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This article is the second within a three-part series on Fourier ptychography, which is a computational microscopy technique for high-resolution, large field-of-view imaging. While the first article laid out the basics of Fourier ptychography, this second part sheds light on its algorithmic ingredients. We present a non-technical discussion of phase retrieval, which allows for the synthesis of high-resolution images from a sequence of low-resolution raw data. Fourier ptychographic phase retrieval can be carried out on standard, widefield microscopy platforms with the simple addition of a low-cost LED array, thus offering a convenient alternative to other phase-sensitive techniques that require more elaborate hardware such as differential interference contrast and digital holography.

Fourier ptychography is an emerging computational microscopy technique that can generate gigapixel-scale images of biological samples. With only the addition of a low-cost LED array to a standard digital microscope and a reconstruction algorithm, Fourier ptychography overcomes the fundamental trade-off between a microscope's resolution and field-of-view without any moving parts. This article is the first in a three-part series that aims to introduce the fundamentals of the technology to the broader microscopy community and beyond, using intuitive explanations.

SUMMARYDevelopmentally programmed polyploidy (whole-genome-duplication) of cardiomyocytes is common across evolution. Functions of such polyploidy are essentially unknown. Here, we reveal roles for precise polyploidy levels in cardiac tissue. We highlight a conserved asymmetry in polyploidy level between cardiac chambers inDrosophilalarvae and humans. InDrosophila, differential Insulin Receptor (InR) sensitivity leads the heart chamber to reach a higher ploidy/cell size relative to the aorta chamber. Cardiac ploidy-reduced animals exhibit reduced heart chamber size, stroke volume, cardiac output, and acceleration of circulating hemocytes. TheseDrosophilaphenotypes mimic systemic human heart failure. Using human donor hearts, we reveal asymmetry in nuclear volume (ploidy) and insulin signaling between the left ventricle and atrium. Our results identify productive and likely conserved roles for polyploidy in cardiac chambers and suggest precise ploidy levels sculpt many developing tissues. These findings of productive cardiomyocyte polyploidy impact efforts to block developmental polyploidy to improve heart injury recovery.

To study the behavior of freely moving model organisms such as zebrafish (Danio rerio) and fruit flies (Drosophila) across multiple spatial scales, it would be ideal to use a light microscope that can resolve 3D information over a wide field of view (FOV) at high speed and high spatial resolution. However, it is challenging to design an optical instrument to achieve all of these properties simultaneously. Existing techniques for large-FOV microscopic imaging and for 3D image measurement typically require many sequential image snapshots, thus compromising speed and throughput. Here, we present 3D-RAPID, a computational microscope based on a synchronized array of 54 cameras that can capture high-speed 3D topographic videos over a 135-cm^2 area, achieving up to 230 frames per second at throughputs exceeding 5 gigapixels (GPs) per second. 3D-RAPID features a 3D reconstruction algorithm that, for each synchronized temporal snapshot, simultaneously fuses all 54 images seamlessly into a globally-consistent composite that includes a coregistered 3D height map. The self-supervised 3D reconstruction algorithm itself trains a spatiotemporally-compressed convolutional neural network (CNN) that maps raw photometric images to 3D topography, using stereo overlap redundancy and ray-propagation physics as the only supervision mechanism. As a result, our end-to-end 3D reconstruction algorithm is robust to generalization errors and scales to arbitrarily long videos from arbitrarily sized camera arrays. The scalable hardware and software design of 3D-RAPID addresses a longstanding problem in the field of behavioral imaging, enabling parallelized 3D observation of large collections of freely moving organisms at high spatiotemporal throughputs, which we demonstrate in ants (Pogonomyrmex barbatus), fruit flies, and zebrafish larvae.

