arXiv subjects

Balpreet Singh Ahluwalia

Publications and source records attributed to Balpreet Singh Ahluwalia.

At least 19 recordsLinked to original sources

arXiv2025

Comparative study of microscopy methods to assess fish intestinal microvilli

The primary function of intestinal microvilli is to increase the surface area of the intestinal lining to maximize nutrient absorption. This is especially important as fish, like other animals, need to efficiently absorb proteins, carbohydrates, lipids, vitamins, and minerals from their digested food to support their growth and energy needs. Despite its importance to the fish health, the small size and dense footprint of microvilli hinders its investigation and necessitates the need of advanced microscopy methods for its visualization. Characterization of the microvilli using super-resolution microscopy provides insights into their structural organization, spatial distribution, and surface properties. Here, we present a comprehensive investigation of different optical, electron and force microscopy methods for analysis of fish microvilli. The super-resolution optical microscopy methods used are 3D structured illumination microscopy (SIM), stimulated emission depletion microscopy (STED), and fluorescence fluctuation based super-resolution microscopy (FF-SRM). We also visualized the intestinal microvilli in fish using diffraction-limited optical microscopy methods including confocal and total internal reflection fluorescence microscopy. Additionally, label-free microscopy methods, such as quantitative phase microscopy (QPM) and bright-field imaging, were also employed. To obtain ultra-high resolution, we used scanning electron microscopy (SEM), transmission electron microscopy (TEM) and atomic force microscopy (AFM). We demonstrate a systematic comparison of these microscopy techniques in resolving and quantifying microvilli features, ranging from 1-2 um structural morphology to 10-100 nm surface details.

physics.optics↗

arXiv2024

Label-free incoherent super-resolution optical microscopy

The photo-kinetics of fluorescent molecules have enabled the circumvention of far-field optical diffraction-limit. Despite its enormous potential, the necessity to label the sample may adversely influence the delicate biology under investigation. Thus, continued development efforts are needed to surpass the far-field label-free diffraction barrier. The coherence of the detected light in label-free mode hinders the application of existing super-resolution methods based on incoherent fluorescence imaging. In this article, we present the physics and propose a methodology to circumvent this challenge by exploiting the photoluminescence of silicon nitride waveguides for near-field illumination of unlabeled samples. The technique is abbreviated EPSLON, Evanescently decaying Photoluminescence Scattering enables Label-free Optical Nanoscopy. We demonstrate that such an illumination has properties that mimic the photo-kinetics of nano-sized fluorescent molecules. This allows for developing a label-free incoherent system that is linear in intensity, and stable with time thereby permitting the application of techniques like structured illumination microscopy (SIM) and intensity-fluctuation-based optical nanoscopy (IFON) in label-free mode to circumvent the diffraction limit.

physics.optics↗

arXiv2022

Deep Optical Coding Design in Computational Imaging

Computational optical imaging (COI) systems leverage optical coding elements (CE) in their setups to encode a high-dimensional scene in a single or multiple snapshots and decode it by using computational algorithms. The performance of COI systems highly depends on the design of its main components: the CE pattern and the computational method used to perform a given task. Conventional approaches rely on random patterns or analytical designs to set the distribution of the CE. However, the available data and algorithm capabilities of deep neural networks (DNNs) have opened a new horizon in CE data-driven designs that jointly consider the optical encoder and computational decoder. Specifically, by modeling the COI measurements through a fully differentiable image formation model that considers the physics-based propagation of light and its interaction with the CEs, the parameters that define the CE and the computational decoder can be optimized in an end-to-end (E2E) manner. Moreover, by optimizing just CEs in the same framework, inference tasks can be performed from pure optics. This work surveys the recent advances on CE data-driven design and provides guidelines on how to parametrize different optical elements to include them in the E2E framework. Since the E2E framework can handle different inference applications by changing the loss function and the DNN, we present low-level tasks such as spectral imaging reconstruction or high-level tasks such as pose estimation with privacy preserving enhanced by using optimal task-based optical architectures. Finally, we illustrate classification and 3D object recognition applications performed at the speed of the light using all-optics DNN.

