Lensless Imaging and Sensing

Optical Image Sensors for Smart Analytical Chemiluminescence Biosensors

Optical biosensors have emerged as a powerful tool in analytical biochemistry, offering high sensitivity and specificity in the detection of various biomolecules. This article explores the advancements in the integration of optical biosensors with microfluidic technologies, creating lab-on-a-chip (LOC) platforms that enable rapid, efficient, and miniaturized analysis at the point of need. These LOC platforms leverage optical phenomena such as chemiluminescence and electrochemiluminescence to achieve real-time detection and quantification of analytes, making them ideal for applications in medical diagnostics, environmental monitoring, and food safety. Various optical detectors used for detecting chemiluminescence are reviewed, including single-point detectors such as photomultiplier tubes (PMT) and avalanche photodiodes (APD), and pixelated detectors such as charge-coupled devices (CCD) and complementary metal-oxide-semiconductor (CMOS) sensors. A significant advancement discussed in this review is the integration of optical biosensors with pixelated image sensors, particularly CMOS image sensors. These sensors provide numerous advantages over traditional single-point detectors, including high-resolution imaging, spatially resolved measurements, and the ability to simultaneously detect multiple analytes. Their compact size, low power consumption, and cost-effectiveness further enhance their suitability for portable and point-of-care diagnostic devices. In the future, the integration of machine learning algorithms with these technologies promises to enhance data analysis and interpretation, driving the development of more sophisticated, efficient, and accessible diagnostic tools for diverse applications.

Lens-free on-chip 3D microscopy based on wavelength-scanning Fourier ptychographic diffraction tomography

Lens-free on-chip microscopy is a powerful and promising high-throughput computational microscopy technique due to its unique advantage of creating high-resolution images across the full field-of-view (FOV) of the imaging sensor. Nevertheless, most current lens-free microscopy methods have been designed for imaging only two-dimensional thin samples. Lens-free on-chip tomography (LFOCT) with a uniform resolution across the entire FOV and at a subpixel level remains a critical challenge. In this paper, we demonstrated a new LFOCT technique and associated imaging platform based on wavelength scanning Fourier ptychographic diffraction tomography (wsFPDT). Instead of using angularly-variable illuminations, in wsFPDT, the sample is illuminated by on-axis wavelength-variable illuminations, ranging from 430 to 1200 nm. The corresponding under-sampled diffraction patterns are recorded, and then an iterative ptychographic reconstruction procedure is applied to fill the spectrum of the three-dimensional (3D) scattering potential to recover the sample's 3D refractive index (RI) distribution. The wavelength-scanning scheme not only eliminates the need for mechanical motion during image acquisition and precise registration of the raw images but secures a quasi-uniform, pixel-super-resolved imaging resolution across the entire imaging FOV. With wsFPDT, we demonstrate the high-throughput, billion-voxel 3D tomographic imaging results with a half-pitch lateral resolution of 775 nm and an axial resolution of 5.43 μm across a large FOV of 29.85 mm2 and an imaging depth of >200 μm. The effectiveness of the proposed method was demonstrated by imaging various types of samples, including micro-polystyrene beads, diatoms, and mouse mononuclear macrophage cells. The unique capability to reveal quantitative morphological properties, such as area, volume, and sphericity index of single cell over large cell populations makes wsFPDT a powerful quantitative and label-free tool for high-throughput biological applications.

Roadmap on computational methods in optical imaging and holography [invited]

Computational methods have been established as cornerstones in optical imaging and holography in recent years. Every year, the dependence of optical imaging and holography on computational methods is increasing significantly to the extent that optical methods and components are being completely and efficiently replaced with computational methods at low cost. This roadmap reviews the current scenario in four major areas namely incoherent digital holography, quantitative phase imaging, imaging through scattering layers, and super-resolution imaging. In addition to registering the perspectives of the modern-day architects of the above research areas, the roadmap also reports some of the latest studies on the topic. Computational codes and pseudocodes are presented for computational methods in a plug-and-play fashion for readers to not only read and understand but also practice the latest algorithms with their data. We believe that this roadmap will be a valuable tool for analyzing the current trends in computational methods to predict and prepare the future of computational methods in optical imaging and holography.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00340-024-08280-3.

Quantification of early biofilm growth in microtiter plates through a novel image analysis software

Given the significant impact of biofilms on human health and material corrosion, research in this field urgently needs more accessible techniques to facilitate the testing of new control agents and general understanding of biofilm biology. Microtiter plates offer a convenient format for standardized evaluations, including high-throughput assays of alternative treatments and molecular modulators. This study introduces a novel Biofilm Analysis Software (BAS) for quantifying biofilms from microtiter plate images. We focused on early biofilm growth stages and compared BAS quantification to common techniques: direct turbidity measurement, intrinsic fluorescence detection linked to pyoverdine production, and standard crystal violet staining which enables image analysis and optical density measurement. We also assessed their sensitivity for detecting subtle growth effects caused by cyclic AMP and gentamicin. Our results show that BAS image analysis is at least as sensitive as the standard method of spectrophotometrically quantifying the crystal violet retained by biofilms. Furthermore, we demonstrated that bacteria adhered after short incubations (from 10 min to 4 h), isolated from planktonic populations by a simple rinse, can be monitored until their growth is detectable by intrinsic fluorescence, BAS analysis, or resolubilized crystal violet. These procedures are widely accessible for many laboratories, including those with limited resources, as they do not require a spectrophotometer or other specialized equipment.

Robust 3D phase retrieval via compressed support detection from snapshot diffraction pattern

Traditional multislice iterative phase retrieval (MIPR) from snapshot two-dimensional measurements suffers from the two limitations of pre-defined support and iterative stagnation. To eliminate the requirements for priori knowledge of support masks, this paper proposes a multislice iterative phase retrieval algorithm based on compressed support detection and hybrid input-output algorithm (CSD-MIPR-HIO). The CSD-MIPR-HIO algorithm firstly uses compressed support detection to adaptively detect the support masks of each plane from single 2D diffraction intensity, and then uses a hybrid input-output (HIO) iterative algorithm for MIPR. The proposed method breaks the limitations of traditional MIPR algorithms on priori knowledge of support masks and achieve high-quality reconstruction in noisy environments. Numerical and optical experiments confirm the feasibility, superiority, and robustness of our proposed CSD-MIPR-HIO method.

Extended depth of field for Fresnel zone aperture camera via fast passive depth estimation

The lensless camera with incoherent illumination has gained significant research interest for its thin and flexible structure. However, it faces challenges in resolving scenes with a wide depth of field (DoF) due to its depth-dependent point spread function (PSF). In this paper, we present a single-shot method for extending the DoF in Fresnel zone aperture (FZA) cameras at visible wavelengths through passive depth estimation. The improved ternary search method is utilized to determine the depth of targets rapidly by evaluating the sharpness of the back propagation reconstruction. Based on the depth estimation results, a set of reconstructed images focused on targets at varying depths are derived from the encoded image. After that, the DoF is extended through focus stacking. The experimental results demonstrate an 8-fold increase compared with the calibrated DoF at 130 mm depth. Moreover, our depth estimation method is five times faster than the traversal method, while maintaining the same level of accuracy. The proposed method facilitates the development of lensless imaging in practical applications such as photography, microscopy, and surveillance.

