Different from QIP methods, SQII method is sensitive to scattering and not affected by wavelength changes. Via geometric phase shifting method, we simplify the phase shift operation. The SQII method not only focuses on interference phase, but also on the interference contrast. The singularity points and phase lines of the scattering geometric phase depict the edges of the network structure and can be found at the valley area of the interference contrast parameter SINDR under different wavelengths. Our SQII method has its unique imaging properties, is very simple and easy to implement and has more worth for promotion.

White blood cell detection, classification and analysis using phase imaging with computational specificity (PICS)

Treatment of blood smears with Wright's stain is one of the most helpful tools in detecting white blood cell abnormalities. However, to diagnose leukocyte disorders, a clinical pathologist must perform a tedious, manual process of locating and identifying individual cells. Furthermore, the staining procedure requires considerable preparation time and clinical infrastructure, which is incompatible with point-of-care diagnosis. Thus, rapid and automated evaluations of unlabeled blood smears are highly desirable. In this study, we used color spatial light interference microcopy (cSLIM), a highly sensitive quantitative phase imaging (QPI) technique, coupled with deep learning tools, to localize, classify and segment white blood cells (WBCs) in blood smears. The concept of combining QPI label-free data with AI for the purpose of extracting cellular specificity has recently been introduced in the context of fluorescence imaging as phase imaging with computational specificity (PICS). We employed AI models to first translate SLIM images into brightfield micrographs, then ran parallel tasks of locating and labelling cells using EfficientNet, which is an object detection model. Next, WBC binary masks were created using U-net, a convolutional neural network that performs precise segmentation. After training on digitally stained brightfield images of blood smears with WBCs, we achieved a mean average precision of 75% for localizing and classifying neutrophils, eosinophils, lymphocytes, and monocytes, and an average pixel-wise majority-voting F1 score of 80% for determining the cell class from semantic segmentation maps. Therefore, PICS renders and analyzes synthetically stained blood smears rapidly, at a reduced cost of sample preparation, providing quantitative clinical information.

Transformer oil quality evaluation using quantitative phase microscopy

Transformer oil used in oil-filled electrical power transformers aims at insulating, stopping arcing and corona discharge, and dissipating transformer heat. Transformer running inevitably induces molecule decomposition, thus leading to gases released into transformer oil. The released gases not only reduce the transformer oil's performance but also possibly induce transformer fault. To prevent catastrophic failure, approaches using, e.g., chromatography and spectroscopy, precisely measure dissolved gases to monitor transformer oil quality; however, many of these approaches still suffer from complicated operations, expensive costs, or slow speed. To solve these problems, we provide a new transformer oil quality evaluation method based on quantitative phase microscopy. Using our designed phase real-time microscopic camera (PhaseRMiC), under- and over-focus images of gas bubbles in transformer oil can be simultaneously captured during field of view scanning. Further, oil-to-gas-volume ratio can be computed after phase retrieval via solving the transport of intensity equation to evaluate transformer oil quality. Compared with traditionally and widely used approaches, this newly designed method can successfully distinguish transformer oil quality by only relying on rapid operations and low costs, thus delivering a new solution for transformer prognosis and diagnosis.

Patch-Based U-Net Model for Isotropic Quantitative Differential Phase Contrast Imaging

Quantitative differential phase-contrast (qDPC) imaging is a label-free phase retrieval method for weak phase objects using asymmetric illumination. However, qDPC imaging with fewer intensity measurements leads to anisotropic phase distribution in reconstructed images. In order to obtain isotropic phase transfer function, multiple measurements are required; thus, it is a time-consuming process. Here, we propose the feasibility of using deep learning (DL) method for isotropic qDPC microscopy from the least number of measurements. We utilize a commonly used convolutional neural network namely U-net architecture, trained to generate 12-axis isotropic reconstructed cell images (i.e. output) from 1-axis anisotropic cell images (i.e. input). To further extend the number of images for training, the U-net model is trained with a patch-wise approach. In this work, seven different types of living cell images were used for training, validation, and testing datasets. The results obtained from testing datasets show that our proposed DL-based method generates 1-axis qDPC images of similar accuracy to 12-axis measurements. The quantitative phase value in the region of interest is recovered from 66% up to 97%, compared to ground-truth values, providing solid evidence for improved phase uniformity, as well as retrieved missing spatial frequencies in 1-axis reconstructed images. In addition, results from our model are compared with paired and unpaired CycleGANs. Higher PSNR and SSIM values show the advantage of using the U-net model for isotropic qDPC microscopy. The proposed DL-based method may help in performing high-resolution quantitative studies for cell biology.

