Lensless Holographic Microscopy Research Articles

Lensless Mueller holographic microscopy with robust noise reduction for multiplane polarization imaging

Lensless holographic microscopy has emerged as a powerful and cost-effective tool for computational imaging, offering high resolution over a large field of view, beneficial for various biological applications. However, conventional approaches can struggle with contrast and accurate visualization of diverse components over the samples, which can directly affect the diagnostic precision of the techniques. Mueller imaging, while offering detailed, stain-free observations of polarized light responses in samples, often has a limited field of view and single plane information. This is due to the use of high NA microscope objectives and generally complex hardware setups, thus narrowing its practical effectiveness. This work introduces a Lensless Mueller Holographic Microscopy (LMHM) system that overcomes these limitations, enabling large field of view, volumetric multi-layer imaging, and Mueller matrix computation using in-line lens-free holography setup. The proposed system provides precision visualization of polarization information in samples, offering high-quality features due to the incorporation of a numerical multi-height Gerchberg-Saxton reconstruction algorithm with additional complex field filtering and a physical rotating diffuser. The proposed LMHM framework is validated with a calibrated USAF 1951 birefringent test target. A multiplane sample containing cloth fiber is utilized to study the LMHM capabilities of imaging volumetric samples. Finally, the LMHM is used to analyze two mice’s brain slices, effectively showcasing this organ’s anatomy. Among other structures in the brain, the proposed method easily allows the visualization of, e.g., the corpus callosum. These results constitute a proof-of-concept evaluation for bioimaging applications.

Quantitative phase imaging verification in large field-of-view lensless holographic microscopy via two-photon 3D printing

Large field-of-view (FOV) microscopic imaging (over 100 mm2) with high lateral resolution (1–2 μm) plays a pivotal role in biomedicine and biophotonics, especially within the label-free regime. Lensless digital holographic microscopy (LDHM) is promising in this context but ensuring accurate quantitative phase imaging (QPI) in large FOV LDHM is challenging. While phantoms, 3D printed by two-photon polymerization (TPP), have facilitated testing small FOV lens-based QPI systems, an equivalent evaluation for lensless techniques remains elusive, compounded by issues such as twin-image and beam distortions, particularly towards the detector’s edges. Here, we propose an application of TPP over large area to examine phase consistency in LDHM. Our research involves fabricating widefield phase test targets with galvo and piezo scanning, scrutinizing them under single-shot twin-image corrupted conditions and multi-frame iterative twin-image minimization scenarios. By measuring the structures near the detector’s edges, we verified LDHM phase imaging errors across the entire FOV, with less than 12% phase value difference between areas. Our findings indicate that TPP, followed by LDHM and Linnik interferometry cross-verification, requires new design considerations for precise large-area photonic manufacturing. This research paves the way for quantitative benchmarking of large FOV lensless phase imaging, enhancing understanding and further development of LDHM technique.

High-resolution lensless holographic microscopy using a physics-aware deep network.

Lensless digital inline holographic microscopy (LDIHM) is an emerging quantitative phase imaging modality that uses advanced computational methods for phase retrieval from the interference pattern. The existing end-to-end deep networks require a large training dataset with sufficient diversity to achieve high-fidelity hologram reconstruction. To mitigate this data requirement problem, physics-aware deep networks integrate the physics of holography in the loss function to reconstruct complex objects without needing prior training. However, the data fidelity term measures the data consistency with a single low-resolution hologram without any external regularization, which results in a low performance on complex biological data. We aim to mitigate the challenges with trained and physics-aware untrained deep networks separately and combine the benefits of both methods for high-resolution phase recovery from a single low-resolution hologram in LDIHM. We propose a hybrid deep framework (HDPhysNet) using a plug-and-play method that blends the benefits of trained and untrained deep models for phase recovery in LDIHM. The high-resolution phase is generated by a pre-trained high-definition generative adversarial network (HDGAN) from a single low-resolution hologram. The generated phase is then plugged into the loss function of a physics-aware untrained deep network to regulate the complex object reconstruction process. Simulation results show that the SSIM of the proposed method is increased by 0.07 over the trained and 0.04 over the untrained deep networks. The average phase-SNR is elevated by 8.2dB over trained deep models and 9.8dB over untrained deep networks on the experimental biological cells (cervical cells and red blood cells). We showed improved performance of the HDPhysNet against the unknown perturbation in the imaging parameters such as the propagation distance, the wavelength of the illuminating source, and the imaging sample compared with the trained network (HDGAN). LDIHM, combined with HDPhysNet, is a portable and technology-driven microscopy best suited for point-of-care cytology applications.