Fourier Ptychographic Microscopy 10 Years on: A Review

Fourier ptychographic microscopy (FPM) emerged as a prominent imaging technique in 2013, attracting significant interest due to its remarkable features such as precise phase retrieval, expansive field of view (FOV), and superior resolution. Over the past decade, FPM has become an essential tool in microscopy, with applications in metrology, scientific research, biomedicine, and inspection. This achievement arises from its ability to effectively address the persistent challenge of achieving a trade-off between FOV and resolution in imaging systems. It has a wide range of applications, including label-free imaging, drug screening, and digital pathology. In this comprehensive review, we present a concise overview of the fundamental principles of FPM and compare it with similar imaging techniques. In addition, we present a study on achieving colorization of restored photographs and enhancing the speed of FPM. Subsequently, we showcase several FPM applications utilizing the previously described technologies, with a specific focus on digital pathology, drug screening, and three-dimensional imaging. We thoroughly examine the benefits and challenges associated with integrating deep learning and FPM. To summarize, we express our own viewpoints on the technological progress of FPM and explore prospective avenues for its future developments.

Modulation of Point Spread Function for Super-Resolution Imaging

High image resolution is desired in wave-related areas such as ultrasound, acoustics, optics, and electromagnetics. However, the spatial resolution of an imaging system is limited by the spatial frequency of the point spread function (PSF) of the system due to diffraction. In this article, the PSF is modulated in amplitude, phase, or both to increase the spatial frequency to reconstruct super-resolution images of objects or wave sources/fields, where the modulator can be a focused shear wave produced remotely by, for example, a radiation force from a focused Bessel beam or X-wave, or can be a small particle manipulated remotely by a radiation-force (such as acoustic and optical tweezers) or electrical and magnetic forces. A theory of the PSF-modulation method was developed, and computer simulations and experiments were conducted. The result of an ultrasound experiment shows that a pulse-echo (two-way) image reconstructed has a super-resolution (0.65 mm) as compared to the diffraction limit (2.65 mm) using a 0.5-mm-diameter modulator at 1.483-mm wavelength, and the signal-to-noise ratio (SNR) of the image was about 31 dB. If the minimal SNR of a "visible" image is 3, the resolution can be further increased to about 0.19 mm by decreasing the size of the modulator. Another ultrasound experiment shows that a wave source was imaged (one-way) at about 30-dB SNR using the same modulator size and wavelength above. The image clearly separated two 0.5-mm spaced lines, which gives a 7.26-fold higher resolution than that of the diffraction limit (3.63 mm). Although, in theory, the method has no limit on the highest achievable image resolution, in practice, the resolution is limited by noises. Also, a PSF-weighted super-resolution imaging method based on the PSF-modulation method was developed. This method is easier to implement but may have some limitations. Finally, the methods above can be applied to imaging systems of an arbitrary PSF and can produce 4-D super-resolution images. With a proper choice of a modulator (e.g., a quantum dot) and imaging system, nanoscale (a few nanometers) imaging is possible.

Optical imaging for screening and early cancer diagnosis in low-resource settings

Low-cost optical imaging technologies have the potential to reduce inequalities in healthcare by improving the detection of pre-cancer or early cancer and enabling more effective and less invasive treatment. In this Review, we summarise technologies for in vivo widefield, multi-spectral, endoscopic, and high-resolution optical imaging that could offer affordable approaches to improve cancer screening and early detection at the point-of-care. Additionally, we discuss approaches to slide-free microscopy, including confocal imaging, lightsheet microscopy, and phase modulation techniques that can reduce the infrastructure and expertise needed for definitive cancer diagnosis. We also evaluate how machine learning-based algorithms can improve the accuracy and accessibility of optical imaging systems and provide real-time image analysis. To achieve the potential of optical technologies, developers must ensure that devices are easy to use; the optical technologies must be evaluated in multi-institutional, prospective clinical tests in the intended setting; and the barriers to commercial scale-up in under-resourced markets must be overcome. Therefore, test developers should view the production of simple and effective diagnostic tools that are accessible and affordable for all countries and settings as a central goal of their profession.

Label-Free CD34+ Cell Identification Using Deep Learning and Lens-Free Shadow Imaging Technology

Accurate and efficient classification and quantification of CD34+ cells are essential for the diagnosis and monitoring of leukemia. Current methods, such as flow cytometry, are complex, time-consuming, and require specialized expertise and equipment. This study proposes a novel approach for the label-free identification of CD34+ cells using a deep learning model and lens-free shadow imaging technology (LSIT). LSIT is a portable and user-friendly technique that eliminates the need for cell staining, enhances accessibility to nonexperts, and reduces the risk of sample degradation. The study involved three phases: sample preparation, dataset generation, and data analysis. Bone marrow and peripheral blood samples were collected from leukemia patients, and mononuclear cells were isolated using Ficoll density gradient centrifugation. The samples were then injected into a cell chip and analyzed using a proprietary LSIT-based device (Cellytics). A robust dataset was generated, and a custom AlexNet deep learning model was meticulously trained to distinguish CD34+ from non-CD34+ cells using the dataset. The model achieved a high accuracy in identifying CD34+ cells from 1929 bone marrow cell images, with training and validation accuracies of 97.3% and 96.2%, respectively. The customized AlexNet model outperformed the Vgg16 and ResNet50 models. It also demonstrated a strong correlation with the standard fluorescence-activated cell sorting (FACS) technique for quantifying CD34+ cells across 13 patient samples, yielding a coefficient of determination of 0.81. Bland-Altman analysis confirmed the model's reliability, with a mean bias of -2.29 and 95% limits of agreement between 18.49 and -23.07. This deep-learning-powered LSIT offers a groundbreaking approach to detecting CD34+ cells without the need for cell staining, facilitating rapid CD34+ cell classification, even by individuals without prior expertise.

Non-interferometric stand-alone single-shot holographic camera using reciprocal diffractive imaging

An ideal holographic camera measures the amplitude and phase of the light field so that the focus can be numerically adjusted after the acquisition, and depth information about an imaged object can be deduced. The performance of holographic cameras based on reference-assisted holography is significantly limited owing to their vulnerability to vibration and complex optical configurations. Non-interferometric holographic cameras can resolve these issues. However, existing methods require constraints on an object or measurement of multiple-intensity images. In this paper, we present a holographic image sensor that reconstructs the complex amplitude of scattered light from a single-intensity image using reciprocal diffractive imaging. We experimentally demonstrate holographic imaging of three-dimensional diffusive objects and suggest its potential applications by imaging a variety of samples under both static and dynamic conditions.

