Complex differential variance angiography with noise-bias correction for optical coherence tomography of the retina

Imaging of the vascular distribution of the outer ear using optical coherence tomography angiography for highly accurate positioning of a hearable sensor

Novel hearable technology is securely and comfortably positioned within the ear canal minimizing inaccuracies caused by accessory movements during activities. Despite extensive research on hearable technologies within the outer ear, there is a lack of research in the field of vascular imaging and quantitative analysis in the outer ear in vivo, which is one of the crucial factors to select the appropriate sensor position. Therefore, in this paper, we introduced optical coherence tomography angiography (OCTA)-based qualitative and quantitative analyses to visualize the inner vasculature of the outer ear to acquire vascular maps for microvascular assessments in vivo. By generating maximum amplitude projection images from three-dimensional blood vascular volume, we identified variations of blood vessel signal caused by the different biological characteristics and curvature of the ear among individuals. The performance of micro-vascular mapping using the proposed method was validated through the comparison and analysis of individual vascular parameters using extracted 20 vascular-related variables. In addition, we extracted pulsatile blood flow signals, demonstrating its potential to provide photoplethysmographic signals and ear blood maps simultaneously. Therefore, our proposed OCTA-based method for ear vascular mapping successfully provides quantitative information about ear vasculature, which is potentially used for determining the position of system-on-chip sensors for health monitoring in hearable devices.

Deep learning-based label-free imaging of lymphatics and aqueous veins in the eye using optical coherence tomography

We demonstrate an adaptation of deep learning for label-free imaging of the micro-scale lymphatic vessels and aqueous veins in the eye using optical coherence tomography (OCT). The proposed deep learning-based OCT lymphangiography (DL-OCTL) method was trained, validated and tested, using OCT scans (23 volumetric scans comprising 19,736 B-scans) from 11 fresh ex vivo porcine eyes with the corresponding vessel labels generated by a conventional OCT lymphangiography (OCTL) method based on thresholding with attenuation compensation. Compared to conventional OCTL, the DL-OCTL method demonstrates comparable results for imaging lymphatics and aqueous veins in the eye, with an Intersection over Union value of 0.79 ± 0.071 (mean ± standard deviation). In addition, DL-OCTL mitigates the imaging artifacts in conventional OCTL where the OCT signal modelling was corrupted by the tissue heterogeneity, provides ~ 10 times faster processing based on a rough comparison and does not require OCT-related knowledge for correct implementation as in conventional OCTL. With these favorable features, DL-OCTL promises to improve the practicality of OCTL for label-free imaging of lymphatics and aqueous veins for preclinical and clinical imaging applications.

Probabilistic volumetric speckle suppression in OCT using deep learning

We present a deep learning framework for volumetric speckle reduction in optical coherence tomography (OCT) based on a conditional generative adversarial network (cGAN) that leverages the volumetric nature of OCT data. In order to utilize the volumetric nature of OCT data, our network takes partial OCT volumes as input, resulting in artifact-free despeckled volumes that exhibit excellent speckle reduction and resolution preservation in all three dimensions. Furthermore, we address the ongoing challenge of generating ground truth data for supervised speckle suppression deep learning frameworks by using volumetric non-local means despeckling-TNode to generate training data. We show that, while TNode processing is computationally demanding, it serves as a convenient, accessible gold-standard source for training data; our cGAN replicates efficient suppression of speckle while preserving tissue structures with dimensions approaching the system resolution of non-local means despeckling while being two orders of magnitude faster than TNode. We demonstrate fast, effective, and high-quality despeckling of the proposed network in different tissue types acquired with three different OCT systems compared to existing deep learning methods. The open-source nature of our work facilitates re-training and deployment in any OCT system with an all-software implementation, working around the challenge of generating high-quality, speckle-free training data.