Averaging fractional Fourier domains for background noise removal applied to digital lensless holographic microscopy

A background noise removal method based on averaging fractional Fourier domains is presented. The method is applied to Digital Lensless Holographic Microscopy (DLHM) intensity reconstructions, where the background is perturbed by the weak yet detrimental presence of information of the twin image. A set of fractional Fourier domains of a DLHM intensity reconstruction is computed and thereafter averaged leading to a sensible reduction of the background noise and, therefore, an increase in the overall contrast of the resulting image. The maximum reach of the fractional rotations used in the method is determined by measuring the spatial resolution in a regular star test target such that the spatial resolution is kept within the span of interest for a given application. The set of images to be averaged is composed of fractional rotations of the original intensity reconstruction that are smaller than the previously determined maximum reach. The number of fractional rotations that are finally averaged is determined by the sought increase in the contrast of the resulting image. Experimental samples of micrometer-sized objects and an intricate biological specimen have been used to validate the proposal.

Portable Digital Lensless Holographic Microscope for fieldwork applications using a conventional smartphone sensor and a customized freeform lens

Portable Digital Lensless Holographic Microscope for fieldwork applications using a conventional smartphone sensor and a customized freeform lens

Off-axis digital lensless holographic microscopy based on spatially multiplexed interferometry.

Digital holographic microscopy (DHM) is a label-free microscopy technique that provides time-resolved quantitative phase imaging (QPI) by measuring the optical path delay of light induced by transparent biological samples. DHM has been utilized for various biomedical applications, such as cancer research and sperm cell assessment, as well as for in vitro drug or toxicity testing. Its lensless version, digital lensless holographic microscopy (DLHM), is an emerging technology that offers size-reduced, lightweight, and cost-effective imaging systems. These features make DLHM applicable, for example, in limited resource laboratories, remote areas, and point-of-care applications. In addition to the abovementioned advantages, in-line arrangements for DLHM also include the limitation of the twin-image presence, which can restrict accurate QPI. We therefore propose a compact lensless common-path interferometric off-axis approach that is capable of quantitative imaging of fast-moving biological specimens, such as living cells in flow. We suggest lensless spatially multiplexed interferometric microscopy (LESSMIM) as a lens-free variant of the previously reported spatially multiplexed interferometric microscopy (SMIM) concept. LESSMIM comprises a common-path interferometric architecture that is based on a single diffraction grating to achieve digital off-axis holography. From a series of single-shot off-axis holograms, twin-image free and time-resolved QPI is achieved by commonly used methods for Fourier filtering-based reconstruction, aberration compensation, and numerical propagation. Initially, the LESSMIM concept is experimentally demonstrated by results from a resolution test chart and investigations on temporal stability. Then, the accuracy of QPI and capabilities for imaging of living adherent cell cultures is characterized. Finally, utilizing a microfluidic channel, the cytometry of suspended cells in flow is evaluated. LESSMIM overcomes several limitations of in-line DLHM and provides fast time-resolved QPI in a compact optical arrangement. In summary, LESSMIM represents a promising technique with potential biomedical applications for fast imaging such as in imaging flow cytometry or sperm cell analysis.

Multi-view occlusion removal in digital lensless holographic microscopy

Multi-view occlusion removal in digital lensless holographic microscopy

Artifacts-free lensless on-chip tomography empowered by three-dimensional deconvolution

A lensless holographic microscope based on in-line holograms and optical diffraction tomography is an ideal imaging system for label-free 3D biological samples and can achieve large-volume imaging with single-cell resolution in a convenient way. However, due to the phase information loss and the missing cone problem, the imaging quality is significantly degraded by the reconstructed artifacts of twin images and out-of-focus images, which severely hinders the identification and interpretation of the objects. We propose an artifacts-free lensless on-chip tomography certified by three-dimensional deconvolution, which facilitates the extraction of real object morphology through straightforward yet effective computation. Initially, a globally valid systemic point spread function (PSF) is generated by simulating the imaging output of an ideal point light source positioned at the origin of the object space coordinate. Subsequently, an iterative three-dimensional deconvolution process is applied to the primitive imaging outcome of the lensless on-chip tomography using this PSF. Through rapid iterations, the optimized imaging result is swiftly obtained. Both the simulated and experimental results indicate that the artifacts-free lensless on-chip tomography can effectively circumvent the reconstructed artifacts and retrieve the real object morphology, which is critical for detailed observation and further quantitative analysis. In addition, we anticipate that the proposed approach has the potential to be transferred to other 3D imaging systems in systemic artifacts removal after corresponding modifications.

