Ankit Butola

SignificanceImaging 3D in vitro kidney models is essential to understand kidney function and pathology. Label-free characterization of such specimens seeks to supplement existing imaging techniques and avoid the need for contrast agents that can disturb the native state of living samples. Conventional label-free optical imaging techniques are compatible with living samples but face challenges such as poor sectioning capability, fragmentary morphology, and lack of chemical-specific information.AimWe aim to develop and demonstrate a correlative label-free imaging platform capable of simultaneously capturing morphological and chemical-specific information from 3D cultured kidney mesangial cells.ApproachWe combined simultaneous label-free autofluorescence-multiharmonic (SLAM) microscopy and gradient light interference microscopy (GLIM) to extract both chemical-specific and morphological tomography of 3D cultured kidney mesangial cells. In this approach, SLAM provides a nonlinear imaging platform with a single excitation source to simultaneously acquire autofluorescence (FAD and NAD(P)H), second- and third-harmonic signals from the cells. Complementarily, GLIM acquires high-contrast quantitative phase information to quantify structural changes in samples with a thickness of up to 250 μm.ResultsOur correlative imaging results demonstrate the ability to image and quantify both morphology and chemical-specific signals of kidney mesangial cells in 3D. The combination of GLIM and SLAM provides complementary information critical for understanding kidney function, including metabolism and matrix deposition under controlled physiological conditions.ConclusionsThe proposed correlative imaging approach establishes a versatile and hassle-free platform for morpho-chemical cellular tomography, offering unique opportunities for studying the structure and function of 3D kidney models in their native state.

Optical coherence tomography (OCT) is a powerful imaging technique that utilizes optical scattering to extract tomographic information of biological samples in their natural state. Over the past two decades, OCT has advanced both experimentally and computationally to improve its performance in terms of resolution, penetration depth, acquisition speed, and sensitivity. The interpretability of OCT particularly increased through its integration with artificial intelligence (AI). While fast and label-free nature of OCT becomes popular in optical imaging, AI assigned an artificial system to mimic human intelligence, such as classification, visualizations, and segmentation. In particular, the subfield of AI, i.e., machine learning (ML) is a data-driven approach, which is explicitly designed and optimized by fine-tuning the hyperparameters to reflect certain features through learning rule. In this chapter, we aim to provide a critical overview of OCT and machine learning model by presenting the scientific context, working principles, and current biomedical applications. The chapter will also cover types of convolutional neural networks (CNNs) that have been used in analyzing diverse OCT images.

Label-free characterization of biological specimens seeks to supplement existing imaging techniques and avoid the need for contrast agents that can disturb the native state of living samples. Conventional label-free optical imaging techniques are compatible with living samples but face challenges such as poor sectioning capability, fragmentary morphology, and lack chemical specific information. Here, we combined simultaneous label-free autofluorescence multi-harmonic (SLAM) microscopy and gradient light interference microscopy (GLIM) to extract both chemical specific and morphological tomography of 3D cultured kidney mesangial cells. Imaging 3D in vitro kidney models is essential to understand kidney function and pathology. Our correlative approach enables imaging and quantification of these cells to extract both morphology and chemical-specific signals that is crucial for understanding kidney function. In our approach, SLAM offers a nonlinear imaging platform with a single-excitation source to simultaneously acquire autofluorescence (FAD and NAD(P)H), second, and third harmonic signal from the 3D cultured cells. Complementarily, GLIM acquires high-contrast quantitative phase information to quantify structural changes in samples with thickness of up to 250 micron. Our correlative imaging results demonstrate a versatile and hassle-free platform for morpho-chemical cellular tomography to investigate functions such as metabolism and matrix deposition of kidney mesangial cells in 3D under controlled physiological conditions.

Structured beams carrying topological defects, namely phase and Stokes singularities, have gained extensive interest in numerous areas of optics. The non-separable spin and orbital angular momentum states of hybridly polarized Stokes singular beams provide additional freedom for manipulating optical fields. However, the characterization of hybridly polarized Stokes vortex beams remains challenging owing to the degeneracy associated with the complex polarization structures of these beams. In addition, experimental noise factors such as relative phase, amplitude, and polarization difference together with beam fluctuations add to the perplexity in the identification process. Here, we present a generalized diffraction-based Stokes polarimetry approach assisted with deep learning for efficient identification of Stokes singular beams. A total of 15 classes of beams are considered based on the type of Stokes singularity and their associated mode indices. The resultant total and polarization component intensities of Stokes singular beams after diffraction through a triangular aperture are exploited by the deep neural network to recognize these beams. Our approach presents a classification accuracy of 98.67% for 15 types of Stokes singular beams that comprise several degenerate cases. The present study illustrates the potential of diffraction of the Stokes singular beam with polarization transformation, modeling of experimental noise factors, and a deep learning framework for characterizing hybridly polarized beams.

