Lens Free Holographic Imaging for Urinary Tract Infection Screening.

OBJECTIVE: The diagnosis of urinary tract infection (UTI) currently requires precise specimen collection, handling infectious human waste, controlled urine storage, and timely transportation to modern laboratory equipment for analysis. Here we investigate holographic lens free imaging (LFI) to show its promise for enabling automatic urine analysis at the patient bedside. METHODS: We introduce an LFI system capable of resolving important urine clinical biomarkers such as red blood cells, white blood cells, crystals, and casts in 2 mm thick urine phantoms. RESULTS: This approach is sensitive to the particulate concentrations relevant for detecting several clinical urine abnormalities such as hematuria and pyuria, linearly correlating to ground truth hemacytometer measurements with R 2 = 0.9941 and R 2 = 0.9973, respectively. We show that LFI can estimate E. coli concentrations of 10 3 to 10 5 cells/mL by counting individual cells, and is sensitive to concentrations of 10 5 cells/mL to 10 8 cells/mL by analyzing hologram texture. Further, LFI measurements of blood cell concentrations are relatively insensitive to changes in bacteria concentrations of over seven orders of magnitude. Lastly, LFI reveals clear differences between UTI-positive and UTI-negative urine from human patients. CONCLUSION: LFI is sensitive to clinically-relevant concentrations of bacteria, blood cells, and other sediment in large urine volumes. SIGNIFICANCE: Together, these results show promise for LFI as a tool for urine screening, potentially offering early, point-of-care detection of UTI and other pathological processes.

Adaptive sparse reconstruction for lensless digital holography via PSF estimation and phase retrieval.

In-line lensless digital holography has great potential in multiple applications; however, reconstructing high-quality images from a single recorded hologram is challenging due to the loss of phase information. Typical reconstruction methods are based on solving a regularized inverse problem and work well under suitable image priors, but they are extremely sensitive to mismatches between the forward model and the actual imaging system. This paper aims to improve the robustness of such algorithms by introducing the adaptive sparse reconstruction method, ASR, which learns a properly constrained point spread function (PSF) directly from data, as opposed to solely relying on physics-based approximations of it. ASR jointly performs holographic reconstruction, PSF estimation, and phase retrieval in an unsupervised way by maximizing the sparsity of the reconstructed images. Like traditional methods, ASR uses the image formation model along with a sparsity prior, which, unlike recent deep learning approaches, allows for unsupervised reconstruction with as little as one sample. Experimental results in synthetic and real data show the advantages of ASR over traditional reconstruction methods, especially in cases where the theoretical PSF does not match that of the actual system.

Scattering oblique plane microscopy for in-vivo blood cell imaging.

Oblique plane microscopy (OPM) enables high speed, volumetric fluorescence imaging through a single-objective geometry. While these advantages have positioned OPM as a valuable tool to probe biological questions in animal models, its potential for in vivo human imaging is largely unexplored due to its typical use with exogenous fluorescent dyes. Here we introduce a scattering-contrast oblique plane microscope (sOPM) and demonstrate label-free imaging of blood cells flowing through human capillaries in vivo. The sOPM illuminates a capillary bed in the ventral tongue with an oblique light sheet, and images side- and back- scattered signal from blood cells. By synchronizing the sOPM with a conventional capillaroscope, we acquire paired widefield and axial images of blood cells flowing through a capillary loop. The widefield capillaroscope image provides absorption contrast and confirms the presence of red blood cells (RBCs), while the sOPM image may aid in determining whether optical absorption gaps (OAGs) between RBCs have cellular or acellular composition. Further, we demonstrate consequential differences between fluorescence and scattering versions of OPM by imaging the same polystyrene beads sequentially with each technique. Lastly, we substantiate in vivo observations by imaging isolated red blood cells, white blood cells, and platelets in vitro using 3D agar phantoms. These results demonstrate a promising new avenue towards in vivo blood analysis.

Diffuser-based computational imaging funduscope.

Poor access to eye care is a major global challenge that could be ameliorated by low-cost, portable, and easy-to-use diagnostic technologies. Diffuser-based imaging has the potential to enable inexpensive, compact optical systems that can reconstruct a focused image of an object over a range of defocus errors. Here, we present a diffuser-based computational funduscope that reconstructs important clinical features of a model eye. Compared to existing diffuser-imager architectures, our system features an infinite-conjugate design by relaying the ocular lens onto the diffuser. This offers shift-invariance across a wide field-of-view (FOV) and an invariant magnification across an extended depth range. Experimentally, we demonstrate fundus image reconstruction over a 33° FOV and robustness to ±4D refractive error using a constant point-spread-function. Combined with diffuser-based wavefront sensing, this technology could enable combined ocular aberrometry and funduscopic screening through a single diffuser sensor.