Phase retrieval via conjugate gradient minimization in double-plane lensless holographic microscopy

Optimization-based phase retrieval method for digital lensless holographic microscopy in the double-plane recording configuration is proposed. In our method the phase retrieval is framed as an optimization problem that can be efficiently and rigorously tackled with gradient decent tools. This is done with the conjugate gradient method that possesses excellent theoretical features such as global and fast convergence (compared to steepest descent) and relatively low computational cost (compared to second order optimizers). The proposed method is extensively tested with simulations and experimental measurements that show superiority of our method over the Gerchberg-Saxton algorithm, especially in terms of reconstruction of problematic low frequency components of viable phase information.

Miniaturized quantitative detection of particles in transformer oil based on lensless holographic microscopy

The insulation effectiveness of transformer oil has a substantial impact on the safety of transformers. In the presence of particles within the transformer oil, they are able to adversely affect its performance, potentially originating from electrical arcs or severe carbonization processes. These particles are commonly capable of reducing the insulating capability of the transformer oil, thereby enhancing the risk of transformer failure. To overcome this dilemma, we propose a miniaturized quantitative particle detection approach in transformer oil based on a lensless digital holographic microscope. In general, methods based on traditional high-resolution optical microscopy are essentially limited by their fairly huge cost and importability. However, the proposed approach utilizes a homemade miniaturized model combined with a CMOS image sensor to achieve microscopic imaging. The deployed device here weighs ∼ 50 g, making it portable and affordable for on-the-go detection. By simplifying the microscope system, we can quickly and accurately detect tiny particles in transformer oils. This enables us to efficiently assess the status of impurities in power transformers and to identify potential issues in advance, thereby enhancing the reliability of transformer oil. This detection method not only features simplicity of operation, convenient on-the-go detection, low cost, and high precision but also provides a convenient approach for accurately assessing the condition of impurity particles in power transformers and preemptively identifying potential issues.

Clinical and Biomedical Applications of Lensless Holographic Microscopy

AbstractMany clinical procedures and biomedical research workflows rely on microscopy, including diagnosis of cancer, genetic disorders, autoimmune diseases, infections, and quantification of cell culture. Despite its widespread use, traditional image acquisition and review by trained microscopists is often lengthy and expensive, limited to large hospitals or laboratories, precluding use in point‐of‐care settings. In contrast, lensless or lensfree holographic microscopy (LHM) is inexpensive and widely deployable because it can achieve performance comparable to expensive and bulky objective‐based benchtop microscopes while relying on components that cost only a few hundred dollars or less. Lab‐on‐a‐chip integration is practical and enables LHM to be combined with single‐cell isolation, sample mixing, and in‐incubator imaging. Additionally, many manual tasks in conventional microscopy are instead computational in LHM, including image focusing, stitching, and classification. Furthermore, LHM offers a field of view hundreds of times greater than that of conventional microscopy without sacrificing resolution. Here, the basic LHM principles are summarized, as well as recent advances in artificial intelligence integration and enhanced resolution. How LHM is applied to the above clinical and biomedical applications is discussed in detail. Finally, emerging clinical applications, high‐impact areas for future research, and some current challenges facing widespread adoption are identified.

Comparative analysis of digital holographic microscopy and digital lensless holographic microscopy for quantitative phase imaging

This study provides a detailed comparison of two widely used quantitative phase imaging (QPI) techniques: single-shot off-axis digital holographic microscopy (DHM) and digital lensless holographic microscopy (DLHM). The primary aim is to evaluate and contrast critical aspects of their imaging performance, including spatial phase sensitivity, phase measurement accuracy, and spatial lateral resolution. Employing typical configurations for both DHM and DLHM, the study utilizes a customized phase test target featuring linear phase changes introduced by a specially designed linear density attenuation filter. Ground truth data from an atomic force microscope is incorporated to validate the experimental findings. The comparative analysis reveals that DHM and DLHM exhibit nearly identical spatial phase sensitivity, with DHM demonstrating a minimal 3.2% measurement error compared to DLHM's 4% in height measurement accuracy. Notably, DHM achieves a finer spatial lateral resolution down to 3.1 µm, surpassing DLHM's 5.52 µm. While DHM outperforms DLHM in precision and resolution, the latter offers advantages in terms of portability and cost-effectiveness. These findings provide valuable insights for researchers and practitioners, aiding in the informed selection of QPI methods based on specific application requirements.