Domain-Aware Few-Shot Learning for Optical Coherence Tomography Noise Reduction

Speckle noise has long been an extensively studied problem in medical imaging. In recent years, there have been significant advances in leveraging deep learning methods for noise reduction. Nevertheless, adaptation of supervised learning models to unseen domains remains a challenging problem. Specifically, deep neural networks (DNNs) trained for computational imaging tasks are vulnerable to changes in the acquisition system's physical parameters, such as: sampling space, resolution, and contrast. Even within the same acquisition system, performance degrades across datasets of different biological tissues. In this work, we propose a few-shot supervised learning framework for optical coherence tomography (OCT) noise reduction, that offers high-speed training (of the order of seconds) and requires only a single image, or part of an image, and a corresponding speckle-suppressed ground truth, for training. Furthermore, we formulate the domain shift problem for OCT diverse imaging systems and prove that the output resolution of a despeckling trained model is determined by the source domain resolution. We also provide possible remedies. We propose different practical implementations of our approach, verify and compare their applicability, robustness, and computational efficiency. Our results demonstrate the potential to improve sample complexity, generalization, and time efficiency, for coherent and non-coherent noise reduction via supervised learning models, that can also be leveraged for other real-time computer vision applications.

Comparative study of OCTA algorithms with a high-sensitivity multi-contrast Jones matrix OCT system for human skin imaging

The multi-contrast Jones matrix OCT (JMT) system can not only improve the tissue-specific contrast but also enhance the sensitivity of detecting flow, which is gaining increasing attention. However, for the JMT system, there is currently a lack of studies evaluating and guiding the selection of suitable angiography algorithms to map the most appealing quality of angiograms for clinical use. In this paper, by a homemade high-sensitivity multi-contrast JMT system based 200 kHz swept source, the performance of two complex-signal-based OCTA methods that are insensitive to phase instability and one amplitude-signal-based OCTA method are compared for in-vivo imaging of human skin qualitatively and quantitatively. Six metrics, including vascular connectivity, image contrast-to-noise ratio, image signal-to-noise ratio, vessel diameter index, blood vessel density, and processing time, are assessed. The results show that the vascular networks processed by all OCTA methods and the texture of skin could be visualized simultaneously and markedly. Additionally, the complex-signal-based OCTA methods successfully suppress phase instabilities and even outperform the amplitude-signal-based OCTA algorithm in some indicators. This paper has a certain guiding significance for selecting an appropriate angiography algorithm and expanding the application field with this system.

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

The field of ophthalmic imaging has grown substantially over the last years. Massive improvements in image processing and computer hardware have allowed the emergence of multiple imaging techniques of the eye that can transform patient care. The purpose of this review is to describe the most recent advances in eye imaging and explain how new technologies and imaging methods can be utilized in a clinical setting. The introduction of optical coherence tomography (OCT) was a revolution in eye imaging and has since become the standard of care for a plethora of conditions. Its most recent iterations, OCT angiography, and visible light OCT, as well as imaging modalities, such as fluorescent lifetime imaging ophthalmoscopy, would allow a more thorough evaluation of patients and provide additional information on disease processes. Toward that goal, the application of adaptive optics (AO) and full-field scanning to a variety of eye imaging techniques has further allowed the histologic study of single cells in the retina and anterior segment. Toward the goal of remote eye care and more accessible eye imaging, methods such as handheld OCT devices and imaging through smartphones, have emerged. Finally, incorporating artificial intelligence (AI) in eye images has the potential to become a new milestone for eye imaging while also contributing in social aspects of eye care.

Using the dynamic forward scattering signal for optical coherence tomography based blood flow quantification

To our knowledge, all existing optical coherence tomography approaches for quantifying blood flow, whether Doppler-based or decorrelation-based, analyze light that is back-scattered by moving red blood cells (RBCs). This work investigates the potential advantages of basing these measurements on light that is forward-scattered by RBCs, i.e., by looking at the signals back-scattered from below the vessel. We show experimentally that flowmetry based on forward-scattering is insensitive to vessel orientation for vessels that are approximately orthogonal to the imaging beam. We further provide proof-of-principle demonstrations of dynamic forward-scattering (DFS) flowmetry in human retinal and choroidal vessels.

