Counting White Blood Cells from a Blood Smear Using Fourier Ptychographic Microscopy

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.

Single-shot Fourier ptychographic microscopy with isotropic lateral resolution via polarization-multiplexed LED illumination

Fourier ptychographic microscopy (FPM) has emerged as a new wide-field and high-resolution computational imaging technique in recent years. To ensure data redundancy for a stable convergence solution, conventional FPM requires dozens or hundreds of raw images, increasing the time cost for both data collection and computation. Here, we propose a single-shot Fourier ptychographic microscopy with isotropic lateral resolution via polarization-multiplexed LED illumination, termed SIFPM. Three LED elements covered with 0°/45°/135° polarization films, respectively, are used to provide numerical aperture-matched illumination for the sample simultaneously. Meanwhile, a polarization camera is utilized to record the light field distribution transmitted through the sample. Based on weak object transfer functions, we first obtain the amplitude and phase estimations of the sample by deconvolution, and then we use them as the initial guesses of the FPM algorithm to refine the accuracy of reconstruction. We validate the complex sample imaging performance of the proposed method on quantitative phase target, unstained and stained bio-samples. These results show that SIFPM can realize quantitative imaging for general samples with the resolution of the incoherent diffraction limit, permitting high-speed quantitative characterization for cells and tissues.

Enabling biomedical technologies for chronic myelogenous leukemia (CML) biomarkers detection

Chronic myelogenous/myeloid leukemia (CML) is a type of cancer of bone marrow that arises from hematopoietic stem cells and affects millions of people worldwide. Eighty-five percent of the CML cases are diagnosed during chronic phase, most of which are detected through routine tests. Leukocytes, micro-Ribonucleic Acids, and myeloid markers are the primary biomarkers for CML diagnosis and are mainly detected using real-time reverse transcription polymerase chain reaction, flow cytometry, and genetic testing. Though multiple therapies have been developed to treat CML, early detection still plays a pivotal role in the overall patient survival rate. The current technologies used for CML diagnosis are costly and are confined to laboratory settings which impede their application in the point-of-care settings for early-stage detection of CML. This study provides detailed analysis and insights into the significance of CML, patient symptoms, biomarkers used for testing, and best possible detection techniques responsible for the enhancement in survival rates. A critical and detailed review is provided around potential microfluidic devices that can be adapted to detect the biomarkers associated with CML while enabling point-of-care testing for early diagnosis of CML to improve patient survival rates.

Fast digital refocusing Fourier ptychographic microscopy method based on convolutional neural network

Fourier ptychographic microscopy (FPM) is used to achieve high resolution and a large field of view. However, traditional FPM image reconstruction methods often yield poor image quality when encountering out-of-focus issues during reconstruction. Therefore, this study proposes a defocus-distance regression network based on convolutional neural networks. In an experimental validation, the root-mean-square error calculated from 1000 sets of predicted and true values was approximately 6.2 µm. The experimental results suggest that the proposed method has good generalization, maintains high accuracy in predicting defocus distances even for different biological samples, and extends the imaging depth-of-field of the FPM system by a factor of more than 3.

Peripheral Blood Leukocyte Detection Based on an Improved Detection Transformer Algorithm

The combination of a blood cell analyzer and artificial microscopy to detect white blood cells is used in hospitals. Blood cell analyzers not only have large throughput, but they also cannot detect cell morphology; although artificial microscopy has high accuracy, it is inefficient and prone to missed detections. In view of the above problems, a method based on Fourier ptychographic microscopy (FPM) and deep learning to detect peripheral blood leukocytes is proposed in this paper. Firstly, high-resolution and wide-field microscopic images of human peripheral blood cells are obtained using the FPM system, and the cell image data are enhanced with DCGANs (deep convolution generative adversarial networks) to construct datasets for performance evaluation. Then, an improved DETR (detection transformer) algorithm is proposed to improve the detection accuracy of small white blood cell targets; that is, the residual module Conv Block in the feature extraction part of the DETR network is improved to reduce the problem of information loss caused by downsampling. Finally, CIOU (complete intersection over union) is introduced as the bounding box loss function, which avoids the problem that GIOU (generalized intersection over union) is difficult to optimize when the two boxes are far away and the convergence speed is faster. The experimental results show that the mAP of the improved DETR algorithm in the detection of human peripheral white blood cells is 0.936. In addition, this algorithm is compared with other convolutional neural networks in terms of average accuracy, parameters, and number of inference frames per second, which verifies the feasibility of this method in microscopic medical image detection.