This paper experimentally examines different configurations of a multi-camera array microscope (MCAM) imaging technology. The MCAM is based upon a densely packed array of “micro-cameras” to jointly image across a large field-of-view (FOV) at high resolution. Each micro-camera within the array images a unique area of a sample of interest, and then all acquired data with 54 micro-cameras are digitally combined into composite frames, whose total pixel counts significantly exceed the pixel counts of standard microscope systems. We present results from three unique MCAM configurations for different use cases. First, we demonstrate a configuration that simultaneously images and estimates the 3D object depth across a 100×135mm2 FOV at approximately 20 µm resolution, which results in 0.15 gigapixels (GP) per snapshot. Second, we demonstrate an MCAM configuration that records video across a continuous 83×123mm2 FOV with twofold increased resolution (0.48 GP per frame). Finally, we report a third high-resolution configuration (2 µm resolution) that can rapidly produce 9.8 GP composites of large histopathology specimens.

Ophthalmic microsurgery is technically difficult because the scale of required surgical tool manipulations challenge the limits of the surgeon's visual acuity, sensory perception, and physical dexterity. Intraoperative optical coherence tomography (OCT) imaging with micrometer-scale resolution is increasingly being used to monitor and provide enhanced real-time visualization of ophthalmic surgical maneuvers, but surgeons still face physical limitations when manipulating instruments inside the eye. Autonomously controlled robots are one avenue for overcoming these physical limitations. In this article, we demonstrate the feasibility of using learning from demonstration and reinforcement learning with an industrial robot to perform OCT-guided corneal needle insertions in an ex vivo model of deep anterior lamellar keratoplasty (DALK) surgery. Our reinforcement learning agent trained on ex vivo human corneas, then outperformed surgical fellows in reaching a target needle insertion depth in mock corneal surgery trials. This article shows the combination of learning from demonstration and reinforcement learning is a viable option for performing OCT-guided robotic ophthalmic surgery.

We present a multi-modal fiber array snapshot technique (M-FAST) based on an array of 96 compact cameras placed behind a primary objective lens and a fiber bundle array. Our technique is capable of large-area, high-resolution, multi-channel video acquisition. The proposed design provides two key improvements to prior cascaded imaging system approaches: a novel optical arrangement that accommodates the use of planar camera arrays, and a new ability to acquire multi-modal image data acquisition. M-FAST is a multi-modal, scalable imaging system that can acquire snapshot dual-channel fluorescence images as well as differential phase contrast measurements over a large 6.59 mm × 9.74 mm field-of-view at 2.2-μm center full-pitch resolution.

Optical coherence tomography (OCT) is a cross-sectional, micrometre-scale imaging modality with widespread clinical application. Typical OCT systems sacrifice lateral resolution to achieve long depths of focus for bulk tissue imaging, and therefore tend to have better axial than lateral resolution. Such anisotropic resolution can obscure fine ultrastructural features. Furthermore, conventional OCT suffers from refraction-induced image distortions. Here, we introduce optical coherence refraction tomography (OCRT), which extends the superior axial resolution to the lateral dimension, synthesizing undistorted cross-sectional image reconstructions from multiple conventional images acquired with angular diversity. In correcting refraction-induced distortions to register the OCT images, OCRT also achieves spatially resolved refractive index imaging. We demonstrate greater than threefold improvement in lateral resolution as well as speckle reduction in imaging the tissue ultrastructure, consistent with histology. With further optimization in optical designs to incorporate angular diversity into clinical instruments, OCRT could be widely applied as an enhancement over conventional OCT. By synthesizing undistorted cross-sectional image reconstructions from multiple conventional images acquired with angular diversity, optical coherence refraction tomography offers greater than threefold improvement in lateral resolution and speckle reduction in imaging tissue ultrastructure, and reconstructs the tissue’s internal refractive index distribution.

We present a new multi-camera array microscope (MCAM) that can capture images at single micrometer resolution over 54 cm2 fields of view, which contain up to 16 gigapixels, and associated software for seamless viewing and analysis.

We present a novel spectral-scanning FMCW 3D imaging and velocimetry system that can produce 3D depth maps of 507 X 500 pixels at 33 Hz, with 48° X 68° FOV and 32.8 cm depth range.