Lensfree Air-Quality Monitoring of Fine and Ultrafine Particulate Matter Using Vapor-Condensed Nanolenses

Current commercial air-quality monitoring devices lack a large dynamic range, especially at the small, ultrafine size scale. Furthermore, there is a low density of air-quality monitoring stations, reducing the precision with which local particulate matter hazards can be tracked. Here, we show a low-cost, lensfree, and portable air-quality monitoring device (LPAQD) that can detect and measure micron-sized particles down to 100 nm-sized particles, with the capability to track and measure particles in real time throughout a day and the ability to accurately measure particulate matter densities as low as 3 μg m-3. A vapor-condensed film is deposited onto the coverslip used to collect particles before the LPAQD is deployed at outdoor monitoring sites. The vapor-condensed film increases the scattering cross section of particles smaller than the pixel size, enabling the sub-pixel and sub-diffraction-limit-sized particles to be detected. The high dynamic range, low cost, and portability of this device can enable citizens to monitor their own air quality to hopefully impact user decisions that reduce the risk for particulate matter-related diseases.

Thin and Scalable Hybrid Emission Filter via Plasma Etching for Low-Invasive Fluorescence Detection

Hybrid emission filters, comprising an interference filter and an absorption filter, exhibit high excitation light rejection performance and can act as lensless fluorescent devices. However, it has been challenging to produce them in large batches over a large area. In this study, we propose and demonstrate a method for transferring a Si substrate, on which the hybrid filter is deposited, onto an image sensor by attaching it to the sensor and removing the substrate via plasma etching. Through this method, we can transfer uniform filters onto fine micrometer-sized needle devices and millimeter-sized multisensor chips. Optical evaluation reveals that the hybrid filter emits light in the 500 to 560 nm range, close to the emission region of green fluorescent protein (GFP). Furthermore, by observing the fluorescence emission from the microbeads, a spatial resolution of 12.11 μm is calculated. In vitro experiments confirm that the fabricated device is able to discriminate GFP emission patterns from brain slices.

Optofluidic imaging meets deep learning: from merging to emerging

Propelled by the striking advances in optical microscopy and deep learning (DL), the role of imaging in lab-on-a-chip has dramatically been transformed from a silo inspection tool to a quantitative "smart" engine. A suite of advanced optical microscopes now enables imaging over a range of spatial scales (from molecules to organisms) and temporal window (from microseconds to hours). On the other hand, the staggering diversity of DL algorithms has revolutionized image processing and analysis at the scale and complexity that were once inconceivable. Recognizing these exciting but overwhelming developments, we provide a timely review of their latest trends in the context of lab-on-a-chip imaging, or coined optofluidic imaging. More importantly, here we discuss the strengths and caveats of how to adopt, reinvent, and integrate these imaging techniques and DL algorithms in order to tailor different lab-on-a-chip applications. In particular, we highlight three areas where the latest advances in lab-on-a-chip imaging and DL can form unique synergisms: image formation, image analytics and intelligent image-guided autonomous lab-on-a-chip. Despite the on-going challenges, we anticipate that they will represent the next frontiers in lab-on-a-chip imaging that will spearhead new capabilities in advancing analytical chemistry research, accelerating biological discovery, and empowering new intelligent clinical applications.

Development of a Lensless Radiomicroscope for Cellular-Resolution Radionuclide Imaging

The action of radiopharmaceuticals takes place at the level of cells. However, existing radionuclide assays can only measure uptake in bulk or in small populations of single cells. This potentially hinders the development of effective radiopharmaceuticals for disease detection, staging, and treatment. Methods: We have developed a new imaging modality, the lensless radiomicroscope (LRM), for in vitro, cellular-resolution imaging of β- and α-emitting radionuclides. The palm-sized instrument is constructed from off-the-shelf parts for a total cost of less than $100, about 500 times less than the radioluminescence microscope, its closest equivalent. The instrument images radiopharmaceuticals by direct detection of ionizing charged particles via a consumer-grade complementary metal-oxide semiconductor detector. Results: The LRM can simultaneously image more than 5,000 cells within its 1 cm2 field of view, a 100-times increase over state-of-the-art technology. It has spatial resolution of 5 μm for brightfield imaging and 30 μm for 18F positron imaging. We used the LRM to quantify 18F-FDG uptake in MDA-MB-231 breast cancer cells 72 h after radiation treatment. Cells receiving 3 Gy were 3 times larger (mean = 3,116 μm2) than their untreated counterparts (mean = 940 μm2) but had 2 times less 18F-FDG per area (mean = 217 Bq/mm2), a finding in agreement with the clinical use of this tracer to monitor response. Additionally, the LRM was used to dynamically image the uptake of 18F-FDG by live cancer cells, and thus measure their avidity for glucose. Conclusion: The LRM is a high-resolution, large-field-of-view, and cost-effective approach to image radiotracer uptake with single-cell resolution in vitro.

Holographic PIV/PTV for nano flow rates-A study in the 70 to 200 nL/min range

Accurately measuring flow rates is a key requirement in many medical applications such as infusion and drug delivery systems. A major drawback of current systems is the low resolution of the sensors in the low flow rate regime. In this article, we present a method based on Holographic PIV/PTV that has been used for the first time to measure flow rates in the range of a few nL/min. Our method requires a very simple setup that combines lensless holography with particle velocimetry. For flow rates in the 70 to 200 nL/min range, the highest uncertainty was 5.6% (coverage factor k=2). This is an open-source project; the CAD designs and software source code are available at https://github.com/gui-miotto/holovel.

Portable, Automated and Deep-Learning-Enabled Microscopy for Smartphone-Tethered Optical Platform Towards Remote Homecare Diagnostics: A Review

Globally new pandemic diseases induce urgent demands for portable diagnostic systems to prevent and control infectious diseases. Smartphone-based portable diagnostic devices are significantly efficient tools to user-friendly connect personalized health conditions and collect valuable optical information for rapid diagnosis and biomedical research through at-home screening. Deep learning algorithms for portable microscopes also help to enhance diagnostic accuracy by reducing the imaging resolution gap between benchtop and portable microscopes. This review highlighted recent progress and continued efforts in a smartphone-tethered optical platform through portable, automated, and deep-learning-enabled microscopy for personalized diagnostics and remote monitoring. In detail, the optical platforms through smartphone-based microscopes and lens-free holographic microscopy are introduced, and deep learning-based portable microscopic imaging is explained to improve the image resolution and accuracy of diagnostics. The challenges and prospects of portable optical systems with microfluidic channels and a compact microscope to screen COVID-19 in the current pandemic are also discussed. It has been believed that this review offers a novel guide for rapid diagnosis, biomedical imaging, and digital healthcare with low cost and portability.