cs.CV↗

arXiv2022

Transmission Structured Illumination Microscopy using Tilt-mirror Assembly

We present experimental demonstration of tilt-mirror assisted transmission structured illumination microscopy (tSIM) that offers a large field of view super resolution imaging. An assembly of custom-designed tilt-mirrors are employed as the illumination module where the sample is excited with the interference of two beams reflected from the opposite pair of mirror facets. Tunable frequency structured patterns are generated by changing the mirror-tilt angle and the hexagonal-symmetric arrangement is considered for the isotropic resolution in three orientations. Utilizing high numerical aperture (NA) objective in standard SIM provides super-resolution compromising with the field-of-view (FOV). Employing low NA (20X/0.4) objective lens detection, we experimentally demonstrate ~ (0.56mm x 0.35mm) size single FOV image with ~1.7- and ~2.4-fold resolution improvement (exploiting various illumination by tuning tilt-mirrors) over the diffraction limit. The results are verified both for the fluorescent beads as well as biological samples. The tSIM geometry decouples the illumination and the collection light paths consequently enabling free change of the imaging objective lens without influencing the spatial frequency of the illumination pattern that are defined by the tilt-mirrors. The large and scalable FoV supported by tSIM will find usage for applications where scanning large areas are necessary as in pathology and applications where images must be correlated both in space and time.

physics.optics↗

arXiv2022

Surface acoustic waves inside polystyrene microparticles through photoacoustic microscopy

We demonstrate surface acoustic waves inside polystyrene microspheres of different sizes experimentally through photoacoustic microscopy and validate the experimental result with simulation. A novel method for sample preparation of a lifted sample is also presented where the microparticles are suspended in agarose above the surface of the petridish. Another objective of this study was to investigate the results with different laser focus hitting on the microparticles and their impact on photoacoustic images and photoacoustic signals. An absorbing microsphere is excited with a pulsed laser of wavelength 532 nm and the photoacoustic signal is detected using a 40 MHz transducer. On analyzing the photoacoustic signals from microspheres, we find the signature of surface acoustic waves.

physics.class-ph↗

arXiv2022

Label-free chip-based evanescent light scattering super-resolution and superior-contrast optical microscopy (cELS)

Chip-based Evanescent Light Scattering (cELS) utilizes the multiple modes of a high-index contrast optical waveguide for near-field illumination of unlabeled samples, thereby repositioning the highest spatial frequencies of the sample into the far-field. The multiple modes scattering off the sample with different phase differences is engineered to have random spatial distributions within the integration time of the camera, mitigating the coherent speckle noise. This enables label-free superior-contrast imaging of weakly scattering nanosized specimens such as extra-cellular vesicles (EVs) and liposomes, dynamics of living HeLa cells etc. The article explains and validates experimentally the physics behind cELS by demonstrating a multi-moded straight waveguide as a partially coherent light source. For isotropic super-resolution, spatially incoherent light engineered via multiple-arms waveguide chip and intensity-fluctuation based algorithms are used. The proof-of-concept results are demonstrated on 100 nm polystyrene beads and resolution improvement of close to 2X is shown. cELS also realizes (2-10)X more contrast as opposed to conventional imaging techniques. In addition, cELS platform is miniaturized and enables large field-of-view imaging compared to state of the art label-free techniques. cELS holds a potential for label-free super-resolution imaging of nanosized biological specimens at high-throughput.