Optical coherence tomography (OCT) has seen widespread success as an in vivo clinical diagnostic 3D imaging modality, impacting areas including ophthalmology, cardiology, and gastroenterology. Despite its many advantages, such as high sensitivity, speed, and depth penetration, OCT suffers from several shortcomings that ultimately limit its utility as a 3D microscopy tool, such as its pervasive coherent speckle noise and poor lateral resolution required to maintain millimeter-scale imaging depths. Here, we present 3D optical coherence refraction tomography (OCRT), a computational extension of OCT that synthesizes an incoherent contrast mechanism by combining multiple OCT volumes, acquired across two rotation axes, to form a resolution-enhanced, speckle-reduced, refraction-corrected 3D reconstruction. Our label-free computational 3D microscope features a novel optical design incorporating a parabolic mirror to enable the capture of 5D plenoptic datasets, consisting of millimetric 3D fields of view over up to ± 75 ∘ without moving the sample. We demonstrate that 3D OCRT reveals 3D features unobserved by conventional OCT in fruit fly, zebrafish, and mouse samples.

This paper presents a microscopic imaging technique that uses variable-angle illumination to recover the complex polarimetric properties of a specimen at high resolution and over a large field-of-view. The approach extends Fourier ptychography, which is a synthetic aperture-based imaging approach to improve resolution with phaseless measurements, to additionally account for the vectorial nature of light. After images are acquired using a standard microscope outfitted with an LED illumination array and two polarizers, our vectorial Fourier ptychography (vFP) algorithm solves for the complex 2x2 Jones matrix of the anisotropic specimen of interest at each resolved spatial location. We introduce a new sequential Gauss-Newton-based solver that additionally jointly estimates and removes polarization-dependent imaging system aberrations. We demonstrate effective vFP performance by generating large-area (29 mm2), high-resolution (1.24 μm full-pitch) reconstructions of sample absorption, phase, orientation, diattenuation, and retardance for a variety of calibration samples and biological specimens.

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.

Microscopic imaging of anisotropic samples has many important applications in cytopathology. The endogenous contrast from the polarization properties of a specimen, such as its birefringence, provides valuable diagnostic information for several deadly diseases, including cardiac amyloidosis and squamous cell carcinoma, for example. In the past, polarized light microscopy (PLM) has been widely used as a diagnostic tool during the clinical review. However, in analogy with the standard microscope, the PLM typically has a restricted spatial-bandwidth product (SBP). As a consequence, one can either image a large area with low resolution or see the details of a very small area of the sample at the resolutions required for accurate analysis. To address the SBP issue of the PLM, we propose a computational microscopy method, termed vectorial Fourier ptychography, to illuminate the specimen with polarized light from different angles and detects different polarization states of the diffracted light. By illuminating a specimen with plane waves from different angles, our vectorial Fourier ptychography method effectively modulates the high-spatial-frequency components of the specimen into lower frequencies that can be detected by the optical system. With a Jones calculus-based forward model and a second-order phase retrieval method, we can reconstruct high-resolution, wide field-of-view(FOV) amplitude, phase, birefringence, retardance, and diattenuation of the specimen. To assess the reconstruction accuracy of our method, we imaged polystyrene beads submerged in immersion oils of different refractive index, as well as monosodium urate crystals. Further, To validate the diattenuation reconstruction accuracy, we reconstruct a USAF resolution test chart with a half blocked by a linear polarizer. These experiments confirm quantitatively accurate reconstruction results with a 1.25 um full-pitch resolution over a FOV of 6.6 x 4.4 mm^2, which is 5 times higher than the native (brightfield) resolution of the non-computational optical system. Finally, we demonstrate our technique by producing high SBP polarization images of several anisotropic biologic samples, includes collagen tissue, congo red stained cardiac tissue, and a bean root sample.

