Lens-free Imaging Research Articles

Miniature particulate matter counter and analyzer based on lens-free imaging of light scattering signatures with a holed image sensor

We report our latest developments on a novel miniature particulate matter (PM) sensor for personal air quality monitoring. This optical sensor combines a visible fibered light source and a vertical air channel drilled out of a holed retina. The fabrication of such retina features an unconventional design of a CMOS image sensor and a specific drilling post process at wafer level. The scattering of light by a single particle generates a distinctive pattern. By using a compact optical set-up, we measure a lens-free projection of the scattering signature on the image sensor. We present the design and fabrication of such sensor as well as experimental signatures that are consistent with our in-house analytical model. We demonstrate counting and differentiation between calibrated particles of different sizes. Our current setup allows for the detection of single 320 nm polystyrene particles. Finally, we describe our image analysis procedure that fits a measured signature with a modelled one in order to retrieve particle's size and refractive index.

Pixel super-resolved lens-free on-chip microscopy based on dual laterally shifting modulation.

Achieving high spatial resolution over a wide field of view (FOV) is the goal of many imaging systems. In a traditional lens-based microscope, designing a complex objective with high numerical aperture (NA) to achieve this goal is a tough and challenging task. The lens-free wide-field imaging method based on phase retrieval provides a new way to bypass the trade-off between the spatial resolution and FOV of conventional microscopy. However, the typical lens-free microscopy usually requires mechanical devices with high precision and repeatability. In this paper, we report a robust and cost-effective pixel super-resolved lens-free imaging method based on dual laterally shifting modulation. A thin diffuser is inserted between the object and the image sensor to be used as the modulator. The diffuser and the object are transversely scanned at the same time to add diversities for phase retrieval and pixel super-resolution, respectively. In this way, the positional shifts of the diffuser and the object can be directly recovered with the registration algorithm, thus addressing the low stability and inaccuracy issues of translation stages. We also propose a pixel super-resolution phase-retrieval algorithm to recover the object and the unknown diffuser. We first use numerical simulations to evaluate the proposed scheme. Then, we validate this approach by imaging a resolution target and a pollen sample, thus achieving an FOV of ${\sim}{30}\;{\text{mm}^2}$∼30mm2 and a half-pitch resolution of 0.78 µm, which surpasses 2.14 times the theoretical Nyquist-Shannon sampling resolution limit. Finally, the 3D refocusing ability is also verified by imaging a thick mosquito sample.

Low false positive and accurate detection of yeast cell viability and concentration using an automatic staining and lensfree imaging platform

Yeast cell viability and concentration are the crucial factors affecting product quality in food industry and bio-fuel production, as well as the evaluation basis for environmental toxic compounds. To overcome the drawbacks of existing methods, including high error, false positive and low automation, we propose a highly accurate approach based on an automatic staining and high-throughput lensfree imaging platform. A precisely controlled staining process is implemented automatically, which largely avoids the error caused by inappropriate exposure times. Based on optical simulation analysis, energy distribution characteristics are proposed. They are better with steady theoretical evidence for live yeast cell recognition. The parameters are directly extracted from raw cell fingerprints without any reconstruction. Those progresses improve robustness and increase efficiency. Availability of this approach is validated by compared the detection results with gold-standard PI counting method in a H2O2 toxicity test. So it is expected to be widely used in industrial production and environmental toxicity assessment.

Generative optical modeling of whole blood for detecting platelets in lens-free images.

In this paper, we consider the task of detecting platelets in images of diluted whole blood taken with a lens-free microscope. Despite having several advantages over traditional microscopes, lens-free imaging systems have the significant challenge that the resolution of the system is typically limited by the pixel dimensions of the image sensor. As a result of this limited resolution, detecting platelets is very difficult even by manual inspection of the images due to the fact that platelets occupy just a few pixels of the reconstructed image. To address this challenge, we develop an optical model of diluted whole blood to generate physically realistic simulated holograms suitable for training machine learning models in a supervised manner. We then use this model to train a convolutional neural network (CNN) for platelet detection and validate our approach by developing a novel optical configuration which allows collecting both lens-free and fluorescent microscopy images of the same field of view of diluted whole blood samples with fluorescently labeled platelets.