Open-access database for digital lensless holographic microscopy and its application on the improvement of deep-learning-based autofocusing models.

Among modern optical microscopy techniques, digital lensless holographic microscopy (DLHM) is one of the simplest label-free coherent imaging approaches. However, the hardware simplicity provided by the lensless configuration is often offset by the demanding computational postprocessing required to match the retrieved sample information to the user's expectations. A promising avenue to simplify this stage is the integration of artificial intelligence and machine learning (ML) solutions into the DLHM workflow. The biggest challenge to do so is the preparation of an extensive and high-quality experimental dataset of curated DLHM recordings to train ML models. In this work, a diverse, open-access dataset of DLHM recordings is presented as support for future research, contributing to the data needs of the applied research community. The database comprises 11,760 experimental DLHM holograms of bio and non-bio samples with diversity on the main recording parameters of the DLHM architecture. The database is divided into two datasets of 10 independent imaged samples. The first group, named multi-wavelength dataset, includes 8160 holograms and was recorded using laser diodes emitting at 654nm, 510nm, and 405nm; the second group, named single-wavelength dataset, is composed of 3600 recordings and was acquired using a 633nm He-Ne laser. All the experimental parameters related to the dataset acquisition, preparation, and calibration are described in this paper. The advantages of this large dataset are validated by re-training an existing autofocusing model for DLHM and as the training set for a simpler architecture that achieves comparable performance, proving its feasibility for improving existing ML-based models and the development of new ones.

Seeing cells without a lens: Compact 3D digital lensless holographic microscopy for wide-field imaging

Optical microscopy is an essential tool for biomedical discoveries and cell diagnosis at micro- to nano-scales. However, conventional microscopes rely on lenses to record 2-D images of samples, which limits in-depth inspection of large volumes of cells. This research project implements a novel 3-D lensless microscopic imaging system that achieves a wide field of view, high resolution, and an extremely compact, cost-effective design: the Digital Lensless Holographic Microscope (DLHM).A lensless holographic microscope is built with only a light source, a sample, and an imaging chip (with other non-essential supporting structures). The entire setup costs $500 to $600. A series of MATLAB-based algorithms were designed to reconstruct phase information of samples simultaneously from the recorded hologram with built-in high-resolution and phase unwrapping functions. This produces 3-D images of cell samples. The 3-D cell reconstruction of biological samples maintained a comparable resolution with conventional optical microscopes while covering a field of view of 36.2 mm2, which is 20-30 times larger. While most microscopes are extremely time-consuming and require professional expertise, the lensless holographic microscope is portable, low-cost, high-stability, and extremely simple. This makes it accessible for point-of-care testing (POCT) to a broader coverage, including developing regions with limited medical facilities.

Teaching Image Processing and Optical Engineering to University Biology Students

ABSTRACT Biophysics is an interdisciplinary pursuit requiring researchers with knowledge and skills in several areas. Optical instruments and computers are fundamental tools in biophysics research to collect and analyze data. We developed a 1-semester Optical Engineering Laboratory course to teach image processing, optical engineering, and research skills to undergraduate students majoring in biology and biochemistry. With the use of development systems on students' laptops and in the cloud, students learned image processing with Python and OpenCV. Each student constructed a microprocessor-based lensless holographic microscope, gaining hands-on experience with optical engineering. The class culminated in original, student-designed research projects. All lectures, hands-on labs, and student research projects were performed both in person and remotely, in response to the COVID-19 pandemic.

Image enhancement and field of view enlargement in digital lensless holographic microscopy by multi-shot imaging.

A method to improve the quality of reconstructed images while the field of view (FOV) is enlarged in digital lensless holographic microscopy (DLHM) is presented. Multiple DLHM holograms are recorded while a still sample is located at different places of the plane containing it. The different locations of the sample must produce a set of DLHM holograms that share an overlapped area with a fixed DLHM hologram. The relative displacement among multiple DLHM holograms is computed by means of a normalized cross-correlation. The value of the computed displacement is utilized to produce a new DLHM hologram resulting from the coordinated addition of multi-shot DLHM holograms with the corresponding compensated displacement. The composed DLHM hologram carries enhanced information of the sample in a larger format, leading to a reconstructed image with improved quality and larger FOV. The feasibility of the method is illustrated and validated with results obtained from imaging a calibration test target and a biological specimen.