We present a feature-free photogrammetric technique that enables quantitative 3D mesoscopic (mm-scale height variation) imaging with tens-of-micron accuracy from sequences of images acquired by a smartphone at close range (several cm) under freehand motion without additional hardware. Our end-to-end, pixel-intensity-based approach jointly registers and stitches all the images by estimating a coaligned height map, which acts as a pixel-wise radial deformation field that orthorectifies each camera image to allow plane-plus-parallax registration. The height maps themselves are reparameterized as the output of an untrained encoder-decoder convolutional neural network (CNN) with the raw camera images as the input, which effectively removes many reconstruction artifacts. Our method also jointly estimates both the camera’s dynamic 6D pose and its distortion using a nonparametric model, the latter of which is especially important in mesoscopic applications when using cameras not designed for imaging at short working distances, such as smartphone cameras. We also propose strategies for reducing computation time and memory, applicable to other multi-frame registration problems. Finally, we demonstrate our method using sequences of multi-megapixel images captured by an un-stabilized smartphone on a variety of samples (e.g., painting brushstrokes, circuit board, seeds).

We present a real-time spectral-scanning frequency-modulated continuous wave (FMCW) 3D imaging and velocimetry system that can produce 3D depth maps at 33 Hz, with 48° × 68° field of view (FOV) and 32.8-cm depth range. Each depth map consists of 507 × 500 pixels, with 0.095° × 0.14° angular resolution and 2.82-mm depth resolution. The system employs a grating for beam steering and a telescope for angular FOV magnification. Quantitative depth, reflectivity, and axial velocity measurements of a static 3D printed depth variation target and a moving robotic arm are demonstrated.

We present a gigapixel-scale multi-aperture microscope capable of measuring a sample’s 3D height profile over multi-centimeter-scale fields of view with a series of single synchronized camera snapshots. Exploiting the overlap redundancy in our multi-aperture camera array microscope, we developed a novel, end-to-end photogrammetric reconstruction algorithm that simultaneously calibrates the cameras’ 3D positions and poses, stitches the acquired images, and generates a coregistered, pixel-wise 3D height map of the sample. Our work opens the door to video-rate 3D monitoring of dynamic scenes at micrometer-scale resolutions and centimeter-scale fields of view.

Anterior uveitis is the most common form of intraocular inflammation, and one of its main signs is the presence of white blood cells (WBCs) in the anterior chamber (AC). Clinically, the true composition of cells can currently only be obtained using AC paracentesis, an invasive procedure to obtain AC fluid requiring needle insertion into the AC. We previously developed a spectroscopic optical coherence tomography (SOCT) analysis method to differentiate between populations of RBCs and subtypes of WBCs, including granulocytes, lymphocytes and monocytes, both in vitro and in ACs of excised porcine eyes. We have shown that different types of WBCs have distinct characteristic size distributions, extracted from the backscattered reflectance spectrum of individual cells using Mie theory. Here, we further develop our method to estimate the composition of blood cell mixtures, both in vitro and in vivo. To do so, we estimate the size distribution of unknown cell mixtures by fitting the distribution observed using SOCT with a weighted combination of reference size distributions of each WBC type calculated using kernel density estimation. We validate the accuracy of our estimation in an in vitro study, by comparing our results for a given WBC sample mixture with the cellular concentrations measured by a hemocytometer and SOCT images before mixing. We also conducted a small in vivo quantitative cell mixture validation pilot study which demonstrates congruence between our method and AC paracentesis in two patients with uveitis. The SOCT based method appears promising to provide quantitative diagnostic information of cellular responses in the ACs of patients with uveitis.

We present 3D optical coherence refraction tomography (OCRT), which incoherently synthesizes an extended 3D transfer function using multi-view OCT volumes spanning two angular dimensions. Our setup features a novel use of parabolic mirrors, achieving millimetric fields of view over ±75 ◦ without rotation stages.