Conventional methods of assessing sperm quality are based on qualitative analysis of semen. In this paper, we combined time-lapse quantitative phase imaging and deep learning for the classification of healthy and unhealthy sperm cells.

We present a gradient light interference microscopy system to visualize 3D quantitative imaging of kidney mesangial cells. We used the system to obtain the morphology of 3D cultured kidney cells of thickness 200 µm.

Reconstructing 3D refractive index profile of scatterers using optical microscopy measurements presents several challenges over the conventional microwave and RF domain measurement scenario. These include phaseless and polarization-insensitive measurements, small numerical aperture, as well as a Green’s function where spatial frequencies are integrated in a weighted manner such that far-field angular spectrum cannot be probed and high spatial frequencies that permit better resolution are weighed down. As a result of these factors, the non-linearity and the ill-posedness of the inverse problem are quite severe. These limitations have imposed that inverse scattering problems in the microscopy domain largely consider scalar wave approximations and neglect multiple scattering. Here, we present first inverse scattering results for optical microscopy setup where full-wave vectorial formulation and multiple scattering is incorporated. We present (a) how three popular inverse scattering solvers from microwave domain can be adapted for the present inverse problem, (b) the opportunities and challenges presented by each of these solvers, (c) a comparative insight into these solvers and contrast with the simpler Born approximation approach, and (d) potential routes to improve the performance of these solvers for the hard inverse problem of optical microscopy.

Structured illumination microscopy (SIM) is a most popular super-resolution technique used in cell biology and bio-imaging. Here, we present a novel approach to realize multiscale super-resolution SIM by swapping the non-linearity between instrumentation and reconstruction algorithm to achieve super-resolution. Our goal is to overcome two conventional limitations of SIM i.e., fixed resolution and the need of precise knowledge of illumination pattern. The optical system encodes higher order frequencies of the sample by projecting PSF-modulated binary patterns for illuminating the sample plane, which do not have clean Fourier peaks conventionally used in SIM. These patterns fold high frequency content of sample into the measurements in an obfuscated manner, which are de-obfuscated using multiple signal classification algorithm. Our approach eliminates the need of clean peaks in the illumination pattern, which have multiple advantages i.e., simple instrumentation and the flexibility of using different collection lenses. The reconstruction algorithm used in the proposed work does not require known illumination. Finally, we reduce the sensitivity of reconstruction algorithm to the signal to background ratio. Here, we acquired patterned illumination images of the same sample using different collection objective lenses, and obtained diffraction limited as well as super-resolved images, supporting 4 different resolution in the same system through SIM. Our experimental results with multiple collection objective lens show wider applicability of the proposed system at signal to background ration as small as <3.

Structured beams carrying topological defects, namely phase and Stokes singularities, have gained extensive interest in numerous areas of optics. The non-separable spin and orbital angular momentum states of hybridly polarized Stokes singular beams provide additional freedom for manipulating optical fields. However, the characterization of hybridly polarized Stokes vortex beams remains challenging owing to the degeneracy associated with the complex polarization structures of these beams. In addition, experimental noise factors such as relative phase, amplitude, and polarization difference together with beam fluctuations add to the perplexity in the identification process. Here, we present a generalized diffraction-based Stokes polarimetry approach assisted with deep learning for efficient identification of Stokes singular beams. A total of 15 classes of beams are considered based on the type of Stokes singularity and their associated mode indices. The resultant total and polarization component intensities of Stokes singular beams after diffraction through a triangular aperture are exploited by the deep neural network to recognize these beams. Our approach presents a classification accuracy of 98.67% for 15 types of Stokes singular beams that comprise several degenerate cases. The present study illustrates the potential of diffraction of the Stokes singular beam with polarization transformation, modeling of experimental noise factors, and a deep learning framework for characterizing hybridly polarized beams