FocusNET: An autofocusing learning‐based model for digital lensless holographic microscopy

This paper reports on a convolutional neural network (CNN) – based regression model, called FocusNET, to predict the accurate reconstruction distance of raw holograms in Digital Lensless Holographic Microscopy (DLHM). This proposal provides a physical-mathematical formulation to extend its use to different DLHM setups than the optical and geometrical conditions utilized for recording the training dataset; this unique feature is tested by applying the proposal to holograms of diverse samples recorded with different DLHM setups. Additionally, a comparison between FocusNET and conventional autofocusing methods in terms of processing times and accuracy is provided. Although the proposed method predicts reconstruction distances with approximately 54 µm standard deviation, accurate information about the samples in the validation dataset is still retrieved. When compared to a method that utilizes a stack of reconstructions to find the best focal plane, FocusNET performs 600 times faster, as no hologram reconstruction is needed. When implemented in batches, the network can achieve up to a 1200-fold reduction in processing time, depending on the number of holograms to be processed. The training and validation datasets, and the code implementations, are hosted on a public GitHub repository that can be freely accessed.

Robust and compact digital Lensless Holographic microscope for Label-Free blood smear imaging

The lack of equipped healthcare infrastructure in isolated hard-to-reach zones exposes their population to a higher risk of complications in common diseases. With a timely diagnosis setting a life-altering difference, worldwide efforts have been conducted for the development of point-of-care testing (PoCT) with cost-effective devices. Among the most common interests in PoCT is the analysis of blood smear samples, as they can help to detect, diagnose, and monitor a wide range of diseases and disorders. With microscopy being the traditional tool for these analyses, a significative advance has been the development of cost-effective digital holographic microscopy systems, driven in part by its label-free imaging capabilities that waive the need for any sample preprocessing. Here, a robust and portable digital lensless holographic microscope, functionalized for the analysis of non-preprocessed blood smear samples in PoCT environments, is presented, and its viability is tested in the observation of red blood cells. The device uses an optical fiber with a cone-shaped tip instead of a pinhole, which ensures the sturdiness of the system and eliminates the need for challenging alignment. While the distances of the microscope can be tuned before fabrication, the herein-reported operational parameters are functionalized for the specific analysis of blood samples.

Multi-Illumination Single-Holographic-Exposure Lensless Fresnel (MISHELF) Microscopy: Principles and Biomedical Applications.

Lensless holographic microscopy (LHM) comes out as a promising label-free technique since it supplies high-quality imaging and adaptive magnification in a lens-free, compact and cost-effective way. Compact sizes and reduced prices of LHMs make them a perfect instrument for point-of-care diagnosis and increase their usability in limited-resource laboratories, remote areas, and poor countries. LHM can provide excellent intensity and phase imaging when the twin image is removed. In that sense, multi-illumination single-holographic-exposure lensless Fresnel (MISHELF) microscopy appears as a single-shot and phase-retrieved imaging technique employing multiple illumination/detection channels and a fast-iterative phase-retrieval algorithm. In this contribution, we review MISHELF microscopy through the description of the principles, the analysis of the performance, the presentation of the microscope prototypes and the inclusion of the main biomedical applications reported so far.

Lensless holographic microscope with a time and memory-saving algorithm for large-volume imaging of organoids.

Organoids, the 3D culture systems derived from stem cells, are promising models for human organs. However, organoid study requires large-volume imaging with single cell resolution, which is beyond the spatial bandwidth limit of conventional optical microscopy. Herein, we propose a lensless holographic microscope empowered with a time and memory-saving algorithm. It solves the trade-off between the imaging field of view, resolution, and processing speed, and provides a practical tool for the study of organoids. We first build a compact microscopy system using a multi-angle LED illumination scheme and an on-chip structure. Then we develop a fast angular spectrum formula for fast reconstruction of oblique-illuminated coaxial holography under the under-sampling condition. Additionally, we derive a multi-angle illuminated filtered backpropagation algorithm to achieve high-precision and slice-wise recovery of 3D structures of objects. The reconstruction process demands only 1/50 of the memory required by a traditional optical diffraction tomography algorithm. Experimental results indicate that the proposed method can achieve 6.28 mm × 4.71 mm × 0.37 mm volume imaging within 104 s. Through the standardized polystyrene beads test, we demonstrate that the proposed microscope has micrometer-scale resolution in both lateral and axial directions. In addition, the 3D imaging results of salivary gland organoids show great application prospects of the proposed method in the field of living biological sampling imaging.