We present a general theory of optical coherence tomography (OCT), which synthesizes the fundamental concepts and implementations of OCT under a common 3D k -space framework. At the heart of this analysis is the Fourier diffraction theorem, which relates the coherent interaction between a sample and plane wave to the Ewald sphere in the 3D k -space representation of the sample. While only the axial dimension of OCT is typically analyzed in k -space, we show that embracing a fully 3D k -space formalism allows explanation of nearly every fundamental physical phenomenon or property of OCT, including contrast mechanism, resolution, dispersion, aberration, limited depth of focus, and speckle. The theory also unifies diffraction tomography, confocal microscopy, point-scanning OCT, line-field OCT, full-field OCT, Bessel beam OCT, transillumination OCT, interferometric synthetic aperture microscopy (ISAM), and optical coherence refraction tomography (OCRT), among others. Our unified theory not only enables clear understanding of existing techniques but also suggests new research directions to continue advancing the field of OCT.

Recently, there has been growing interest and effort in developing high-speed high-precision 3D imaging technologies for a wide range of industrial, automotive and biomedical applications. Optical frequency-modulated continuous wave (FMCW) light detection and ranging (LiDAR), which shares the same working principle as swept-source optical coherence tomography (SSOCT), is an emerging 3D surface imaging technology that offers higher sensitivity and resolution than conventional time-of-flight (ToF) ranging. Recently, with the development of high-performance swept sources with meter-scale instantaneous coherence lengths, the imaging range of both OCT and FMCW has been significantly improved. However, due to the limited bandwidth of current generation digitizers and the speed limitations of beam steering using mechanical scanners, long range OCT and FMCW typically suffer from a low 3D frame rate (

We present a tomographic imaging technique, termed Deep Prior Diffraction Tomography (DP-DT), to reconstruct the 3D refractive index (RI) of thick biological samples at high resolution from a sequence of low-resolution images collected under angularly varying illumination. DP-DT processes the multi-angle data using a phase retrieval algorithm that is extended by a deep image prior (DIP), which reparameterizes the 3D sample reconstruction with an untrained, deep generative 3D convolutional neural network (CNN). We show that DP-DT effectively addresses the missing cone problem, which otherwise degrades the resolution and quality of standard 3D reconstruction algorithms. As DP-DT does not require pre-captured data or pre-training, it is not biased towards any particular dataset. Hence, it is a general technique that can be applied to a wide variety of 3D samples, including scenarios in which large datasets for supervised training would be infeasible or expensive. We applied DP-DT to obtain 3D RI maps of bead phantoms and complex biological specimens, both in simulation and experiment, and show that DP-DT produces higher-quality results than standard regularization techniques. We further demonstrate the generality of DP-DT, using two different scattering models, the first Born and multi-slice models. Our results point to the potential benefits of DP-DT for other 3D imaging modalities, including X-ray computed tomography, magnetic resonance imaging, and electron microscopy.

Traditional imaging systems exhibit a well-known trade-off between the resolution and the field of view of their captured images. Typical cameras and microscopes can either “zoom in” and image at high-resolution, or they can “zoom out” to see a larger area at lower resolution, but can rarely achieve both effects simultaneously. In this review, we present details about a relatively new procedure termed Fourier ptychography (FP), which addresses the above trade-off to produce gigapixel-scale images without requiring any moving parts. To accomplish this, FP captures multiple low-resolution, large field-of-view images and computationally combines them in the Fourier domain into a high-resolution, large field-of-view result. Here, we present details about the various implementations of FP and highlight its demonstrated advantages to date, such as aberration recovery, phase imaging, and 3D tomographic reconstruction, to name a few. After providing some basics about FP, we list important details for successful experimental implementation, discuss its relationship with other computational imaging techniques, and point to the latest advances in the field while highlighting persisting challenges.

We present a preliminary k-space-based analysis of OCT and our recently proposed extension, optical coherence refraction tomography (OCRT). Our theory provides insight into how angular compounding enhances resolution and reduces speckle in coherence imaging techniques.