Artifactless, lens-free coherent microscopy with quasi-3D scanning

We propose a quasi-3D scheme to solve imaging artifacts from the laser source for lens-free holographic imaging. The scheme includes three steps: background noise removal, image alignment and twin-image elimination. The sample moves along the lateral direction and the imaging sensor is sequentially placed at different axial distances for data recording. With this volume data, the artifacts from the laser source can be effectively mitigated. The experiments show that a pixel-level imaging resolution (1.55 µm) over a field-of-view larger than 20 mm2 can be achieved. The experiments with biological samples, including kidney and intestine pathological slides of rats, validate that this technique can realize dramatic image contrast and resolution enhancements compared to conventional lens-free holographic methods. Our work provides a new framework to suppress laser source artifacts for holographic imaging.

Deep learning virtual colorful lens-free on-chip microscopy

Currently, it is generally known that lens-free holographic microscopy, which has no imaging lens, can realize a large field-of-view imaging with a low-cost setup. However, in order to obtain colorful images, traditional lens-free holographic microscopy should utilize at least three quasi-chromatic light sources of discrete wavelengths, such as red LED, green LED, and blue LED. Here, we present a virtual colorization by deep learning methods to transfer a gray lens-free microscopy image into a colorful image. Through pairs of images, i.e., grayscale lens-free microscopy images under green LED at 550 nm illumination and colorful bright-field microscopy images, a generative adversarial network (GAN) is trained, and its effectiveness of virtual colorization is proved by applying it to hematoxylin and eosin stained pathological tissue samples imaging. Our computational virtual colorization method might strengthen the monochromatic illumination lens-free microscopy in medical pathology applications and label staining biomedical research.

基于生成对抗网络的无透镜成像方法

基于生成对抗网络的无透镜成像方法

Computational Image Analysis of Guided Acoustic Waves Enables Rheological Assessment of Sub-nanoliter Volumes.

We present a method for the computational image analysis of high frequency guided sound waves based upon the measurement of optical interference fringes, produced at the air interface of a thin film of liquid. These acoustic actuations induce an affine deformation of the liquid, creating a lensing effect that can be readily observed using a simple imaging system. We exploit this effect to measure and analyze the spatiotemporal behavior of the thin liquid film as the acoustic wave interacts with it. We also show that, by investigating the dynamics of the relaxation processes of these deformations when actuation ceases, we are able to determine the liquid’s viscosity using just a lens-free imaging system and a simple disposable biochip. Contrary to all other acoustic-based techniques in rheology, our measurements do not require monitoring of the wave parameters to obtain quantitative values for fluid viscosities, for sample volumes as low as 200 pL. We envisage that the proposed methods could enable high throughput, chip-based, reagent-free rheological studies within very small samples.

Lens-free Dual-color Fluorescent CMOS Image Sensor for F?rster Resonance Energy Transfer Imaging

Lens-free Dual-color Fluorescent CMOS Image Sensor for F?rster Resonance Energy Transfer Imaging

Sub-Rayleigh single-pixel imaging via optical fluctuation.

Single-pixel imaging (SPI) is a novel lens-free imaging method quite different from the conventional spatially resolved imaging, yet it suffers the Rayleigh resolution limit imposed by the illumination aperture. Here we show that the Rayleigh resolution limit in SPI can be overcome by employing optical fluctuation. With Nth-order autocorrelation of the signal measured by the bucket detector in SPI, the imaging resolution can surpass the Rayleigh resolution limit by a factor of N. This result is important for optical microscopy based on SPI.

Localizing and Characterizing Colloidal Particles Scattering Using Lens-free Holographic Microscopy**Supported by the National Key R&D Program of China under Grant No 2016YFA0202102, the Fundamental Research Funds for the Central Universities under Grant No 14380078, and the Scientific Research Foundation of Graduate School of Nanjing University under Grant No 2017CL02.

Lens-free holographic microscopy could achieve both improved resolution and field of view (FOV), which has huge potential applications in biomedicine, fluid mechanics and soft matter physics. Unfortunately, due to the limited sensor pixel size, target objects could not be located to a satisfactory level. Recent studies have shown that electromagnetic scattering can be fitted to digital holograms to obtain the 3D positions of isolated colloidal spheres with nanometer precision and millisecond temporal resolution. Here, we describe a lens-free holographic imaging technique that fits multi-sphere superposition scattering to digital holograms to obtain in situ particle position and model parameters: size and refractive index of colloidal spheres. We show that the proposed method can be utilized to analyze the location and character of colloidal particles under large FOV with high density.