Single Shot Lensless Interferenceless Phase Imaging of Biochemical Samples Using Synchrotron near Infrared Beam

Phase imaging of biochemical samples has been demonstrated for the first time at the Infrared Microspectroscopy (IRM) beamline of the Australian Synchrotron using the usually discarded near-IR (NIR) region of the synchrotron-IR beam. The synchrotron-IR beam at the Australian Synchrotron IRM beamline has a unique fork shaped intensity distribution as a result of the gold coated extraction mirror shape, which includes a central slit for rejection of the intense X-ray beam. The resulting beam configuration makes any imaging task challenging. For intensity imaging, the fork shaped beam is usually tightly focused to a point on the sample plane followed by a pixel-by-pixel scanning approach to record the image. In this study, a pinhole was aligned with one of the lobes of the fork shaped beam and the Airy diffraction pattern was used to illuminate biochemical samples. The diffracted light from the samples was captured using a NIR sensitive lensless camera. A rapid phase-retrieval algorithm was applied to the recorded intensity distributions to reconstruct the phase information. The preliminary results are promising to develop multimodal imaging capabilities at the IRM beamline of the Australian Synchrotron.

Experimental optimization of lensless digital holographic microscopy with rotating diffuser-based coherent noise reduction

Laser-based lensless digital holographic microscopy (LDHM) is often spoiled by considerable coherent noise factor. We propose a novel LDHM method with significantly limited coherent artifacts, e.g., speckle noise and parasitic interference fringes. It is achieved by incorporating a rotating diffuser, which introduces partial spatial coherence and preserves high temporal coherence of laser light, crucial for credible in-line hologram reconstruction. We present the first implementation of the classical rotating diffuser concept in LDHM, significantly increasing the signal-to-noise ratio while preserving the straightforwardness and compactness of the LDHM imaging device. Prior to the introduction of the rotating diffusor, we performed LDHM experimental hardware optimization employing 4 light sources, 4 cameras, and 3 different optical magnifications (camera-sample distances). It was guided by the quantitative assessment of numerical amplitude/phase reconstruction of test targets, conducted upon standard deviation calculation (noise factor quantification), and resolution evaluation (information throughput quantification). Optimized rotating diffuser LDHM (RD-LDHM) method was successfully corroborated in technical test target imaging and examination of challenging biomedical sample (60 µm thick mouse brain tissue slice). Physical minimization of coherent noise (up to 50%) was positively verified, while preserving optimal spatial resolution of phase and amplitude imaging. Coherent noise removal, ensured by proposed RD-LDHM method, is especially important in biomedical inference, as speckles can falsely imitate valid biological features. Combining this favorable outcome with large field-of-view imaging can promote the use of reported RD-LDHM technique in high-throughput stain-free biomedical screening.

Low-intensity illumination for lensless digital holographic microscopy with minimized sample interaction

Exposure to laser light alters cell culture examination via optical microscopic imaging techniques based on label-free coherent digital holography. To mitigate this detrimental feature, researchers tend to use a broader spectrum and lower intensity of illumination, which can decrease the quality of holographic imaging due to lower resolution and higher noise. We study the lensless digital holographic microscopy (LDHM) ability to operate in the low photon budget (LPB) regime to enable imaging of unimpaired live cells with minimized sample interaction. Low-cost off-the-shelf components are used, promoting the usability of such a straightforward approach. We show that recording data in the LPB regime (down to 7 µW of illumination power) does not limit the contrast or resolution of the hologram phase and amplitude reconstruction compared to regular illumination. The LPB generates hardware camera shot noise, however, to be effectively minimized via numerical denoising. The ability to obtain high-quality, high-resolution optical complex field reconstruction was confirmed using the USAF 1951 amplitude sample, phase resolution test target, and finally, live glial restricted progenitor cells (as a challenging strongly absorbing and scattering biomedical sample). The proposed approach based on severely limiting the photon budget in lensless holographic microscopy method can open new avenues in high-throughout (optimal resolution, large field-of-view, and high signal-to-noise-ratio single-hologram reconstruction) cell culture imaging with minimized sample interaction.

Point-of-care SARS-CoV-2 sensing using lens-free imaging and a deep learning-assisted quantitative agglutination assay

The persistence of the global COVID-19 pandemic caused by the SARS-CoV-2 virus has continued to emphasize the need for point-of-care (POC) diagnostic tests for viral diagnosis. The most widely used tests, lateral flow assays used in rapid antigen tests, and reverse-transcriptase real-time polymerase chain reaction (RT-PCR), have been instrumental in mitigating the impact of new waves of the pandemic, but fail to provide both sensitive and rapid readout to patients. Here, we present a portable lens-free imaging system coupled with a particle agglutination assay as a novel biosensor for SARS-CoV-2. This sensor images and quantifies individual microbeads undergoing agglutination through a combination of computational imaging and deep learning as a way to detect levels of SARS-CoV-2 in a complex sample. SARS-CoV-2 pseudovirus in solution is incubated with acetyl cholinesterase 2 (ACE2)-functionalized microbeads then loaded into an inexpensive imaging chip. The sample is imaged in a portable in-line lens-free holographic microscope and an image is reconstructed from a pixel superresolved hologram. Images are analyzed by a deep-learning algorithm that distinguishes microbead agglutination from cell debris and viral particle aggregates, and agglutination is quantified based on the network output. We propose an assay procedure using two images which results in the accurate determination of viral concentrations greater than the limit of detection (LOD) of 1.27 × 103 copies per mL, with a tested dynamic range of 3 orders of magnitude, without yet reaching the upper limit. This biosensor can be used for fast SARS-CoV-2 diagnosis in low-resource POC settings and has the potential to mitigate the spread of future waves of the pandemic.

Lensless computational imaging with a hybrid framework of holographic propagation and deep learning

Lensless imaging has attracted attention as it avoids the bulky optical lens. Lensless holographic imaging is a type of a lensless imaging technique. Recently, deep learning has also shown tremendous potential in lensless holographic imaging. A labeled complex field including real and imaginary components of the samples is usually used as a training dataset. However, obtaining such a holographic dataset is challenging. In this Letter, we propose a lensless computational imaging technique with a hybrid framework of holographic propagation and deep learning. The proposed framework takes recorded holograms as input instead of complex fields, and compares the input and regenerated holograms. Compared to previous supervised learning schemes with a labeled complex field, our method does not require this supervision. Furthermore, we use the generative adversarial network to constrain the proposed framework and tackle the trivial solution. We demonstrate high-quality reconstruction with the proposed framework compared to previous deep learning methods.

Physics-enhanced neural network for phase retrieval from two diffraction patterns

In this work, we propose a physics-enhanced two-to-one Y-neural network (two inputs and one output) for phase retrieval of complex wavefronts from two diffraction patterns. The learnable parameters of the Y-net are optimized by minimizing a hybrid loss function, which evaluates the root-mean-square error and normalized Pearson correlated coefficient on the two diffraction planes. An angular spectrum method network is designed for self-supervised training on the Y-net. Amplitudes and phases of wavefronts diffracted by a USAF-1951 resolution target, a phase grating of 200 lp/mm, and a skeletal muscle cell were retrieved using a Y-net with 100 learning iterations. Fast reconstructions could be realized without constraints or a priori knowledge of the samples.