Illuminating or imaging samples from a broad angular range is essential in a wide variety of computational 3D imaging and resolution-enhancement techniques, such as optical projection tomography, optical diffraction tomography, synthetic aperture microscopy, Fourier ptychographic microscopy, structured illumination microscopy, photogrammetry, and optical coherence refraction tomography. The wider the angular coverage, the better the resolution enhancement or 3D-resolving capabilities. However, achieving such angular ranges is a practical challenge, especially when approaching ± 90 ∘ or beyond. Often, researchers resort to expensive, proprietary high numerical aperture (NA) objectives or to rotating the sample or source-detector pair, which sacrifices temporal resolution or perturbs the sample. Here, we propose several new strategies for multiangle imaging approaching 4pi steradians using concave parabolic or ellipsoidal mirrors and fast, low rotational inertia scanners, such as galvanometers. We derive theoretically and empirically relations between a variety of system parameters (e.g., NA, wavelength, focal length, telecentricity) and achievable fields of view (FOVs) and importantly show that intrinsic tilt aberrations do not restrict FOV for many multiview imaging applications, contrary to conventional wisdom. Finally, we present strategies for avoiding spherical aberrations at obliquely illuminated flat boundaries. Our simple designs allow for high-speed multiangle imaging for microscopic, mesoscopic, and macroscopic applications.

Combining principles of computed tomography with modern machine-learning tools significantly improves OCT’s resolution while extending imaging depth, reducing noise and reconstructing refractive-index maps of biological samples.

There is potential clinical significance in identifying cellular responses in the anterior chamber (AC) of the eye, which can indicate hyphema (an accumulation of red blood cells [RBCs]) or aberrant intraocular inflammation (an accumulation of white blood cells [WBCs]). In this work, we developed a spectroscopic OCT analysis method to differentiate between populations of RBCs and subtypes of WBCs, including granulocytes, lymphocytes and monocytes, both in vitro and in ACs of porcine eyes. We developed an algorithm to track single cells within OCT data sets, and extracted the backscatter reflectance spectrum of each single cell from the detected interferograms using the short-time Fourier transform (STFT). A look-up table of Mie back-scattering spectra was generated and used to correlate the backscatter spectral features of single cells to their characteristic sizes. The extracted size distributions based on the best Mie spectra fit were significantly different between each cell type. We also studied theoretical backscattering models of single RBCs to further validate our experimental results. The described work is a promising step towards clinically differentiating and quantifying AC blood cell types.

Since its invention, the microscope has been optimized for interpretation by a human observer. With the recent development of deep learning algorithms for automated image analysis, there is now a clear need to re-design the microscope's hardware for specific interpretation tasks. To increase the speed and accuracy of automated image classification, this work presents a method to co-optimize how a sample is illuminated in a microscope, along with a pipeline to automatically classify the resulting image, using a deep neural network. By adding a ""physical layer"" to a deep classification network, we are able to jointly optimize for specific illumination patterns that highlight the most important sample features for the particular learning task at hand, which may not be obvious under standard illumination. We demonstrate how our learned sensing approach for illumination design can automatically identify malaria-infected cells with up to 5-10% greater accuracy than standard and alternative microscope lighting designs. We show that this joint hardware-software design procedure generalizes to offer accurate diagnoses for two different blood smear types, and experimentally show how our new procedure can translate across different experimental setups while maintaining high accuracy.

In optical coherence tomography (OCT), the axial resolution is often superior to the lateral resolution, which is sacrificed for long imaging depths. To address this anisotropy, we previously developed optical coherence refraction tomography (OCRT), which uses images from multiple angles to computationally reconstruct an image with isotropic resolution, given by the OCT axial resolution. On the other hand, spectroscopic OCT (SOCT), an extension of OCT, trades axial resolution for spectral resolution and hence often has superior lateral resolution. Here, we present spectroscopic OCRT (SOCRT), which uses SOCT images from multiple angles to reconstruct a spectroscopic image with isotropic spatial resolution limited by the OCT lateral resolution. We experimentally show that SOCRT can estimate bead size based on Mie theory at simultaneously high spectral and isotropic spatial resolution. We also applied SOCRT to a biological sample, achieving axial resolution enhancement limited by the lateral resolution.