Multi-probe ptychographic iterative engine method

As a lensfree imaging technique, ptychographic iterative engine (PIE) can provide quantitative amplitude and phase distributions of samples in an extremely large field of view but without aberrations. However, relying on the single pinhole scanning, PIE is time-consuming in both diffraction recording and sample reconstruction, thus limiting its potentiality in dynamic imaging applications. In order to reduce the diffraction captures and increase its processing efficiency, we design the multi-probe PIE, in which a pinhole array is adopted instead of traditionally used single pinhole, thus diffractions at different positions can be captured simultaneously. Moreover, the multi-probe PIE reconstruction algorithm is also implemented to further accelerate the sample reconstruction speed. Proved by experiments, less than 10 s is required for diffraction recording for a large field of view of ∼150 mm2 and combining with the modified multi-probe PIE reconstruction algorithm, high-resolution quantitative sample information can be extracted accurately with much faster speed compared to traditional PIE reconstruction algorithm. It is believed the proposed multi-probe PIE can be future applied in dynamic imaging.

Multi-distance phase retrieval with a weighted shrink-wrap constraint

Multi-distance phase retrieval enables a full wavefront recovery with a set of defocused intensity patterns. However, the convergence speed of multi-distance phase retrieval is restricted by a stagnant issue, which leads to a low imaging contrast for visual observation. We propose an acceleration scheme to solve this problem. A shrink-wrap constraint is imposed to obtain an efficient support region. In support region, a weighted feedback strategy is used to speed up the convergence. In non-support region, an attenuated term is introduced to eliminate redundant data. With this combined constraint, imaging contrast and convergence speed are both enhanced observably. Simulations and experiments demonstrate the validity of this acceleration scheme in a lensfree imaging system. The proposed method can both reduce the workload of experimental data acquisition and computational load.

Highly sensitive lens-free fluorescence imaging device enabled by a complementary combination of interference and absorption filters.

We report a lens-free fluorescence imaging device using a composite filter composed of an interference filter and an absorption filter, each applied to one side of a fiber optic plate (FOP). The transmission of angled excitation light through the interference filter is absorbed by the absorption filter. The auto-fluorescence of the absorption filter is reduced by the reflection from the interference filter of normally incident excitation light. As a result, high-performance rejection of excitation light is achieved in a lens-free device. The FOP provides a flat, hard imaging device surface that does not degrade the spatial resolution. We demonstrate excitation rejection of approximately 108:1 at a wavelength of 450 nm in a fabricated lens-free device. The resolution of fluorescence imaging is approximately 12 µm. Time-lapse imaging of cells containing green fluorescent protein was performed in a 5-µm thin-film chamber. The small dimensions of the device allow observation of cell culturing in a CO2 incubator. We also demonstrate that the proposed lens-free filter is compatible with super-resolution bright-field imaging techniques. These features open a way to develop a high-performance, dual-mode, lens-free imaging device that is expected to be a powerful tool for many applications, such as imaging of labeled cells and point-of-care assay.

Resolution enhancement in digital in-line holography with sparsity

In digital holography, the reconstructed resolution is generally limited by the size of the numerical aperture. For a lens-free imaging configuration, the high-frequency information is lost due to the finite size of the recorded area. It is challenging to obtain an enhanced-resolution recovery with the finite-recorded hologram. Here, we propose an algorithm to enhance the reconstructed resolution by utilizing sparse prior of the image. We combine the sparse regularization term with the extrapolation of the recorded area based on the inverse problem approach. For regularization, the l0-norm is utilized to promote the sparsity of the solution during the iterative process. An iterative optimization algorithm is developed to solve the nonconvex resolution enhancement problem, which is implemented by substituting the primal–dual saddle-point problem for the primal problem. Our method can circumvent the limitation of the recorded area and produce enhanced-resolution recovery by employing the sparsity of the object distribution. Both the simulation and experiment indicate that the proposed algorithm can achieve the superior enhancement for reconstructed resolution with a single captured hologram in a digital in-line holography.

Adaptive lens-free computational coherent imaging using autofocusing quantification with speckle illumination.