Unsupervised adaptive coded illumination Fourier ptychographic microscopy based on a physical neural network

Fourier Ptychographic Microscopy (FPM) is a computational technique that achieves a large space-bandwidth product imaging. It addresses the challenge of balancing a large field of view and high resolution by fusing information from multiple images taken with varying illumination angles. Nevertheless, conventional FPM framework always suffers from long acquisition time and a heavy computational burden. In this paper, we propose a novel physical neural network that generates an adaptive illumination mode by incorporating temporally-encoded illumination modes as a distinct layer, aiming to improve the acquisition and calculation efficiency. Both simulations and experiments have been conducted to validate the feasibility and effectiveness of the proposed method. It is worth mentioning that, unlike previous works that obtain the intensity of a multiplexed illumination by post-combination of each sequentially illuminated and obtained low-resolution images, our experimental data is captured directly by turning on multiple LEDs with a coded illumination pattern. Our method has exhibited state-of-the-art performance in terms of both detail fidelity and imaging velocity when assessed through a multitude of evaluative aspects.

Spatial- and Fourier-domain ptychography for high-throughput bio-imaging

First envisioned for determining crystalline structures, ptychography has become a useful imaging tool for microscopists. However, ptychography remains underused by biomedical researchers due to its limited resolution and throughput in the visible light regime. Recent developments of spatial- and Fourier-domain ptychography have successfully addressed these issues and now offer the potential for high-resolution, high-throughput optical imaging with minimal hardware modifications to existing microscopy setups, often providing an excellent trade-off between resolution and field of view inherent to conventional imaging systems, giving biomedical researchers the best of both worlds. Here, we provide extensive information to enable the implementation of ptychography by biomedical researchers in the visible light regime. We first discuss the intrinsic connections between spatial-domain coded ptychography and Fourier ptychography. A step-by-step guide then provides the user instructions for developing both systems with practical examples. In the spatial-domain implementation, we explain how a large-scale, high-performance blood-cell lens can be made at negligible expense. In the Fourier-domain implementation, we explain how adding a low-cost light source to a regular microscope can improve the resolution beyond the limit of the objective lens. The turnkey operation of these setups is suitable for use by professional research laboratories, as well as citizen scientists. Users with basic experience in optics and programming can build the setups within a week. The do-it-yourself nature of the setups also allows these procedures to be implemented in laboratory courses related to Fourier optics, biomedical instrumentation, digital image processing, robotics and capstone projects.

Beyond conventional microscopy: Observing kidney tissues by means of fourier ptychography

Kidney microscopy is a mainstay in studying the morphological structure, physiology and pathology of kidney tissues, as histology provides important results for a reliable diagnosis. A microscopy modality providing at same time high-resolution images and a wide field of view could be very useful for analyzing the whole architecture and the functioning of the renal tissue. Recently, Fourier Ptychography (FP) has been proofed to yield images of biology samples such as tissues and in vitro cells while providing high resolution and large field of view, thus making it a unique and attractive opportunity for histopathology. Moreover, FP offers tissue imaging with high contrast assuring visualization of small desirable features, although with a stain-free mode that avoids any chemical process in histopathology. Here we report an experimental measuring campaign for creating the first comprehensive and extensive collection of images of kidney tissues captured by this FP microscope. We show that FP microscopy unlocks a new opportunity for the physicians to observe and judge renal tissue slides through the novel FP quantitative phase-contrast microscopy. Phase-contrast images of kidney tissue are analyzed by comparing them with the corresponding renal images taken under a conventional bright-field microscope both for stained and unstained tissue samples of different thicknesses. In depth discussion on the advantages and limitations of this new stain-free microscopy modality is reported, showing its usefulness over the classical light microscopy and opening a potential route for using FP in clinical practice for histopathology of kidney.