More people die from melanoma after a stage I diagnosis than after a stage IV diagnosis, because the tools available to clinicians do not readily identify which early-stage cancers will be aggressive. Near-infrared pump-probe microscopy detects fundamental differences in melanin structure between benign human moles and melanoma and also correlates with metastatic potential. However, the biological mechanisms of these changes have been difficult to quantify, as many different mechanisms can contribute to the pump-probe signal. We use model systems (sepia, squid, and synthetic eumelanin), cellular uptake studies, and a range of pump and probe wavelengths to demonstrate that the clinically observed effects come from alterations of the aggregated mode from “thick oligomer stacks” to “thin oligomer stacks” (due to changes in monomer composition) and (predominantly) deaggregation of the assembled melanin structure. This provides the opportunity to use pump-probe microscopy for the detection and study of melanin-associated diseases.

We integrate spectroscopic optical coherence tomography (SOCT) with stimulated Raman scattering (SRS) to enable simultaneously multiplexed spatial and spectral imaging with sensitivity to many endogenous biochemical species that play an important role in biology and medicine. The combined approach, termed SRS-SOCT, overcomes the limitations of each individual method. Ultimately, SRS-SOCT has the potential to achieve fast, volumetric, and highly sensitive label-free molecular imaging. We demonstrate the approach by imaging excised human adipose tissue and detecting the lipids' Raman signatures in the high-wavenumber region.

Optical coherence tomography (OCT) enables non-invasive, high-resolution, tomographic imaging of biological tissues by leveraging principles of low coherence interferometry; however, OCT lacks molecular specificity. Spectroscopic OCT (SOCT) overcomes this limitation by providing depth-resolved spectroscopic signatures of chromophores, but SOCT has been limited to a couple of endogenous molecules, namely hemoglobin and melanin. Stimulated Raman scattering, on the other hand, can provide highly specific molecular information of many endogenous species, but lacks the spatial and spectral multiplexing capabilities of SOCT. In this work we integrate the two methods, SRS and SOCT, to enable simultaneously multiplexed spatial and spectral imaging with sensitivity to many endogenous biochemical species that play an important role in biology and medicine. The method, termed SRS-SOCT, has the potential to achieve fast, volumetric, and highly sensitive label-free molecular imaging, which would be valuable for many applications. We demonstrate the approach by imaging excised human adipose tissue and detecting the lipids’ Raman signatures in the high-wavenumber region. Details of this method along with validations and results will be presented.

Recent advances in three-dimensional (3D) tissue engineering have concomitantly generated a need for new methods to visualize and assess the tissue. In particular, methods for imaging intact volumes of whole tissue, rather than a single plane, are required. Herein, we describe the use of multiphoton microscopy, combined with optical clearing, to noninvasively probe decellularized lung extracellular matrix scaffolds and decellularized, tissue-engineered blood vessels. We also evaluate recellularized lung tissue scaffolds. In addition to nondestructive imaging of tissue volumes greater than 4 mm(3), the lung tissue can be visualized using three distinct signals, combined or singly, that allow for simple separation of cells and different components of the extracellular matrix. Because the 3D volumes are not reconstructions, they do not require registration algorithms to generate digital volumes, and maintenance of isotropic resolution is not required when acquiring stacks of images. Once a virtual volume of tissue is generated, structures that have innate 3D features, such as the lumens of vessels and airways, are easily animated and explored in all dimensions. In blood vessels, individual collagen fibers can be visualized at the micron scale and their alignment assessed at various depths through the tissue, potentially providing some nondestructive measure of vessel integrity and mechanics. Finally, both the lungs and vessels assayed here were optically cleared, imaged, and visualized in a matter of hours, such that the added benefits of these techniques can be achieved with little more hassle or processing time than that associated with traditional histological methods.