Spatial-Temporal Speckle Variance in the En-Face View as a Contrast for Optical Coherence Tomography Angiography (OCTA)

Optical Coherence Tomography (OCT) is an adaptable depth-resolved imaging modality capable of creating a non-invasive 'digital biopsy' of the eye. One of the latest advances in OCT is optical coherence tomography angiography (OCTA), which uses the speckle variance or phase change in the signal to differentiate static tissue from blood flow. Unlike fluorescein angiography (FA), OCTA is contrast free and depth resolved. By combining high-density scan patterns and image processing algorithms, both morphometric and functional data can be extracted into a depth-resolved vascular map of the retina. The algorithm that we explored takes advantage of the temporal-spatial relationship of the speckle variance to improve the contrast of the vessels in the en-face OCT with a single frame. It also does not require the computationally inefficient decorrelation of multiple A-scans to detect vasculature, as used in conventional OCTA analysis. Furthermore, the spatial temporal OCTA (ST-OCTA) methodology tested offers the potential for post hoc analysis to improve the depth-resolved contrast of specific ocular structures, such as blood vessels, with the capability of using only a single frame for efficient screening of large sample volumes, and additional enhancement by processing with choice of frame averaging methods. Applications of this method in pre-clinical studies suggest that the OCTA algorithm and spatial temporal methodology reported here can be employed to investigate microvascularization and blood flow in the retina, and possibly other compartments of the eye.

Advances in multimodal imaging in ophthalmology

Multimodality ophthalmic imaging systems aim to enhance the contrast, resolution, and functionality of existing technologies to improve disease diagnostics and therapeutic guidance. These systems include advanced acquisition and post-processing methods using optical coherence tomography (OCT), combined scanning laser ophthalmoscopy and OCT systems, adaptive optics, surgical guidance, and photoacoustic technologies. Here, we provide an overview of these ophthalmic imaging systems and their clinical and basic science applications.

High performance OCTA enabled by combining features of shape, intensity, and complex decorrelation

Motion contrast optical coherence tomography angiography (OCTA) entails a precise identification of dynamic flow signals from the static background, but an intermediate region with voxels exhibiting a mixed distribution of dynamic and static scatterers is almost inevitable in practice, which degrades the vascular contrast and connectivity. In this work, the static-dynamic intermediate region was pre-defined according to the asymptotic relation between inverse signal-to-noise ratio (iSNR) and decorrelation, which was theoretically derived for signals with different flow rates based on a multi-variate time series (MVTS) model. Then the ambiguous voxels in the intermediate region were further differentiated using a shape mask with adaptive threshold. Finally, an improved OCTA classifier was built by combining shape, iSNR, and decorrelation features, termed as SID-OCTA, and the performance of the proposed SID-OCTA was validated experimentally through mouse retinal imaging.

Polarization sensitive optical coherence tomography for imaging microvascular information within living tissue without polarization-induced artifacts

When imaging birefringent samples using optical coherence tomography angiography (OCTA), the phase retardation may appear opposite to the phase change due to the blood flow in the orthogonal signals, for which a cancellation effect can occur when deriving OCTA signals. This effect can diminish the ability of OCTA to detect vascular information, leading to an erroneous interpretation of the final OCTA images. To mitigate this issue, we demonstrate polarization-sensitive optical coherence tomography (PS-OCT) to image microvascular information within a living sample without polarization induced artifacts. The system is furnished with a swept source OCT (SS-OCT) that incorporates two imaging modes: OCTA imaging and polarization-sensitive imaging. PS-OCT is used to provide birefringent contrast where the color-encoded Stokes parameters are used to obtain high contrast polarization-state images. OCTA is used to acquire high-resolution images of functional microvascular networks permeating the scanned tissue volume. Taking the advantages of the dual-channel PS-OCT configuration, the polarization induced artifacts are eliminated from OCTA vascular imaging. The proposed PS-OCTA system is employed to visualize the birefringent components and the vascular networks of the human skin in vivo. It is expected that the proposed system setup would have useful and practical applications in the investigations of the vasculature in the birefringent tissue samples both pre-clinically and clinically.

A recommended "minimum data set" framework for SD-OCT retinal image acquisition and analysis from the Atlas of Retinal Imaging in Alzheimer's Study (ARIAS)

INTRODUCTION

We propose a minimum data set framework for the acquisition and analysis of retinal images for the development of retinal Alzheimer's disease (AD) biomarkers. Our goal is to describe methodology that will increase concordance across laboratories, so that the broader research community is able to cross-validate findings in parallel, accumulate large databases with normative data across the cognitive aging spectrum, and progress the application of this technology from the discovery stage to the validation stage in the search for sensitive and specific retinal biomarkers in AD.