Robust Phase Unwrapping via Deep Image Prior for Quantitative Phase Imaging

Quantitative phase imaging (QPI) is an emerging label-free technique that produces images containing morphological and dynamical information without contrast agents. Unfortunately, the phase is wrapped in most imaging system. Phase unwrapping is the computational process that recovers a more informative image. It is particularly challenging with thick and complex samples such as organoids. Recent works that rely on supervised training show that deep learning is a powerful method to unwrap the phase; however, supervised approaches require large and representative datasets which are difficult to obtain for complex biological samples. Inspired by the concept of deep image priors, we propose a deep-learning-based method that does not need any training set. Our framework relies on an untrained convolutional neural network to accurately unwrap the phase while ensuring the consistency of the measurements. We experimentally demonstrate that the proposed method faithfully recovers the phase of complex samples on both real and simulated data. Our work paves the way to reliable phase imaging of thick and complex samples with QPI.

Label-free screening of brain tissue myelin content using phase imaging with computational specificity (PICS)

Inadequate myelination in the central nervous system is associated with neurodevelopmental complications. Thus, quantitative, high spatial resolution measurements of myelin levels are highly desirable. We used spatial light interference microcopy (SLIM), a highly sensitive quantitative phase imaging (QPI) technique, to correlate the dry mass content of myelin in piglet brain tissue with dietary changes and gestational size. We combined SLIM micrographs with an artificial intelligence (AI) classifying model that allows us to discern subtle disparities in myelin distributions with high accuracy. This concept of combining QPI label-free data with AI for the purpose of extracting molecular specificity has recently been introduced by our laboratory as phase imaging with computational specificity. Training on 8000 SLIM images of piglet brain tissue with the 71-layer transfer learning model Xception, we created a two-parameter classification to differentiate gestational size and diet type with an accuracy of 82% and 80%, respectively. To our knowledge, this type of evaluation is impossible to perform by an expert pathologist or other techniques.

Combining quantitative phase microscopy and laser-induced shockwave for the study of cell injury

In this paper, we propose a new system for studying cellular injury. The system is a biophotonic work station that can generate Laser-Induced Shockwave (LIS) in the cell culture medium combined with a Quantitative Phase Microscope (QPM), enabling the real-time measurement of intracellular dynamics and quantitative changes in cellular thickness during the damage and recovery processes. In addition, the system is capable of Phase Contrast (PhC) and Differential Interference Contrast (DIC) microscopy. Our studies showed that QPM allows us to discern changes that otherwise would be unnoticeable or difficult to detect using phase or DIC imaging. As one application, this system enables the study of traumatic brain injury in vitro. Astrocytes are the most numerous cells in the central nervous system (CNS) and have been shown to play a role in the repair of damaged neuronal tissue. In this study, we use LIS to create a precise mechanical force in the culture medium at a controlled distance from astrocytes and measure the quantitative changes, in order of nanometers, in cell thickness. Experiments were performed in different cell culture media in order to evaluate the reproducibility of the experimental method.