Deep camera obscura: an image restoration pipeline for pinhole photography

Modern machine learning has enhanced the image quality for consumer and mobile photography through low-light denoising, high dynamic range (HDR) imaging, and improved demosaicing among other applications. While most of these advances have been made for normal lens-based cameras, there has been an emerging body of research for improved photography for lensless cameras using thin optics such as amplitude or phase masks, diffraction gratings, or diffusion layers. These lensless cameras are suited for size and cost-constrained applications such as tiny robotics and microscopy that prohibit the use of a large lens. However, the earliest and simplest camera design, the camera obscura or pinhole camera, has been relatively overlooked for machine learning pipelines with minimal research on enhancing pinhole camera images for everyday photography applications. In this paper, we develop an image restoration pipeline of the pinhole system to enhance the pinhole image quality through joint denoising and deblurring. Our pipeline integrates optics-based filtering and reblur losses for reconstructing high resolution still images (2600 × 1952) as well as temporal consistency for video reconstruction to enable practical exposure times (30 FPS) for high resolution video (1920 × 1080). We demonstrate high 2D image quality on real pinhole images that is on-par or slightly improved compared to other lensless cameras. This work opens up the potential of pinhole cameras to be used for photography in size-limited devices such as smartphones in the future.

Pixel Super-Resolution Phase Retrieval for Lensless On-Chip Microscopy via Accelerated Wirtinger Flow

Empowered by pixel super-resolution (PSR) and phase retrieval techniques, lensless on-chip microscopy opens up new possibilities for high-throughput biomedical imaging. However, the current PSR phase retrieval approaches are time consuming in terms of both the measurement and reconstruction procedures. In this work, we present a novel computational framework for PSR phase retrieval to address these concerns. Specifically, a sparsity-promoting regularizer is introduced to enhance the well posedness of the nonconvex problem under limited measurements, and Nesterov's momentum is used to accelerate the iterations. The resulting algorithm, termed accelerated Wirtinger flow (AWF), achieves at least an order of magnitude faster rate of convergence and allows a twofold reduction in the measurement number while maintaining competitive reconstruction quality. Furthermore, we provide general guidance for step size selection based on theoretical analyses, facilitating simple implementation without the need for complicated parameter tuning. The proposed AWF algorithm is compatible with most of the existing lensless on-chip microscopes and could help achieve label-free rapid whole slide imaging of dynamic biological activities at subpixel resolution.

Lensless light intensity model for quasi-spherical cell size measurement

Quasi-spherical cell size measurement plays an important role in medical test. Traditional methods such as a microscope and a flow cytometer are either it depends on professionals and cannot be automated, or it is expensive and bulky, which are not suitable for point-of-care test. Lab-on-a-chip technology using the lensless imaging system gives a good solution for obtaining the quasi-spherical cell size. The diffraction effects and the low resolution are the two main problems faced by the lensless imaging system. In this paper, a lensless light intensity model for the quasi-spherical cell size measurement is given. First, the diffraction characteristics of a quasi-spherical cell edge are given. Then, a diffraction model at an arc edge is constructed based on the Fresnel diffraction at a straight edge. Using the diffraction model at an arc edge, we explained the mechanism of the formation of the quasi-spherical cell diffraction fringes. Finally, the light intensity of the first bright ring of the quasi-spherical cell diffraction pattern is used to achieve quasi-spherical cell size measurement. The required equipment and the measurement methods are extremely simple, very suitable for point-of-care test. The experimental results show that the proposed model can realize the statistical measurement of the quasi-spherical cells and the classification of the quasi-spherical cells with a difference of 1 [Formula: see text].

DNF: diffractive neural field for lensless microscopic imaging

Lensless imaging has emerged as a robust means for the observation of microscopic scenes, enabling vast applications like whole-slide imaging, wave-front detection and microfluidic on-chip imaging. Such system captures diffractive measurements in a compact optical setup without the use of optical lens, and then typically applies phase retrieval algorithms to recover the complex field of target object. However existing techniques still suffer from unsatisfactory performance with noticeable reconstruction artifacts especially when the imaging parameter is not well calibrated. Here we propose a novel unsupervised Diffractive Neural Field (DNF) method to accurately characterize the imaging physical process to best reconstruct desired complex field of the target object through very limited measurement snapshots by jointly optimizing the imaging parameter and implicit mapping between spatial coordinates and complex field. Both simulations and experiments reveal the superior performance of proposed method, having > 6 dB PSNR (Peak Signal-to-Noise Ratio) gains on synthetic data quantitatively, and clear qualitative improvement on real-world samples. The proposed DNF also promises attractive prospects in practical applications because of its ultra lightweight complexity (e.g., 50× model size reduction) and plug-to-play advantage (e.g., random measurements with a coarse parameter estimation).

Resolution and Contrast Enhancement for Lensless Digital Holographic Microscopy and Its Application in Biomedicine

An important imaging technique in biomedicine, the conventional optical microscopy relies on relatively complicated and bulky lens and alignment mechanics. Based on the Gabor holography, the lensless digital holographic microscopy has the advantages of light weight and low cost. It has developed rapidly and received attention in many fields. However, the finite pixel size at the sensor plane limits the spatial resolution. In this study, we first review the principle of lensless digital holography, then go over some methods to improve image contrast and discuss the methods to enhance the image resolution of the lensless holographic image. Moreover, the applications of lensless digital holographic microscopy in biomedicine are reviewed. Finally, we look forward to the future development and prospect of lensless digital holographic technology.

An online flow-imaging particle counter and conventional water quality sensors detect drinking water contamination in the presence of normal water quality fluctuations

Contamination detection in drinking water is crucial for water utilities in terms of public health; however, current online water quality sensors can be unresponsive to various possible contaminants consisting of particulate and dissolved content or require a constant supply of reagents and sample preparation. We used a two-line test environment connected to a drinking water distribution system with flow-imaging particle counters and conventional sensors to assess their responses to the injection of contaminants into one line, including stormwater, treated wastewater, wastewater, well water, and Escherichia coli, while simultaneously measuring responses to normal water quality fluctuations in the other line. These water quality fluctuations were detected with all of the conventional sensors (except conductivity) and with 3 out of 5 of the size- and shape-derived particle classes of the flow-imaging particle counter. The flow-imaging particle counter was able to detect all of the studied contaminants, e.g. municipal wastewater at 0.001% (v/v), while the oxidation-reduction potential sensor outperformed other conventional sensors, detecting the same wastewater at 0.03% (v/v). The presence of particles less than 1 µm in size was shown to be a generic parameter for the detection of particulates present in the studied contaminants; however, they manifested a considerable response to fluctuations which led to lower relative response to contaminants in comparison to larger particles. The particle size and class distributions of contaminants were different from those of drinking water, and thus monitoring particles larger than 1 µm or specific particle classes of flow-imaging particle counter, which are substantially more abundant in contaminated water than in pure drinking water, can improve the detection of contamination events. Water utilities could optimize contamination detection by selecting water quality parameters with a minimal response to quality fluctuations and/or a high relative response to contaminants.