Multi-distance phase retrieval (MDPR) based lensfree imaging is promising for an aberration free and compact biological imaging system. In the MDPR processing, the measurement uncertainty of the sample-to-sensor distance undermines its imaging quality and imposes a heavy workload to achieve a perfect reconstruction. The optimal distance can be searched by using an image sharpness quantification function with a refocused data set, however, the scanning is sensitive to noise and aliasing artifact for MDPR. In this work, we propose an adaptive imaging scheme with the help of a diffuser inserted in the lensfree system. The optimal sample-to-sensor distance is searched by combing speckle imaging with sharpness quantification function. With this speckle-based auxiliary, intensity patterns under a coherent illumination are directly used to achieve an in-focusing image reconstruction. Experiments are given to demonstrate the stability, imaging resolution and optical sectioning for our scheme. This method provides a simple, stable and robust tool for the auto-focusing imaging.

High-Throughput Screening of Encapsulated Islets Using Wide-Field Lens-Free On-Chip Imaging

Islet microencapsulation is a promising solution to diabetes treatment, but its quality control based on manual microscopic inspection is extremely low-throughput, highly variable and laborious. This study presents a high-throughput islet-encapsulation quality screening system based on lens-free on-chip imaging with a wide field-of-view of 18.15 cm^2, which is more than 100 times larger than that of a lens-based optical microscope, enabling it to image and analyze ~8,000 microcapsules in a single frame. Custom-written image reconstruction and processing software provides the user with clinically important information, such as microcapsule count, size, intactness, and information on whether each capsule contains an islet. This high-throughput and cost-effective platform can be useful for researchers to develop better encapsulation protocols as well as perform quality control prior to transplantation.

Flocculation of microalgae using extracellular polymeric substances (EPS) extracted from activated sludge

This study investigates the role of microbial extracellular polymeric substances (EPSs) as bioflocculants to harvest microalgae (water-microalgae separation). The EPS extracted from waste activated sludge (WAS) by heat extraction were fractionated into soluble EPS (S-EPS), loosely-bound EPS (LB-EPS) and tightly-bound EPS (TB-EPS) forms. All the EPSs facilitated the flocculation of microalgal cells from stable growth medium. Of those EPSs, the TB-EPS showed the highest flocculating activity (FA) resulting in the substantial decrease in the amount of EPS added in terms of total organic carbon (TOC) during flocculation. The FA of microalgae was improved with the increase in TB-EPS dose, however, excessive dose of TB-EPS adversely affected it due to destabilization. Both LB- and TB-EPS could be utilized for flocculating microalgae as a sustainable option to the existing chemical-based flocculants. In addition to the conventional assessments, the effectiveness of the two bioflocculants for floc forming was also confirmed using a novel assessment of lens-free shadow imaging technique (LSIT), which was firstly applied for the rapid and quantitative assessment of microalgal flocculation.

Reflective lens-free imaging on high-density silicon microelectrode arrays for monitoring and evaluation of in vitro cardiac contractility.

The high rate of drug attrition caused by cardiotoxicity is a major challenge for drug development. Here, we developed a reflective lens-free imaging (RLFI) approach to non-invasively record in vitro cell deformation in cardiac monolayers with high temporal (169 fps) and non-reconstructed spatial resolution (352 µm) over a field-of-view of maximally 57 mm2. The method is compatible with opaque surfaces and silicon-based devices. Further, we demonstrated that the system can detect the impairment of both contractility and fast excitation waves in cardiac monolayers. Additionally, the RLFI device was implemented on a CMOS-based microelectrode array to retrieve multi-parametric information of cardiac cells, thereby offering more in-depth analysis of drug-induced (cardiomyopathic) effects for preclinical cardiotoxicity screening applications.

Accurate label-free 3-part leukocyte recognition with single cell lens-free imaging flow cytometry

Three-part white blood cell differentials which are key to routine blood workups are typically performed in centralized laboratories on conventional hematology analyzers operated by highly trained staff. With the trend of developing miniaturized blood analysis tool for point-of-need in order to accelerate turnaround times and move routine blood testing away from centralized facilities on the rise, our group has developed a highly miniaturized holographic imaging system for generating lens-free images of white blood cells in suspension. Analysis and classification of its output data, constitutes the final crucial step ensuring appropriate accuracy of the system. In this work, we implement reference holographic images of single white blood cells in suspension, in order to establish an accurate ground truth to increase classification accuracy. We also automate the entire workflow for analyzing the output and demonstrate clear improvement in the accuracy of the 3-part classification. High-dimensional optical and morphological features are extracted from reconstructed digital holograms of single cells using the ground-truth images and advanced machine learning algorithms are investigated and implemented to obtain 99% classification accuracy. Representative features of the three white blood cell subtypes are selected and give comparable results, with a focus on rapid cell recognition and decreased computational cost.