Parallel Fourier ptychographic microscopy reconstruction method based on FPGA

Fourier ptychographic microscopy (FPM) can bypass the limitation of spatial bandwidth product to get images with large field-of-view and high resolution. The complicated sequential iterative calculation in the FPM reconstruction process reduces the reconstruction efficiency of the FPM. Therefore, we propose a parallel FPM reconstruction method based on field programmable gate array (FPGA) to accelerate the FPM reconstruction process. Using this method, multiple sub-regions in the Fourier domain can be computed in parallel and we customize a dedicated high-performance computational architecture for this approach. We deploy 4 FPM reconstruct computing architectures with a parallelism of 4 in a FPGA to compute the FPM reconstruction process, achieving the speed nearly 180 times faster than traditional methods. The proposed method provides a new perspective of parallel computing for FPM reconstruction.

Root Bark Extract of Oroxylum indicum Vent. Inhibits Solid and Ascites Tumors and Prevents the Development of DMBA-Induced Skin Papilloma Formation

Oroxylum indicum is a traditionally used plant in Ayurvedic and folk medicines. The plant is useful for the management of gastrointestinal diseases as well as skin diseases. In the present study, we analyzed the antitumor potential of O. indicum in Dalton's lymphoma ascites tumor cells (DLA) and Ehrlich ascites carcinoma (EAC)-induced solid and ascites tumors. Further, the potential of O. indicum extract (OIM) on skin papilloma induction by dimethyl benz(a) anthracene (DMBA) and croton oil was evaluated. The chemical composition of the extract was analyzed using UPLC-Q-TOF-MS. The predominant compounds present in the extract were demethoxycentaureidin 7-O-rutinoside, isorhamnetin-3-O-rutinoside, baicalein-7-O-glucuronide, 5,6,7-trihydroxyflavone, 3-Hydroxy-3',4',5'-trimethoxyflavone, 5,7-dihydroxy-3-(4-methoxyphenyl) chromen-4-one, and 4'-Hydroxy-5,7-dimethoxyflavanone. Treatment with high-dose OIM enhanced the percentage of survival in ascites tumor-bearing mice by 34.97%. Likewise, high and low doses of OIM reduced the tumor volume in mice by 61.84% and 54.21%, respectively. Further, the skin papilloma formation was brought down by the administration of low- and high-dose groups of OIM (by 67.51% and 75.63%). Overall, the study concludes that the Oroxylum indicum root bark extract is a potentially active antitumor and anticancer agent.

Discrimination between normal and cancer white blood cells using holographic projection technique

White blood cells (WBCs) play a vital role in the diagnosis of many blood diseases. Such diagnosis is based on the morphological analysis of blood microscopic images which is performed manually by skilled hematologist. However, this method has many drawbacks, such as the dependence on the hematologist's skill, slow performance, and varying accuracy. Therefore, in the current study, a new optical method for discrimination between normal and cancer WBCs of peripheral blood film (PBF) images is presented. This method is based on holographic projection technique which is able to provide an accurate and fast optical reconstruction method of WBCs floating in the air. Besides, it can provide a 3D visualization map of one WBC with its characterization parameters from only a single 2D hologram. To achieve that, at first, WBCs are accurately segmented from the microscopic PBF images using a developed in-house MATLAB code. Then, their associated phase computer-generated holograms (CGHs) are calculated using the well-known iterative Fourier transform algorithm (IFTA). Within the utilized algorithm, a speckle noise reduction technique, based on temporal multiplexing of spatial frequencies, is applied to minimize the speckle noise across the reconstruction plane. Additionally, a special hologram modulation is added to the calculated holograms to provide a 3D visualization map of one WBC, and discriminate normal and cancer WBCs. Finally, the calculated phase-holograms are uploaded on a phase-only spatial light modulator (SLM) for optical reconstruction. The optical reconstruction of such phase-holograms yields precise representation of normal and cancer WBCs. Moreover, a 3D visualization map of one WBC with its characterization parameters is provided. Therefore, the proposed technique can be used as a valuable tool for interpretation and analysis of WBCs, this in turn could provide an improvement in diagnosis and prognosis of blood diseases.