Automated detection of apoptotic versus nonapoptotic cell death using label-free computational microscopy

Identification of cell death mechanisms, particularly distinguishing between apoptotic versus nonapoptotic pathways, is of paramount importance for a wide range of applications related to cell signaling, interaction with pathogens, therapeutic processes, drug discovery, drug resistance, and even pathogenesis of diseases like cancers and neurogenerative disease among others. Here, we present a novel high-throughput method of identifying apoptotic versus necrotic versus other nonapoptotic cell death processes, based on lensless digital holography. This method relies on identification of the temporal changes in the morphological features of mammalian cells, which are unique to each cell death processes. Different cell death processes were induced by known cytotoxic agents. A deep learning-based approach was used to automatically classify the cell death mechanism (apoptotic vs necrotic vs nonapoptotic) with more than 93% accuracy. This label free approach can provide a low cost (<$250) alternative to some of the currently available high content imaging-based screening tools.

Robust multi-angle structured illumination lensless microscopy via illumination angle calibration

Multi-angle structured illumination lensless (MASIL) microscopy enables high-resolution image recovery over a large field of view. Successful image recovery of MASIL microscopy, however, relies on an accurate knowledge of the multi-angle illumination. System misalignments and slight deviations from the true illumination angle may result in image artifacts in reconstruction. Here we report a MASIL microscopy system that is robust against illumination misalignment. To calibrate the illumination angles, we design and use a double-sided mask, which is a glass wafer fabricated with a ring-array pattern on the upper surface and a disk-array pattern on the lower surface. As such, the illumination angles can be decoded from the captured images by estimating the relative displacement of the two patterns. We experimentally demonstrate that this system can achieve successful image recovery without any prior knowledge of the illumination angles. The reported approach provides a simple yet robust resolution for wide-field lensless microscopy. It can solve the LED array misalignment problem and calibrate angle-varied illumination for a variety of applications.

Multi-parametric functional imaging of cell cultures and tissues with a CMOS microelectrode array

Electrode-based impedance and electrochemical measurements can provide cell-biology information that is difficult to obtain using optical-microscopy techniques. Such electrical methods are non-invasive, label-free, and continuous, eliminating the need for fluorescence reporters and overcoming optical imaging's throughput/temporal resolution limitations. Nonetheless, electrode-based techniques have not been heavily employed because devices typically contain few electrodes per well, resulting in noisy aggregate readouts. Complementary metal-oxide-semiconductor (CMOS) microelectrode arrays (MEAs) have sometimes been used for electrophysiological measurements with thousands of electrodes per well at sub-cellular pitches, but only basic impedance mappings of cell attachment have been performed outside of electrophysiology. Here, we report on new field-based impedance mapping and electrochemical mapping/patterning techniques to expand CMOS-MEA cell-biology applications. The methods enable accurate measurement of cell attachment, growth/wound healing, cell-cell adhesion, metabolic state, and redox properties with single-cell spatial resolution (20 μm electrode pitch). These measurements allow the quantification of adhesion and metabolic differences of cells expressing oncogenes versus wild-type controls. The multi-parametric, cell-population statistics captured by the chip-scale integrated device opens up new avenues for fully electronic high-throughput live-cell assays for phenotypic screening and drug discovery applications.

Toward implantable devices for angle-sensitive, lens-less, multifluorescent, single-photon lifetime imaging in the brain using Fabry-Perot and absorptive color filters

Implantable image sensors have the potential to revolutionize neuroscience. Due to their small form factor requirements; however, conventional filters and optics cannot be implemented. These limitations obstruct high-resolution imaging of large neural densities. Recent advances in angle-sensitive image sensors and single-photon avalanche diodes have provided a path toward ultrathin lens-less fluorescence imaging, enabling plenoptic sensing by extending sensing capabilities to include photon arrival time and incident angle, thereby providing the opportunity for separability of fluorescence point sources within the context of light-field microscopy (LFM). However, the addition of spectral sensitivity to angle-sensitive LFM reduces imager resolution because each wavelength requires a separate pixel subset. Here, we present a 1024-pixel, 50  µm thick implantable shank-based neural imager with color-filter-grating-based angle-sensitive pixels. This angular-spectral sensitive front end combines a metal-insulator-metal (MIM) Fabry-Perot color filter and diffractive optics to produce the measurement of orthogonal light-field information from two distinct colors within a single photodetector. The result is the ability to add independent color sensing to LFM while doubling the effective pixel density. The implantable imager combines angular-spectral and temporal information to demix and localize multispectral fluorescent targets. In this initial prototype, this is demonstrated with 45 μm diameter fluorescently labeled beads in scattering medium. Fluorescent lifetime imaging is exploited to further aid source separation, in addition to detecting pH through lifetime changes in fluorescent dyes. While these initial fluorescent targets are considerably brighter than fluorescently labeled neurons, further improvements will allow the application of these techniques to in-vivo multifluorescent structural and functional neural imaging.

Recent Advances in Lensless Imaging

Lensless imaging provides opportunities to design imaging systems free from the constraints imposed by traditional camera architectures. Thanks to advances in imaging hardware, fabrication techniques, and new algorithms, researchers have recently developed lensless imaging systems that are extremely compact, lightweight or able to image higher-dimensional quantities. Here we review these recent advances and describe the design principles and their effects that one should consider when developing and using lensless imaging systems.

Understanding and correcting wavenumber error in interference pattern structured illumination imaging

The impacts of uncertainty in mirror movements in mechanically scanned interference pattern structured illumination imaging (IPSII) are discussed. It is shown that uncertainty in IPSII mirror movements causes errors in both the phase and amplitude of the Fourier transform of the resulting imaging. Finally, we demonstrate that iterative phase retrieval algorithms can improve the quality of IPSII images by correcting the phase errors caused by mirror movement uncertainties.

Phase retrieval with physics informed zero-shot network

Phase can be reliably estimated from a single diffracted intensity image if faithful prior information about the object is available. Examples include amplitude bounds, object support, sparsity in the spatial or transform domain, deep image prior, and the prior learned from labeled datasets by a deep neural network. Deep learning facilitates state-of-the-art reconstruction quality but requires a large labeled dataset (ground truth measurement pair acquired in the same experimental conditions) for training. To alleviate this data requirement problem, this Letter proposes a zero-shot learning method. The Letter demonstrates that the object prior learned by a deep neural network while being trained for a denoising task can also be utilized for phase retrieval if the diffraction physics is effectively enforced on the network output. The Letter additionally demonstrates that the incorporation of total variation in the proposed zero-shot framework facilitates reconstruction of similar quality in less time (e.g., ∼9 fold, for a test reported in this Letter).