physics.optics↗

arXiv2021

Demystifying speckle field quantitative phase microscopy

Quantitative phase microscopy (QPM) has found significant applications in the field of biomedical imaging which works on the principle of interferometry. The theory behind achieving interference in QPM with conventional light sources such as white light and lasers is very well developed. Recently, the use of dynamic speckle illumination (DSI) in QPM has attracted attention due to its advantages over conventional light sources such as high spatial phase sensitivity, single shot, scalable field of view (FOV) and resolution. However, the understanding behind obtaining interference fringes in QPM with DSI has not been convincingly covered previously. This imposes a constraint on obtaining interference fringes in QPM using DSI and limits its widespread penetration in the field of biomedical imaging. The present article provides the basic understanding of DSI through both simulation and experiments that is essential to build interference optical microscopy systems such as QPM, digital holographic microscopy and optical coherence tomography. Using the developed theory of DSI we demonstrate its capabilities of using non-identical objective lenses in both arms of the interference microscopy without degrading the interference fringe contrast and providing the flexibility to use user-defined microscope objective lens. It is also demonstrated that the interference fringes are not washed out over a large range of optical path difference (OPD) between the object and the reference arm providing competitive edge over low temporal coherence light sources. The theory and explanation developed here would enable wider penetration of DSI based QPM for applications in biology and material sciences.

physics.optics↗

arXiv2020

High throughput spatially sensitive single-shot quantitative phase microscopy

High space-bandwidth product with high spatial phase sensitivity is indispensable for a single-shot quantitative phase microscopy (QPM) system. It opens avenue for widespread applications of QPM in the field of biomedical imaging. Temporally low coherence length light sources are generally implemented to achieve high spatial phase sensitivity in QPM at the cost of either reduced temporal resolution or smaller field of view (FOV). On the contrary, high temporal coherence light sources like lasers are capable of exploiting the full FOV of the QPM systems at the expense of less spatial phase sensitivity. In the present work, we employed pseudo-thermal light source (PTLS) in QPM which overcomes the limitations of conventional light sources. The capabilities of PTLS over conventional light sources are systematically studied and demonstrated on various test objects like USAF resolution chart and thin optical waveguide (height ~ 8 nm). The spatial phase sensitivity of QPM in case of PTLS is measured to be equivalent to that for white light source. The high-speed and large FOV capabilities of PTLS based QPM is demonstrated by high-speed imaging of live sperm cells that is limited by the camera speed and by imaging extra-ordinary large FOV phase imaging on histopathology placenta tissue samples.

physics.optics↗

arXiv2020

Soft thresholding schemes for multiple signal classification algorithm

Multiple signal classification algorithm (MUSICAL) exploits temporal fluctuations in fluorescence intensity to perform super-resolution microscopy by computing the value of a super-resolving indicator function across a fine sample grid. A key step in the algorithm is the separation of the measurements into signal and noise subspaces, based on a single user-specified parameter called the threshold. The resulting image is strongly sensitive to this parameter and the subjectivity arising from multiple practical factors makes it difficult to determine the right rule of selection. We address this issue by proposing soft thresholding schemes derived from a new generalized framework for indicator function design. We show that the new schemes significantly alleviate the subjectivity and sensitivity of hard thresholding while retaining the super-resolution ability. We also evaluate the trade-off between resolution and contrast and the out-of-focus light rejection using the various indicator functions. Through this, we create significant new insights into the use and further optimization of MUSICAL for a wide range of practical scenarios.

eess.IV↗

arXiv2020

Simulation-supervised deep learning for analysing organelles states and behaviour in living cells

In many real-world scientific problems, generating ground truth (GT) for supervised learning is almost impossible. The causes include limitations imposed by scientific instrument, physical phenomenon itself, or the complexity of modeling. Performing artificial intelligence (AI) tasks such as segmentation, tracking, and analytics of small sub-cellular structures such as mitochondria in microscopy videos of living cells is a prime example. The 3D blurring function of microscope, digital resolution from pixel size, optical resolution due to the character of light, noise characteristics, and complex 3D deformable shapes of mitochondria, all contribute to making this problem GT hard. Manual segmentation of 100s of mitochondria across 1000s of frames and then across many such videos is not only herculean but also physically inaccurate because of the instrument and phenomena imposed limitations. Unsupervised learning produces less than optimal results and accuracy is important if inferences relevant to therapy are to be derived. In order to solve this unsurmountable problem, we bring modeling and deep learning to a nexus. We show that accurate physics based modeling of microscopy data including all its limitations can be the solution for generating simulated training datasets for supervised learning. We show here that our simulation-supervised segmentation approach is a great enabler for studying mitochondrial states and behaviour in heart muscle cells, where mitochondria have a significant role to play in the health of the cells. We report unprecedented mean IoU score of 91% for binary segmentation (19% better than the best performing unsupervised approach) of mitochondria in actual microscopy videos of living cells. We further demonstrate the possibility of performing multi-class classification, tracking, and morphology associated analytics at the scale of individual mitochondrion.