Robust full-pose-parameter estimation for the LED array in Fourier ptychographic microscopy

Fourier ptychographic microscopy (FPM) can achieve quantitative phase imaging with a large space-bandwidth product by synthesizing a set of low-resolution intensity images captured under angularly varying illuminations. Determining accurate illumination angles is critical because the consistency between actual systematic parameters and those used in the recovery algorithm is essential for high-quality imaging. This paper presents a full-pose-parameter and physics-based method for calibrating illumination angles. Using a physics-based model constructed with general knowledge of the employed microscope and the brightfield-to-darkfield boundaries inside captured images, we can solve for the full-pose parameters of misplaced LED array, which consist of the distance between the sample and the LED array, two orthogonal lateral shifts, one in-plane rotation angle, and two tilt angles, to correct illumination angles precisely. The feasibility and effectiveness of the proposed method for recovering random or remarkable pose parameters have been demonstrated by both qualitative and quantitative experiments. Due to the completeness of the pose parameters, the clarity of the physical model, and the high robustness for arbitrary misalignments, our method can significantly facilitate the design, implementation, and application of concise and robust FPM platforms.

Efficient multiplexed illumination and imaging approach for Fourier ptychographic microscopy

A Fourier ptychographic microscope (FPM) can obtain images with high resolution and a wide field of view (FOV). However, the time-consuming process of image acquisition and computation leads to low reconstruction efficiency. Therefore, we propose a state-multiplexed method through an optimized illumination pattern to accelerate FPM. First, to reduce the redundancy of the spectrum and analyze the impact of missing a certain sub-spectrum on overall spectrum reconstruction, we use an image quality evaluation method to obtain the differential expression between missing a certain LED lighting and all LED lighting. Second, we use the difference expression to select the important LEDs and obtain an optimized illumination pattern that lights up only the LEDs in the central area and the edge LEDs. Then, we update the multiplexing method with the new, to the best of our knowledge, illumination pattern and obtain satisfactorily reconstructed images. Finally, we validate the effectiveness and efficiency of our method with both simulation and experiments. Compared with the traditional method, our method accelerates the reconstruction speed of FPM while ensuring a large FOV and high resolution, saving about 73% of time.

High-performance heterogeneous FPGA data-flow architecture for Fourier ptychographic microscopy

Fourier ptychographic microscopy (FPM) is a recently developed computational imaging technique that can achieve both high-resolution and a wide field-of-view via a sequence of low-resolution images. FPM is a complex iterative process, and it is difficult to meet the needs of rapid reconstruction imaging with the conventional FPM deployed on general purpose processors. In this paper, we propose a high-performance heterogeneous field-programmable gate array (FPGA) architecture based on the principle of full pipeline and the data-flow structure for the iterative reconstruction procedure of FPM. By optimizing the architecture network at gate-level logic circuits, the running time of the FPGA-based FPM reconstruction procedure is nearly 20 times faster than conventional methods. Our proposed architecture can be used to develop FPM imaging equipment that meets resource and performance requirements.

Increasing a microscope's effective field of view via overlapped imaging and machine learning

This work demonstrates a multi-lens microscopic imaging system that overlaps multiple independent fields of view on a single sensor for high-efficiency automated specimen analysis. Automatic detection, classification and counting of various morphological features of interest is now a crucial component of both biomedical research and disease diagnosis. While convolutional neural networks (CNNs) have dramatically improved the accuracy of counting cells and sub-cellular features from acquired digital image data, the overall throughput is still typically hindered by the limited space-bandwidth product (SBP) of conventional microscopes. Here, we show both in simulation and experiment that overlapped imaging and co-designed analysis software can achieve accurate detection of diagnostically-relevant features for several applications, including counting of white blood cells and the malaria parasite, leading to multi-fold increase in detection and processing throughput with minimal reduction in accuracy.