Lensless imaging-based discrimination between tumour cells and blood cells towards circulating tumour cell cultivation

Circulating tumour cells (CTCs) are recognized as important markers for cancer research. Nonetheless, the extreme rarity of CTCs in blood samples limits their availability for multiple characterization. The cultivation of CTCs is still technically challenging due to the lack of information of CTC proliferation, and it is difficult for conventional microscopy to monitor CTC cultivation owing to low throughput. In addition, for precise monitoring, CTCs need to be distinguished from the blood cells which co-exist with CTCs. Lensless imaging is an emerging technique to visualize micro-objects over a wide field of view, and has been applied for various cytometry analyses including blood tests. However, discrimination between tumour cells and blood cells was not well studied. In this study, we evaluated the potential of the lensless imaging system as a tool for monitoring CTC cultivation. Cell division of model tumour cells was examined using the lensless imaging system composed of a simple setup. Subsequently, we confirmed that tumour cells, JM cells (model lymphocytes), and erythrocytes exhibited cell line-specific patterns on the lensless images. After several discriminative parameters were extracted, discrimination between the tumour cells and other blood cells was demonstrated based on linear discriminant analysis. We also combined the highly efficient CTC recovery device, termed microcavity array, with the lensless-imaging to demonstrate recovery, monitoring and discrimination of the tumour cells spiked into whole blood samples. This study indicates that lensless imaging can be a powerful tool to investigate CTC proliferation and cultivation.

Muscope: a miniature on-chip lensless microscope

We report the Muscope, a miniature lensless holographic microscope suitable for on-chip integration. The prototype of the Muscope measured approximately only 7 mm × 4 mm × 4 mm, and was capable of offering a sub-micron half-pitch resolution. We have used, for the first time, a microLED display as the light source in a microscope. The individual pixels of a microLED display chip are used as programmable, microscopic and intense LEDs which can be spatially moved in a two-dimensional plane with a 5 μm pitch. This unique feature set of the display was used to implement computational super-resolution and wide-field imaging without any extra hardware, unlike many other lensless microscopes. We also report a new method to evaluate the magnification in our setting. The Muscope surpasses the existing lensless microscopes in compactness, scalability for production, automated operation and system integration. It provides exciting opportunities for a new class of devices with in-built optical imaging and monitoring and/or sensing capabilities.

Roadmap on digital holography [Invited]

This Roadmap article on digital holography provides an overview of a vast array of research activities in the field of digital holography. The paper consists of a series of 25 sections from the prominent experts in digital holography presenting various aspects of the field on sensing, 3D imaging and displays, virtual and augmented reality, microscopy, cell identification, tomography, label-free live cell imaging, and other applications. Each section represents the vision of its author to describe the significant progress, potential impact, important developments, and challenging issues in the field of digital holography.

Improving particle detection and size estimation accuracy in digital in-line holography using autoregressive interpolation

The accuracy of particle detection and size estimation is limited by the physical size of the digital sensor used to record the hologram in a digital in-line holographic imaging system. In this paper, we propose to utilize the autoregressive (AR) interpolation of the hologram to increase pixel density and, effectively, the quality of hologram reconstruction. Simulation studies are conducted to evaluate the influence of AR interpolation of a hologram on the accuracy of detection and size estimation of single and multiple particles of varying sizes. A comparative study on the performance of different interpolation techniques indicates the advantage of the proposed AR hologram interpolation approach. An experimental result is provided to validate the suitability of the proposed algorithm in practical applications.

Approaching phase-imaging through defocusing shadowgraphy for acoustic resonator diagnosis and the capability of direct index-of-refraction measurements

The visualization of index-of-refraction (IoR) distribution is one of the common methods to investigate fluid flow or pressure fields. While schlieren and shadowgraphy imaging techniques are widely accepted, their inherent limitations often lead to difficulties in elucidating the IoR distribution and extracting the true IoR information from the resulting images. While sophisticated solutions exist, the IoR-gradient-to-image was achieved by purposely introducing a commonly avoided "defect" into the optical path of a conventional coincident schlieren/shadowgraphy setup; the defect is a combination of slight defocusing and the use of non-conjugate optical components. As such, the method presented in this work is referred to as defocusing shadowgraphy, or DF-shadowgraphy. While retaining the ease of a conventional schlieren/shadowgraphy geometry, this DF approach allows direct visualization of complicated resonant acoustic fields even without any data processing. For instance, the transient acoustic fields of a common linear acoustic resonator and a two-dimensional one were directly visualized without inversion. Moreover, the optical process involved in DF-shadowgraphy was investigated from a theoretical perspective. A numerical solution of the sophisticated impulse response function was obtained, which converts the phase distortion into intensity distributions. Based on this solution, the IoRs of various gas streams (e.g., CO₂ and isopropanol vapor) were determined from single images.

Adaptive super-resolution enabled on-chip contact microscopy

We demonstrate an adaptive super-resolution based contact imaging on a CMOS chip to achieve subcellular spatial resolution over a large field of view of ∼24 mm². By using regular LED illumination, we acquire the single lower-resolution image of the objects placed approximate to the sensor with unit magnification. For the raw contact-mode lens-free image, the pixel size of the sensor chip limits the spatial resolution. We develop a hybrid supervised-unsupervised strategy to train a super-resolution network, circumventing the missing of in-situ ground truth, effectively recovering a much higher resolution image of the objects, permitting sub-micron spatial resolution to be achieved across the entire sensor chip active area. We demonstrate the success of this approach by imaging the proliferation dynamics of cells directly cultured on the chip.

Quantitative particle agglutination assay for point-of-care testing using mobile holographic imaging and deep learning

Particle agglutination assays are widely adopted immunological tests that are based on antigen-antibody interactions. Antibody-coated microscopic particles are mixed with a test sample that potentially contains the target antigen, as a result of which the particles form clusters, with a size that is a function of the antigen concentration and the reaction time. Here, we present a quantitative particle agglutination assay that combines mobile lens-free microscopy and deep learning for rapidly measuring the concentration of a target analyte; as its proof-of-concept, we demonstrate high-sensitivity C-reactive protein (hs-CRP) testing using human serum samples. A dual-channel capillary lateral flow device is designed to host the agglutination reaction using 4 μL of serum sample with a material cost of 1.79 cents per test. A mobile lens-free microscope records time-lapsed inline holograms of the lateral flow device, monitoring the agglutination process over 3 min. These captured holograms are processed, and at each frame the number and area of the particle clusters are automatically extracted and fed into shallow neural networks to predict the CRP concentration. 189 measurements using 88 unique patient serum samples were utilized to train, validate and blindly test our platform, which matched the corresponding ground truth concentrations in the hs-CRP range (0-10 μg mL^-1) with an R² value of 0.912. This computational sensing platform was also able to successfully differentiate very high CRP concentrations (e.g., >10-500 μg mL^-1) from the hs-CRP range. This mobile, cost-effective and quantitative particle agglutination assay can be useful for various point-of-care sensing needs and global health related applications.

Classification of unlabeled cells using lensless digital holographic images and deep neural networks

Background

Image-based cell analytic methodologies offer a relatively simple and economical way to analyze and understand cell heterogeneities and developments. Owing to developments in high-resolution image sensors and high-performance computation processors, the emerging lensless digital holography technique enables a simple and cost-effective approach to obtain label-free cell images with a large field of view and microscopic spatial resolution.