Common-path intrinsically achromatic optical diffraction tomography

In this work we propose an open-top like common-path intrinsically achromatic optical diffraction tomography system. It operates as a total-shear interferometer and employs Ronchi-type amplitude diffraction grating, positioned in between the camera and the tube lens without an additional 4f system, generating three-beam interferograms with achromatic second harmonic. Such configuration makes the proposed system low cost, compact and immune to vibrations. We present the results of the measurements of 3D-printed cell phantom using laser diode (coherent) and superluminescent diode (partially coherent) light sources. Broadband light sources can be naturally employed without the need for any cumbersome compensation because of the intrinsic achromaticity of the interferometric recording (holograms generated by -1st and +1st conjugated diffraction orders are not affected by the illumination wavelength). The results show that the decreased coherence offers much reduced coherent noise and higher fidelity tomographic reconstruction especially when applied nonnegativity constraint regularization procedure.

Quantifying myelin content in brain tissue using color Spatial Light Interference Microscopy (cSLIM)

Deficient myelination of the brain is associated with neurodevelopmental delays, particularly in high-risk infants, such as those born small in relation to their gestational age (SGA). New methods are needed to further study this condition. Here, we employ Color Spatial Light Interference Microscopy (cSLIM), which uses a brightfield objective and RGB camera to generate pathlength-maps with nanoscale sensitivity in conjunction with a regular brightfield image. Using tissue sections stained with Luxol Fast Blue, the myelin structures were segmented from a brightfield image. Using a binary mask, those portions were quantitatively analyzed in the corresponding phase maps. We first used the CLARITY method to remove tissue lipids and validate the sensitivity of cSLIM to lipid content. We then applied cSLIM to brain histology slices. These specimens are from a previous MRI study, which demonstrated that appropriate for gestational age (AGA) piglets have increased internal capsule myelination (ICM) compared to small for gestational age (SGA) piglets and that a hydrolyzed fat diet improved ICM in both. The identity of samples was blinded until after statistical analyses.

Harmonically decoupled gradient light interference microscopy (HD-GLIM)

Differential phase sensitive methods, such as Nomarski microscopy, play an important role in quantitative phase imaging due to their compatibility with partially coherent illumination and excellent optical sectioning ability. In this Letter, we propose a new system, to the best of our knowledge, to retrieve differential phase information from transparent samples. It is based on a 4f optical system with an amplitude-type spatial light modulator (SLM), which removes the need for traditional differential interference contrast (DIC) optics and specialized phase-only SLMs. We demonstrate the principle of harmonically decoupled gradient light interference microscopy using standard samples, as well as static and dynamic biospecimens.

Quantitative phase imaging of stromal prognostic markers in pancreatic ductal adenocarcinoma

New quantitative prognostic markers are needed for improved pancreatic ductal adenocarcinoma (PDAC) prognosis. Second harmonic generation microscopy has been used to show that collagen fiber alignment in PDAC is a negative prognostic factor. In this work, a series of PDAC and normal adjacent tissue (NAT) biopsies were imaged with spatial light interference microscopy (SLIM). Quantitative analysis performed on the biopsy SLIM images show that PDAC fiber structures have lower alignment per unit length, narrower width, and are longer than NAT controls. Importantly, fibrillar collagen in PDAC shows an inverse relationship between survival data and fiber width and length (p < 0.05).

Effects of substrate patterning on cellular spheroid growth and dynamics measured by gradient light interference microscopy (GLIM)

The development of three-dimensional (3D) cellular architectures during development and pathological processes involves intricate migratory patterns that are modulated by genetics and the surrounding microenvironment. The substrate composition of cell cultures has been demonstrated to influence growth, proliferation and migration in 2D. Here, we study the growth and dynamics of mouse embryonic fibroblast cultures patterned in a tissue sheet which then exhibits 3D growth. Using gradient light interference microscopy (GLIM), a label-free quantitative phase imaging approach, we explored the influence of geometry on cell growth patterns and rotational dynamics. We apply, for the first time to our knowledge, dispersion-relation phase spectroscopy (DPS) in polar coordinates to generate the radial and rotational cell mass-transport. Our data show that cells cultured on engineered substrates undergo rotational transport in a radially independent manner and exhibit faster vertical growth than the control, unpatterned cells. The use of GLIM and polar DPS provides a novel quantitative approach to studying the effects of spatially patterned substrates on cell motility and growth.