Systematic segmentation method based on PCA of image hue features for white blood cell counting

Leukocyte (white blood cell, WBC) count is an essential factor that physicians use to diagnose infections and provide adequate treatment. Currently, WBC count is determined manually or semi-automatically, which often leads to miscounting. In this paper, we propose an automated method that uses a bioinspired segmentation mimicking the human perception of color. It is based on the claim that a person can locate WBCs in a blood smear image via the high chromatic contrast. First, by applying principal component analysis over RGB, HSV, and L*a*b* spaces, with specific combinations, pixels of leukocytes present high chromatic variance; this results in increased contrast with the average hue of the other blood smear elements. Second, chromaticity is processed as a feature, without separating hue components; this is different to most of the current automation that perform mathematical operations between hue components in an intuitive way. As a result of this systematic method, WBC recognition is computationally efficient, overlapping WBCs are separated, and the final count is more precise. In experiments with the ALL-IDB benchmark, the performance of the proposed segmentation was assessed by comparing the WBC from the processed images with the ground truth. Compared with previous methods, the proposed method achieved similar results in sensitivity and precision and approximately 0.2% higher specificity and 0.3% higher accuracy for pixel classification in the segmentation stage; as well, the counting results are similar to previous works.

A microfluidic cytometer for white blood cell analysis

Despite the wide use of cytometry for white blood cell classification, the performance of traditional cytometers in point-of-care testing remains to be improved. Microfluidic techniques have been shown with considerable potentials in the development of portable devices. Here we present a prototype of microfluidic cytometer which integrates a three-dimensional hydrodynamic focusing system and an on-chip optical system to count and classify white blood cells. By adjusting the flow speed of sheath flow and sample flow, the blood cells can be horizontally and vertically focused in the center of microchannel. Optical fibers and on-chip microlens are embedded for the excitation and detection of single-cell. The microfluidic chip was validated by classifying white blood cells from clinical blood samples.

Optical mesoscopy, machine learning, and computational microscopy enable high information content diagnostic imaging of blood films

Automated image-based assessment of blood films has tremendous potential to support clinical haematology within overstretched healthcare systems. To achieve this, efficient and reliable digital capture of the rich diagnostic information contained within a blood film is a critical first step. However, this is often challenging, and in many cases entirely unfeasible, with the microscopes typically used in haematology due to the fundamental trade-off between magnification and spatial resolution. To address this, we investigated three state-of-the-art approaches to microscopic imaging of blood films which leverage recent advances in optical and computational imaging and analysis to increase the information capture capacity of the optical microscope: optical mesoscopy, which uses a giant microscope objective (Mesolens) to enable high-resolution imaging at low magnification; Fourier ptychographic microscopy, a computational imaging method which relies on oblique illumination with a series of LEDs to capture high-resolution information; and deep neural networks which can be trained to increase the quality of low magnification, low resolution images. We compare and contrast the performance of these techniques for blood film imaging for the exemplar case of Giemsa-stained peripheral blood smears. Using computational image analysis and shape-based object classification, we demonstrate their use for automated analysis of red blood cell morphology and visualization and detection of small blood-borne parasites such as the malarial parasite Plasmodium falciparum. Our results demonstrate that these new methods greatly increase the information capturing capacity of the light microscope, with transformative potential for haematology and more generally across digital pathology. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

A low-cost and high-throughput benchtop cell sorter for isolating white blood cells from whole blood

The ability to isolate and purify white blood cells (WBCs) from mixed ensembles such as blood would benefit autologous cell-based therapeutics as well as diagnosis of WBC disorders. Current WBCs isolation methods have the limitations of low purity or requiring complex and expensive equipment. In addition, due to the overlap in size distribution between lymphocytes (i.e., a sub-population of WBCs) and red blood cells (RBCs), it is challenging to achieve isolation of entire WBCs populations. In this work, we developed an inertial microfluidics-based cell sorter, which enables size-based, high-throughput isolation, and enrichment of WBCs from RBC-lysed whole blood. Using the developed inertial microfluidic chip, the sorting resolution is sharpened within 2 μm, which achieved separation between 3 and 5 μm diameter particles. Thus, with the present cell sorter, a full population of WBCs can be isolated from RBC-lysed blood samples with recovery ratio of 92%, and merely 5% difference in the composition percentage of the three subpopulations of granulocytes, monocytes, and lymphocytes compared to the original sample. Furthermore, our cell sorter is designed to enable broad application of size-based inertial cell sorting by supplying a series of microchips with different sorting cutoff size. This strategy allows us to further enrich the lymphocytes population by twofold using another microchip with a cutoff size between 10 and 15 μm. With simplicity and efficiency, our cell sorter provides a powerful platform for isolating and sorting of WBCs and also envisions broad potential sorting applications for other cell types.