Imaging human blood cells in vivo with oblique back-illumination capillaroscopy.

We present a non-invasive, label-free method of imaging blood cells flowing through human capillaries in vivo using oblique back-illumination capillaroscopy (OBC). Green light illumination allows simultaneous phase and absorption contrast, enhancing the ability to distinguish red and white blood cells. Single-sided illumination through the objective lens enables 200 Hz imaging with close illumination-detection separation and a simplified setup. Phase contrast is optimized when the illumination axis is offset from the detection axis by approximately 225 µm when imaging ∼80 µm deep in phantoms and human ventral tongue. We demonstrate high-speed imaging of individual red blood cells, white blood cells with sub-cellular detail, and platelets flowing through capillaries and vessels in human tongue. A custom pneumatic cap placed over the objective lens stabilizes the field of view, enabling longitudinal imaging of a single capillary for up to seven minutes. We present high-quality images of blood cells in individuals with Fitzpatrick skin phototypes II, IV, and VI, showing that the technique is robust to high peripheral melanin concentration. The signal quality, speed, simplicity, and robustness of this approach underscores its potential for non-invasive blood cell counting.

Visualization of blood cell contrast in nailfold capillaries with high-speed reverse lens mobile phone microscopy.

Quantification of optical absorption gaps in nailfold capillaries has recently shown promise as a non-invasive technique for neutropenia screening. Here we demonstrate a low-cost, portable attachment to a mobile phone that can resolve optical absorption gaps in nailfold capillaries using a reverse lens technique and oblique 520nm illumination. Resolution <4µm within a 1mm2 on-axis region is demonstrated, and wide field of view (3.5mm × 4.8mm) imaging is achieved with resolution <6µm in the periphery. Optical absorption gaps (OAGs) are visible in superficial capillary loops of a healthy human participant by an ∼8-fold difference in contrast-to-noise ratio with respect to red blood cell absorption contrast. High speed video capillaroscopy up to 240 frames per second (fps) is possible, though 60fps is sufficient to resolve an average frequency of 37 OAGs/minute passing through nailfold capillaries. The simplicity and portability of this technique may enable the development of an effective non-invasive tool for white blood cell screening in point-of-care and global health settings.

Deep Adversarial Training for Multi-Organ Nuclei Segmentation in Histopathology Images.

Nuclei mymargin segmentation is a fundamental task for various computational pathology applications including nuclei morphology analysis, cell type classification, and cancer grading. Deep learning has emerged as a powerful approach to segmenting nuclei but the accuracy of convolutional neural networks (CNNs) depends on the volume and the quality of labeled histopathology data for training. In particular, conventional CNN-based approaches lack structured prediction capabilities, which are required to distinguish overlapping and clumped nuclei. Here, we present an approach to nuclei segmentation that overcomes these challenges by utilizing a conditional generative adversarial network (cGAN) trained with synthetic and real data. We generate a large dataset of H&E training images with perfect nuclei segmentation labels using an unpaired GAN framework. This synthetic data along with real histopathology data from six different organs are used to train a conditional GAN with spectral normalization and gradient penalty for nuclei segmentation. This adversarial regression framework enforces higher-order spacial-consistency when compared to conventional CNN models. We demonstrate that this nuclei segmentation approach generalizes across different organs, sites, patients and disease states, and outperforms conventional approaches, especially in isolating individual and overlapping nuclei.

Large dynamic range autorefraction with a low-cost diffuser wavefront sensor.

Wavefront sensing with a thin diffuser has emerged as a potential low-cost alternative to a lenslet array for aberrometry. Here we show that displacement of caustic patterns can be tracked for estimating wavefront gradient in a diffuser wavefront sensor (DWFS), enabling large dynamic-range wavefront measurements with sufficient accuracy for eyeglass prescription measurements. We compare the dynamic range, repeatability, precision, and number of resolvable prescriptions of a DWFS to a Shack-Hartmann wavefront sensor (SHWFS) for autorefraction measurement. We induce spherical and cylindrical errors in a model eye and use a multi-level Demon's non-rigid registration algorithm to estimate caustic displacements relative to an emmetropic model eye. When compared to spherical error measurements with the SHWFS using a laser diode with a laser speckle reducer, the DWFS demonstrates a ∼5-fold improvement in dynamic range (-4.0 to +4.5 D vs. -22.0 to +19.5 D) with less than half the reduction in resolution (0.072 vs. 0.116 D), enabling a ∼3-fold increase in the number of resolvable prescriptions (118 vs. 358). In addition to being lower-cost, the unique, non-periodic nature of the caustic pattern formed by a diffuser enables a larger dynamic range of aberration measurements compared to a lenslet array.