eess.IV↗

arXiv2020

On-chip TIRF nanoscopy by applying Haar wavelet kernel analysis on intensity fluctuations induced by chip illumination

Photonic-chip based TIRF illumination has been used to demonstrate several on-chip optical nanoscopy methods. The sample is illuminated by the evanescent field generated by the electromagnetic wave modes guided inside the optical waveguide. In addition to the photokinetics of the fluorophores, the waveguide modes can be further exploited for introducing controlled intensity fluctuations for exploitation by techniques such as super-resolution optical fluctuation imaging (SOFI). However, the problem of non-uniform illumination pattern generated by the modes contribute to artifacts in the reconstructed image. To alleviate this problem, we propose to perform Haar wavelet kernel (HAWK) analysis on the original image stack prior to the application of (SOFI). HAWK produces a computational image stack with higher spatio-temporal sparsity than the original stack. In the case of multimoded non-uniform illumination patterns, HAWK processing bre aks the mode pattern while introducing spatio-temporal sparsity, thereby differentially affecting the non-uniformity of the illumination. Consequently, this assists nanoscopy methods such as SOFI to better support super-resolution, which is otherwise compromised due to spatial correlation of the mode patterns in the raw image. Furthermore, applying HAWK prior to SOFI alleviates the problem of artifacts due to non-uniform illumination without degrading temporal resolution. Our experimental results demonstrate resolution enhancement as well as reduction in artifacts through the combination of HAWK and SOFI.

physics.optics↗

arXiv2020

Multi-modal on-chip nanoscopy and quantitative phase image reveals the morphology of liver sinusoidal enodthelial cells

Visualization of three-dimensional morphological changes in the subcellular structures of a biological specimen is one of the greatest challenges in life science. Despite conspicuous refinements in optical nanoscopy, determination of quantitative changes in subcellular structure, i.e., size and thickness, remains elusive. We present an integrated chip-based optical nanoscopy set-up that provides a lateral optical resolution of 61 nm combined with a highly sensitive quantitative phase microscopy (QPM) system with a spatial phase sensitivity of $\pm$20 mrad. We use the system to obtain the 3D morphology of liver sinusoidal endothelial cells (LSECs) combined with super-resolved spatial information. LSECs have a unique morphology with nanopores that are present in the plasma membrane, called fenestration. The fenestrations are grouped in clusters called sieve plates, which are around 100 nm thick. Thus, imaging and quantification of fenestration and sieve plate thickness requires resolution and sensitivity of sub-100 nm along both lateral and axial directions. In the chip-based nanoscope, the optical waveguides are used both for hosting and illuminating the sample. A strong evanescent field is generated on top of the waveguide surface for single molecule fluorescence excitation. The fluorescence signal is captured by an upright microscope, which is converted into a Linnik-type interferometer to sequentially acquire both super-resolved images and quantitative phase information of the sample. The multi-modal microscope provided an estimate of the fenestration diameter of 124$\pm$41 nm and revealed the average estimated thickness of the sieve plates in the range of 91.2$\pm$43.5 nm for two different cells. The combination of these techniques offers visualization of both the lateral size (using nanoscopy) and the thickness map of sieve plates, i.e. discrete clusters fenestrations in QPM mode.

physics.bio-ph↗

arXiv2020

Deep learning architecture LightOCT for diagnostic decision support using optical coherence tomography images of biological samples