White blood cell detection and classification using Euler’s Jenks optimized multinomial logistic neural networks

Due to the wide acceptance of White Blood Cells (WBCs) in disease diagnosis, detection and classification of WBC are hot topic. Existing methodologies have some drawbacks such as significant degree of error, higher accuracy, time bound and higher misclassification rate. A WBCs detection and classification called, Jenks Optimized Logistic Convolutional Neural Network (JO-LCNN) method has proposed. Initally, Eulers Principal Axis is used as a convolution model to obtain a rotation invariant form of image by differentiating the background and RBCs, then eliminating them which leaves only the WBCs. By eliminating the wanton features, inherent features are detected contributing to minimum misclassification rate. According to above, Jenks Optimization function is used as a pooling model to obtain feature map for lower resolution. Therefore JO-LCNN is used for removing tiny objects in image and complete nuclei. Finally, Multinomial Logistic classifier is used to classify five types of classes by means of loss function and updating weight according to the loss function, therefore classifying with higher accuracy rate. Using LISC database for WBCs with different parameters as classification accuracy, false positive rate and time complexity are performed. Result shows that JO-LCNN, efficiently improves accuracy with less time, misclassification rate than the state-of-art methods.

An efficient Fourier ptychographic microscopy method based on optimized pattern of LED angle illumination

Considering angle diversity and synthetic aperture, Fourier ptychographic microscopy (FPM) could address contradiction of high resolution and wide field of view. However, in the conventional FPM method, large capture quantity leads to poor efficiency. So, an efficient FPM method based on optimized pattern of LED angle illumination is proposed. Firstly, from position relationship between the LED, aperture and sample, we obtain the theoretical expandable spectrum range and the spectrum distribution in Fourier space of all LEDs. Secondly, we u se image quality assessment methods to extract differential expressions between an arbitrary LED illumination and full LEDs illumination. Thirdly, the optimized angle illumination strategy is achieved according to the analysis of different expressions. Based on this method, we design a rhombus-based illumination method for the LED array to accelerate the FPM efficiency. Finally, we validate the effectiveness and efficiency of our method with both simulated and real experiments. The results indicate that our method can effectively improve the efficiency for FPM without sacrificing image reconstruction quality.

Forward imaging neural network with correction of positional misalignment for Fourier ptychographic microscopy

Fourier ptychographic microscopy (FPM) is a computational imaging technology used to achieve high-resolution imaging with a wide field-of-view. The existing methods of FPM suffer from the positional misalignment in the system, by which the quality of the recovered high-resolution image is determined. In this paper, a forward neural network method with correction of the positional misalignment (FNN-CP) is proposed based on TensorFlow, which consists of two models. Both the spectrum of the sample and four global position factors, which are introduced to describe the positions of the LED elements, are treated as the learnable weights in layers in the first model. By minimizing the loss function in the training process, the positional error can be corrected based on the trained position factors. In order to fit the wavefront aberrations caused by optical components in the FPM system for better recovery results, the second model is designed, in which the spectrum of the sample and coefficients of different Zernike modes are treated as the learnable weights in layers. After the training process of the second model, the wavefront aberration can be fit according to the coefficients of different Zernike modes and the high-resolution complex image can be obtained based on the trained spectrum of the sample. Both the simulation and experiment have been performed to verify the effectiveness of our proposed method. Compared with the state-of-art FPM methods based on forward neural network, FNN-CP can achieve the best reconstruction results.