One critical barrier to the robust study of cilia-driven fluid flow in developmental biology is a lack of methods for acquiring three-dimensional (3D) images of three vector component (3C) measurements of flow velocities. A 3D3C map of cilia-driven fluid flow quantifies the flow speed along three axes (e.g. three Cartesian vector components v_x, v_y, v_z) at each point in 3D space. 3D3C quantification is important because cilia-driven fluid flow is not amenable to simplifying assumptions (e.g. parabolic flow profile. Such quantification may enable systematically detailed characterization of performance using shear force and power dissipation metrics derived from 3D3C flow velocity fields. We report our OCT-based results in developing methods for the 3D3C quantification of cilia-driven flow fields. First, we used custom scan protocols and reconstruction algorithms to synthesize 3D3C flow velocity fields from 2D2C fields generated using correlation-based methods (directional dynamic light scattering and digital particle image velocimetry). Xenopus results include flow driven by ciliated embryo skin and flow driven by ciliated ependymal cells in developing brain ventricles. Second, we developed a new approach to particle tracking velocimetry that generates 2D2.5C (2.5C: v_x,|v_y|,v_z) velocity fields from single-plane 2D image acquisitions. We demonstrated this particle streak velocimetry method in calibrated flow phantoms and in flow driven by ciliated Xenopus embryo skin. Additionally, we have preliminary results extending particle streak velocimetry to 3D3C in calibrated flow phantoms with ongoing work in Xenopus embryos.

Tissue-engineered blood vessels (TEVs) are typically produced using the pulsatile, uniaxial circumferential stretch to mechanically condition and strengthen the arterial grafts. Despite improvements in the mechanical integrity of TEVs after uniaxial conditioning, these tissues fail to achieve critical properties of native arteries such as matrix content, collagen fiber orientation, and mechanical strength. As a result, uniaxially loaded TEVs can result in mechanical failure, thrombus, or stenosis on implantation. In planar tissue equivalents such as artificial skin, biaxial loading has been shown to improve matrix production and mechanical properties. To date however, multiaxial loading has not been examined as a means to improve mechanical and biochemical properties of TEVs during culture. Therefore, we developed a novel bioreactor that utilizes both circumferential and axial stretch that more closely simulates loading conditions in native arteries, and we examined the suture strength, matrix production, fiber orientation, and cell proliferation. After 3 months of biaxial loading, TEVs developed a formation of mature elastic fibers that consisted of elastin cores and microfibril sheaths. Furthermore, the distinctive features of collagen undulation and crimp in the biaxial TEVs were absent in both uniaxial and static TEVs. Relative to the uniaxially loaded TEVs, tissues that underwent biaxial loading remodeled and realigned collagen fibers toward a more physiologic, native-like organization. The biaxial TEVs also showed increased mechanical strength (suture retention load of 303 ± 14.53 g, with a wall thickness of 0.76 ± 0.028 mm) and increased compliance. The increase in compliance was due to combinatorial effects of mature elastic fibers, undulated collagen fibers, and collagen matrix orientation. In conclusion, biaxial stretching is a potential means to regenerate TEVs with improved matrix production, collagen organization, and mechanical properties.

We present a new method for 2.5 and 3 vector component velocimetry. We call this method particle streak velocimetry OCT (PSV-OCT). PSV-OCT generates two-dimensional, 2.5 vector component (v_x,|v_y|,v_z) cross-sectional maps of microscale flow velocity (e.g. biological cilia-driven fluid flow). The enabling insight is that a tracer particle in sparsely-seeded fluid flow traces out streaks in (x,z,t)-space. The streak orientations in x-t and z-t yield v_x and v_z, respectively. The in-plane (x-z plane) residence time yields the out-of-plane speed |v_y|. Vector component values are generated by fitting streaks to a model of image formation. We demonstrate cross-sectional estimation of (v_x,|v_y|,v_z) in two important animal models in ciliary biology: Xenopus embryos (tadpoles) and mouse trachea. Further, by incorporation the assumption of incompressible flow into the estimation process, we are able to generate 3 vector component (v_x,v_y,v_z) estimates in three spatial dimensions from 2.5 vector component measurements taken in parallel OCT planes in 3D space.