Optical coherence tomography (OCT) is being increasingly adopted as a label-free and non-invasive technique for biomedical applications such as cancer and ocular disease diagnosis. Diagnostic information for these tissues is manifest in textural and geometric features of the OCT images, which are used by human expertise to interpret and triage. However, it suffers delays due to the long process of the conventional diagnostic procedure and shortage of human expertise. Here, a custom deep learning architecture, LightOCT, is proposed for the classification of OCT images into diagnostically relevant classes. LightOCT is a convolutional neural network with only two convolutional layers and a fully connected layer, but it is shown to provide excellent training and test results for diverse OCT image datasets. We show that LightOCT provides 98.9% accuracy in classifying 44 normal and 44 malignant (invasive ductal carcinoma) breast tissue volumetric OCT images. Also, >96% accuracy in classifying public datasets of ocular OCT images as normal, age-related macular degeneration and diabetic macular edema. Additionally, we show ~96% test accuracy for classifying retinal images as belonging to choroidal neovascularization, diabetic macular edema, drusen, and normal samples on a large public dataset of more than 100,000 images. The performance of the architecture is compared with transfer learning based deep neural networks. Through this, we show that LightOCT can provide significant diagnostic support for a variety of OCT images with sufficient training and minimal hyper-parameter tuning. The trained LightOCT networks for the three-classification problem will be released online to support transfer learning on other datasets.

physics.med-ph↗

arXiv2020

High space-bandwidth in quantitative phase imaging using partially spatially coherent optical coherence microscopy and deep neural network

Quantitative phase microscopy (QPM) is a label-free technique that enables to monitor morphological changes at subcellular level. The performance of the QPM system in terms of spatial sensitivity and resolution depends on the coherence properties of the light source and the numerical aperture (NA) of objective lenses. Here, we propose high space-bandwidth QPM using partially spatially coherent optical coherence microscopy (PSC-OCM) assisted with deep neural network. The PSC source synthesized to improve the spatial sensitivity of the reconstructed phase map from the interferometric images. Further, compatible generative adversarial network (GAN) is used and trained with paired low-resolution (LR) and high-resolution (HR) datasets acquired from PSC-OCM system. The training of the network is performed on two different types of samples i.e. mostly homogenous human red blood cells (RBC) and on highly heterogenous macrophages. The performance is evaluated by predicting the HR images from the datasets captured with low NA lens and compared with the actual HR phase images. An improvement of 9 times in space-bandwidth product is demonstrated for both RBC and macrophages datasets. We believe that the PSC-OCM+GAN approach would be applicable in single-shot label free tissue imaging, disease classification and other high-resolution tomography applications by utilizing the longitudinal spatial coherence properties of the light source.

physics.bio-ph↗

arXiv2020

A transparent waveguide chip for versatile TIRF-based microscopy and nanoscopy

Total internal reflection fluorescence microscopy (TIRF) has enabled low-background, live-cell friendly imaging of cell surfaces and other thin samples thanks to the shallow penetration of the evanescent light field into the sample. The implementation of TIRF on optical waveguide chips (c-TIRF) has overcome historical limitations on the magnification and field of view (FOV) compared to lens-based TIRF, and further allows the light to be guided in complicated patterns that can be used for advanced imaging techniques or selective stimulation of the sample. However, the opacity of the chips themselves has thus far precluded their use on inverted microscopes and complicated sample preparation and handling. In this work, we introduce a new platform for c-TIRF imaging based on a transparent substrate, which is fully compatible with sample handling and imaging procedures commonly used with a standard #1.5 glass coverslip, and is fabricated using standard complementary metal-oxide-semiconductor (CMOS) techniques, which can easily be scaled up for mass production. We demonstrate its performance on synthetic and biological samples using both upright and inverted microscopes, and show how it can be extended to super-resolution applications, achieving a resolution of 116 nm using super resolution radial fluctuations (SRRF). These new chips retain the scalable FOV of opaque chip-based TIRF and the high axial resolution of TIRF, and have the versatility to be used with many different objective lenses, microscopy methods, and handling techniques. We thus see c-TIRF as a technology primed for widespread adoption, increasing both TIRF's accessibility to users and the range of applications that can benefit from it.

physics.app-ph↗

arXiv2020

High spatially sensitive quantitative phase imaging assisted with deep neural network for classification of human spermatozoa under stressed condition