Digital refocusing and extended depth of field reconstruction in Fourier ptychographic microscopy

Fourier ptychography microscopy (FPM) is a recently developed microscopic imaging method that allows the recovery of a high-resolution complex image by combining a sequence of bright and darkfield images acquired under inclined illumination. The capacity of FPM for high resolution imaging at low magnification makes it particularly attractive for applications in digital pathology which require imaging of large specimens such as tissue sections and blood films. To date most applications of FPM have been limited to imaging thin samples, simplifying both image reconstruction and analysis. In this work we show that, for samples of intermediate thickness (defined here as less than the depth of field of a raw captured image), numerical propagation of the reconstructed complex field allows effective digital refocusing of FPM images. The results are validated by comparison against images obtained with an equivalent high numerical aperture objective lens. We find that post reconstruction refocusing (PRR) yields images comparable in quality to adding a defocus term to the pupil function within the reconstruction algorithm, while reducing computing time by several orders of magnitude. We apply PRR to visualize FPM images of Giemsa-stained peripheral blood films and present a novel image processing pipeline to construct an effective extended depth of field image which optimally displays the 3D sample structure in a 2D image. We also show how digital refocusing allows effective correction of the chromatic focus shifts inherent to the low magnification objective lenses used in FPM setups, improving the overall quality of color FPM images.

Apodized coherent transfer function constraint for partially coherent Fourier ptychographic microscopy

Fourier ptychographic microscopy (FPM) is a recently developed computational microscopy approach that produces both wide field-of-view (FOV) and high resolution (HR) intensity and a phase image of the sample. Inspired by the ideas of synthetic aperture and phase retrieval, FPM iteratively stitches multiple low-resolution (LR) images with variable illumination angles in Fourier space to reconstruct an HR complex image. Typically, FPM illuminating the sample with an LED array is approximated as a coherent imaging process, and the coherent transfer function (CTF) is imposed as a support constraint in Fourier space. However, a millimeter-scale LED is inapposite to be treated as a coherent light source. As a result, the quality of reconstructed image is degraded by the inappropriate approximation. In this paper, we analyze the coherence of an FPM system and propose a novel constraint approach termed Apodized CTF (AC) constraint in Fourier space. Results on both simulated data and actual captured data show that this new constraint is more stable and robust than CTF. This approach can also relax the coherence requirement of illumination. In addition, it is simple, does not require additional computations, and is easy to be embedded in almost all the reconstruction algorithms proposed so far.

Fast and robust misalignment correction of Fourier ptychographic microscopy for full field of view reconstruction

Fourier ptychographic microscopy (FPM) is a newly developed computational imaging technique that can provide gigapixel images with both high resolution (HR) and wide field of view (FOV). However, there are two possible reasons for position misalignment, which induce a degradation of the reconstructed image. The first one is the position misalignment of the LED array, which can largely be eliminated during the experimental system building process. The more important one is the segment-dependent position misalignment. Note that, this segment-dependent positional misalignment still exists, even after we correct the central coordinates of every small segment. In this paper, we carefully analyze this segment-dependent misalignment and find that this global shift matters more, compared with the rotational misalignments. According to this fact, we propose a robust and fast method to correct the two factors of position misalignment of the FPM, termed as misalignment correction for the FPM misalignment correction (mcFPM). Although different regions in the FOV have different sensitivities to the position misalignment, the experimental results show that the mcFPM is robust with respect to the elimination of each region. Compared with the state-of-the-art methods, the mcFPM is much faster.

An on-chip instrument for white blood cells classification based on a lens-less shadow imaging technique

Routine blood tests provide important basic information for disease diagnoses. The proportions of three subtypes of white blood cells (WBCs), which are neutrophils, monocytes, lymphocytes, is key information for disease diagnosis. However, current instruments for routine blood tests, such as blood cell analyzers, flow cytometers, and optical microscopes, are cumbersome, time consuming and expensive. To make a smaller, automatic low-cost blood cell analyzer, much research has focused on a technique called lens-less shadow imaging, which can obtain microscopic images of cells in a lens-less system. Nevertheless, the efficiency of this imaging system is not satisfactory because of two problems: low resolution and imaging diffraction phenomena. In this paper, a novel method of classifying cells with the shadow imaging technique was proposed. It could be used for the classification of the three subtypes of WBCs, and the correlation of the results of classification between the proposed system and the reference system (BC-5180, Mindray) was 0.93. However, the instrument was only 10 × 10 × 10 cm, and the cost was less than $100. Depending on the lens-free shadow imaging technology, the main hardware could be integrated on a chip scale and could be called an on-chip instrument.