Methods

The holograms of three types of cells, including MCF-10A, EC-109, and MDA-MB-231 cells, were recorded using a lensless digital holography system composed of a laser diode, a sample stage, an image sensor, and a laptop computer. The amplitude images were reconstructed using the angular spectrum method, and the sample to sensor distance was determined using the autofocusing criteria based on the sparsity of image edges and corner points. Four convolutional neural networks (CNNs) were used to classify the cell types based on the recovered holographic images.

Results

Classification of two cell types and three cell types achieved an accuracy of higher than 91% by all the networks used. The ResNet and the DenseNet models had similar classification accuracy of 95% or greater, outperforming the GoogLeNet and the CNN-5 models.

Conclusions

These experiments demonstrated that the CNNs were effective at classifying two or three types of tumor cells. The lensless holography combined with machine learning holds great promise in the application of stainless cell imaging and classification, such as in cancer diagnosis and cancer biology research, where distinguishing normal cells from cancer cells and recognizing different cancer cell types will be greatly beneficial.

Compact metalens-based integrated imaging devices for near-infrared microscopy

With current trends to progressively miniaturize optical systems, it is now essential to look for alternative methods to control light at extremely small dimensions. Metalenses are composed of subwavelength nanostructures and have an excellent ability to manipulate the polarization, phase, and amplitude of incident light. Although great progress of metalenses has been made, the compact metalens-integrated devices have not been researched adequately. In the study, we present compact imaging devices for near-infrared microscopy, in which a metalens is exploited. The indicators including resolution, magnification, and image quality are investigated via imaging several specimens of intestinal cells to verify the overall performance of the imaging system. The further compact devices, where the metalens is integrated directly on the CMOS imaging sensor, are also researched to detect biomedical issues. This study provides an approach to constructing compact imaging devices based on metalenses for near-infrared microscopy, micro-telecopy, etc., which can promote the miniaturization tending of futural optical systems.

Adaptive Parameter Model for Quasi-Spherical Cell Size Measurement Based on Lensless Imaging System

Many biological cells appear quasi-spherical, such as red blood cells, white blood cells, egg cells, cancer cells, etc. Cell size is an important basis for medical diagnosis. The traditional method is to use a microscope or flow cytometer to obtain the cell size. Either it depends on professionals and cannot be automated, or it is expensive and bulky, which are not suitable for point-of-care test. Lab-on-a-chip technology using a lensless imaging system gives a better solution for obtaining the cell size. In order to deal with the diffraction in the lensless imaging system, the distance between the light source and the cell, the distance between the cell and the CMOS image sensor and optical wavelength need to be accurately measured or controlled, which will greatly increase the complexity of the system, making it difficult to truly apply to point-of-care test. In this paper, an adaptive parameter model for quasi-spherical cell size measurement based on lensless imaging system is given. First, the diffraction theory used in the model is explained. Then, the adaptive algorithm of the system parameter is given. To illustrate the practicality of the algorithm, a quasi-spherical cell size measurement method and a super-resolution algorithm are given. Finally, the experiment proves that the adaptive parameter model is effective can meet the needs of quasi-spherical cell size measurement.

GEOMScope: Large Field-of-view 3D Lensless Microscopy with Low Computational Complexity

Imaging systems with miniaturized device footprint, real-time processing speed and high resolution three-dimensional (3D) visualization are critical to broad biomedical applications such as endoscopy. Most of existing imaging systems rely on bulky lenses and mechanically refocusing to perform 3D imaging. Here, we demonstrate GEOMScope, a lensless single-shot 3D microscope that forms image through a single layer of thin microlens array and reconstructs objects through an innovative algorithm combining geometrical-optics-based pixel back projection and background suppressions. We verify the effectiveness of GEOMScope on resolution target, fluorescent particles and volumetric objects. Comparing to other widefield lensless imaging devices, we significantly reduce the required computational resource and increase the reconstruction speed by orders of magnitude. This enables us to image and recover large volume 3D object in high resolution with near real-time processing speed. Such a low computational complexity is attributed to the joint design of imaging optics and reconstruction algorithms, and a joint application of geometrical optics and machine learning in the 3D reconstruction. More broadly, the excellent performance of GEOMScope in imaging resolution, volume, and reconstruction speed implicates that geometrical optics could greatly benefit and play an important role in computational imaging.

Accurate and fast modeling of scattering from random arrays of nanoparticles using the discrete dipole approximation and angular spectrum method

Lens-free microscopes can utilize holographic reconstruction techniques to recover the image of an object from the digitally recorded superposition of an unperturbed plane wave and a wave scattered by the object. Image reconstruction most commonly relies on the scalar angular spectrum method (ASM). While fast, the scalar ASM can be inaccurate for nanoscale objects, either because of the scalar approximation, or more generally, because it only models field propagation and not light-matter interaction, including inter-particle coupling. Here we evaluate the accuracy of the scalar ASM when combined with three different light-matter interaction models for computing the far-field light scattered by random arrays of gold and polystyrene nanoparticles. Among the three models-a dipole-matched transmission model, an optical path length model, and a binary amplitude model-we find that which model is most accurate depends on the nanoparticle material and packing density. For polystyrene particles at any packing density, there is always at least one model with error below 20%, while for gold nanoparticles with 40% or 50% surface coverage, there are no models that can provide errors better than 30%. The ASM error is determined in comparison to a discrete dipole approximation model, which is more computationally efficient than other full-wave modeling techniques. The knowledge of when and how the ASM fails can serve as a first step toward improved resolution in lens-free reconstruction and can also be applied to other random nanoparticle array applications such as lens-based super-resolution imaging, sub-diffraction beam focusing, and biomolecular sensing.

Dynamic Imaging and Characterization of Volatile Aerosols in E-Cigarette Emissions Using Deep Learning-Based Holographic Microscopy

Various volatile aerosols have been associated with adverse health effects; however, characterization of these aerosols is challenging due to their dynamic nature. Here, we present a method that directly measures the volatility of particulate matter (PM) using computational microscopy and deep learning. This method was applied to aerosols generated by electronic cigarettes (e-cigs), which vaporize a liquid mixture (e-liquid) that mainly consists of propylene glycol (PG), vegetable glycerin (VG), nicotine, and flavoring compounds. E-cig-generated aerosols were recorded by a field-portable computational microscope, using an impaction-based air sampler. A lensless digital holographic microscope inside this mobile device continuously records the inline holograms of the collected particles. A deep learning-based algorithm is used to automatically reconstruct the microscopic images of e-cig-generated particles from their holograms and rapidly quantify their volatility. To evaluate the effects of e-liquid composition on aerosol dynamics, we measured the volatility of the particles generated by flavorless, nicotine-free e-liquids with various PG/VG volumetric ratios, revealing a negative correlation between the particles' volatility and the volumetric ratio of VG in the e-liquid. For a given PG/VG composition, the addition of nicotine dominated the evaporation dynamics of the e-cig aerosol and the aforementioned negative correlation was no longer observed. We also revealed that flavoring additives in e-liquids significantly decrease the volatility of e-cig aerosol. The presented holographic volatility measurement technique and the associated mobile device might provide new insights on the volatility of e-cig-generated particles and can be applied to characterize various volatile PM.