Sperm cell motility and morphology observed under the bright field microscopy are the only criteria for selecting particular sperm cell during Intracytoplasmic Sperm Injection (ICSI) procedure of Assisted Reproductive Technology (ART). Several factors such as, oxidative stress, cryopreservation, heat, smoking and alcohol consumption, are negatively associated with the quality of sperm cell and fertilization potential due to the changing of sub-cellular structures and functions which are overlooked. A bright field imaging contrast is insufficient to distinguish tiniest morphological cell features that might influence the fertilizing ability of sperm cell. We developed a partially spatially coherent digital holographic microscope (PSC-DHM) for quantitative phase imaging (QPI) in order to distinguish normal sperm cells from sperm cells under different stress conditions such as cryopreservation, exposure to hydrogen peroxide and ethanol without any labeling. Phase maps of 10,163 sperm cells (2,400 control cells, 2,750 spermatozoa after cryopreservation, 2,515 and 2,498 cells under hydrogen peroxide and ethanol respectively) are reconstructed using the data acquired from PSC-DHM system. Total of seven feedforward deep neural networks (DNN) were employed for the classification of the phase maps for normal and stress affected sperm cells. When validated against the test dataset, the DNN provided an average sensitivity, specificity and accuracy of 84.88%, 95.03% and 85%, respectively. The current approach DNN and QPI techniques of quantitative information can be applied for further improving ICSI procedure and the diagnostic efficiency for the classification of semen quality in regards to their fertilization potential and other biomedical applications in general.

physics.bio-ph↗

arXiv2019

Photonic-chip assisted correlative light and electron microscopy

Correlative light-electron microscopy (CLEM) unifies the versatility of light microscopy (LM) with the high resolution of electron microscopy (EM), allowing one to zoom into the complex organization of cells. Most CLEM techniques use ultrathin sections, and thus lack the 3D-EM structural information, and focusing on a very restricted field of view. Here, we introduce photonic chip assisted CLEM, enabling multi-modal total internal reflection fluorescence (TIRF) microscopy over large field of view and high precision localization of the target area of interest within EM. The chip-based direct stochastic optical reconstruction microscopy (dSTORM), and 3D high precision correlation of biological processes by focused ion beam-scanning electron microscopy (FIB-SEM) is further demonstrated. The core layer of the photonic chips are used as a substrate to hold, to illuminate and the cladding layer is used to enable high-precision landmarking of the sample through specially designed grid-like numbering systems. The landmarks are fabricated on the cladding of the photonic chips as extruding pillars from the waveguide surface, thus remaining visible for FIB-SEM after resin embedding during sample processing. Using this approach we demonstrate its applicability for tracking the area of interest, imaging the 3D structural organization of nano-sized morphological features on liver sinusoidal endothelial cells such as fenestrations, and correlating specific endo-lysosomal compartments with its cargo protein upon endocytosis. We envisage that photonic chip equipped with landmarks can be used in the future to automatize the work-flow for both LM and EM for high-throughput CLEM, providing the resolution needed for insights into the complex intracellular communication and the relation between morphology and function in health and disease.

physics.optics↗

arXiv2018

Highly stable common-path quantitative phase microscope for biomedical imaging

High temporal stability is the primary requirement of any quantitative phase microscope (QPM) systems for the early stage detection of various human related diseases. The high temporal stability of the system provides accurate measurement of membrane fluctuations of the biological cells, which can be good indicator of various diseases. We developed a single element highly stable common-path QPM system to obtain temporally stable holograms of the biological specimens. With the proposed system, the temporal stability is obtained ~ 15 mrad without using any vibration isolation table. The capability of the proposed system is demonstrated on USAF resolution chart, polystyrene spheres (dia. 4.5 micron) and human red blood cells (RBCs). The membrane fluctuation of healthy human RBCs is further successfully measured and found to be equal to 63 nm. Contrary to its counterparts, present system offers energy efficient, cost effective and simple way of generating object and reference beam for the development of common-path QPM.

physics.optics↗