Wide-field Fourier ptychographic microscopy using laser illumination source

Fourier ptychographic (FP) microscopy is a coherent imaging method that can synthesize an image with a higher bandwidth using multiple low-bandwidth images captured at different spatial frequency regions. The method's demand for multiple images drives the need for a brighter illumination scheme and a high-frame-rate camera for a faster acquisition. We report the use of a guided laser beam as an illumination source for an FP microscope. It uses a mirror array and a 2-dimensional scanning Galvo mirror system to provide a sample with plane-wave illuminations at diverse incidence angles. The use of a laser presents speckles in the image capturing process due to reflections between glass surfaces in the system. They appear as slowly varying background fluctuations in the final reconstructed image. We are able to mitigate these artifacts by including a phase image obtained by differential phase contrast (DPC) deconvolution in the FP algorithm. We use a 1-Watt laser configured to provide a collimated beam with 150 mW of power and beam diameter of 1 cm to allow for the total capturing time of 0.96 seconds for 96 raw FPM input images in our system, with the camera sensor's frame rate being the bottleneck for speed. We demonstrate a factor of 4 resolution improvement using a 0.1 NA objective lens over the full camera field-of-view of 2.7 mm by 1.5 mm.

Incubator embedded cell culture imaging system (EmSight) based on Fourier ptychographic microscopy

Multi-day tracking of cells in culture systems can provide valuable information in bioscience experiments. We report the development of a cell culture imaging system, named EmSight, which incorporates multiple compact Fourier ptychographic microscopes with a standard multiwell imaging plate. The system is housed in an incubator and presently incorporates six microscopes. By using the same low magnification objective lenses as the objective and the tube lens, the EmSight is configured as a 1:1 imaging system that, providing large field-of-view (FOV) imaging onto a low-cost CMOS imaging sensor. The EmSight improves the image resolution by capturing a series of images of the sample at varying illumination angles; the instrument reconstructs a higher-resolution image by using the iterative Fourier ptychographic algorithm. In addition to providing high-resolution brightfield and phase imaging, the EmSight is also capable of fluorescence imaging at the native resolution of the objectives. We characterized the system using a phase Siemens star target, and show four-fold improved coherent resolution (synthetic NA of 0.42) and a depth of field of 0.2 mm. To conduct live, long-term dopaminergic neuron imaging, we cultured ventral midbrain from mice driving eGFP from the tyrosine hydroxylase promoter. The EmSight system tracks movements of dopaminergic neurons over a 21 day period.

Sputum and serum microRNA-144 levels in patients with tuberculosis before and after treatment

OBJECTIVE

To measure the expression levels of sputum and serum microRNA-144 (miR-144) before and after the treatment of patients with tuberculosis (TB).

METHODS

Details of the cases of a total of 124 TB patients were collected at Qilu Hospital of Shandong University between April 2014 and April 2015. Fifty-three of these patients had sputum positive for bacteria and a cavity on imaging (group A), 20 patients had sputum negative for bacteria and a cavity on imaging (group B), and 51 patients had sputum negative for bacteria and no cavity on imaging (group C). One hundred seventeen healthy people who attended the hospital for a physical examination were recruited as controls. Quantitative real-time PCR (qRT-PCR) was used to measure the levels of sputum and serum miR-144 before anti-TB treatment and at 1 month after treatment.

RESULTS

Before treatment, sputum and serum miR-144 expression levels in the TB patients were both higher than those of the controls (both p<0.05). After treatment, sputum and serum miR-144 levels in the TB patients were significantly lower than those measured before treatment (both p<0.05). The levels of sputum and serum miR-144 in the improved TB patients decreased significantly after treatment compared to those measured before treatment (both p<0.001). Significant differences were found in sputum and serum miR-144 levels in the TB patients, with or without improvement, compared with the healthy controls (all p<0.05).

CONCLUSION

Sputum and serum miR-144 levels were significantly upregulated in the TB patients, but were found to decrease significantly after anti-TB treatment.