METHODS

The proposed minimum data set framework is based on the Atlas of Retinal Imaging Study (ARIAS), an ongoing, longitudinal, multi-site observational cohort study. However, the ARIAS protocol has been edited and refined with the expertise of all co-authors, representing 16 institutions, and research groups from three countries, as a first step to address a pressing need identified by experts in neuroscience, neurology, optometry, and ophthalmology at the Retinal Imaging in Alzheimer's Disease (RIAD) conference, convened by the Alzheimer's Association and held in Washington, DC, in May 2019.

RESULTS

Our framework delineates specific imaging protocols and methods of analysis for imaging structural changes in retinal neuronal layers, with optional add-on procedures of fundus autofluorescence to examine beta-amyloid accumulation and optical coherence tomography angiography to examine AD-related changes in the retinal vasculature.

DISCUSSION

This minimum data set represents a first step toward the standardization of retinal imaging data acquisition and analysis in cognitive aging and AD. A standardized approach is essential to move from discovery to validation, and to examine which retinal AD biomarkers may be more sensitive and specific for the different stages of the disease severity spectrum. This approach has worked for other biomarkers in the AD field, such as magnetic resonance imaging; amyloid positron emission tomography; and, more recently, blood proteomics. Potential context of use for retinal AD biomarkers is discussed.

Jones matrix-based speckle-decorrelation angiography using polarization-sensitive optical coherence tomography

We show that polarization-sensitive optical coherence tomography angiography (PS-OCTA) based on full Jones matrix assessment of speckle decorrelation offers improved contrast and depth of vessel imaging over conventional OCTA. We determine how best to combine the individual Jones matrix elements and compare the resulting image quality to that of a conventional OCT scanner by co-locating and imaging the same skin locations with closely matched scanning setups. Vessel projection images from finger and forearm skin demonstrate the benefits of Jones matrix-based PS-OCTA. Our study provides a promising starting point and a useful reference for future pre-clinical and clinical applications of Jones matrix-based PS-OCTA.

Approaches to quantify optical coherence tomography angiography metrics

Optical coherence tomography (OCT) has revolutionized the field of ophthalmology in the last three decades. As an OCT extension, OCT angiography (OCTA) utilizes a fast OCT system to detect motion contrast in ocular tissue and provides a three-dimensional representation of the ocular vasculature in a non-invasive, dye-free manner. The first OCT machine equipped with OCTA function was approved by U.S. Food and Drug Administration in 2016 and now it is widely applied in clinics. To date, numerous methods have been developed to aid OCTA interpretation and quantification. In this review, we focused on the workflow of OCTA-based interpretation, beginning from the generation of the OCTA images using signal decorrelation, which we divided into intensity-based, phase-based and phasor-based methods. We further discussed methods used to address image artifacts that are commonly observed in clinical settings, to the algorithms for image enhancement, binarization, and OCTA metrics extraction. We believe a better grasp of these technical aspects of OCTA will enhance the understanding of the technology and its potential application in disease diagnosis and management. Moreover, future studies will also explore the use of ocular OCTA as a window to link ocular vasculature to the function of other organs such as the kidney and brain.

A Neural Network Approach to Quantify Blood Flow from Retinal OCT Intensity Time-Series Measurements

Many diseases of the eye are associated with alterations in the retinal vasculature that are possibly preceded by undetected changes in blood flow. In this work, a robust blood flow quantification framework is presented based on optical coherence tomography (OCT) angiography imaging and deep learning. The analysis used a forward signal model to simulate OCT blood flow data for training of a neural network (NN). The NN was combined with pre- and post-processing steps to create an analysis framework for measuring flow rates from individual blood vessels. The framework’s accuracy was validated using both blood flow phantoms and human subject imaging, and across flow speed, vessel angle, hematocrit levels, and signal-to-noise ratio. The reported flow rate of the calibrated NN framework was measured to be largely independent of vessel angle, hematocrit levels, and measurement signal-to-noise ratio. In vivo retinal flow rate measurements were self-consistent across vascular branch points, and approximately followed a predicted power-law dependence on the vessel diameter. The presented OCT-based NN flow rate estimation framework addresses the need for a robust, deployable, and label-free quantitative retinal blood flow mapping technique.

Suite of methods for assessing inner retinal temporal dynamics across spatial and temporal scales in the living human eye

Significance: There are no label-free imaging descriptors related to physiological activity of inner retinal cells in the living human eye. A major reason is that inner retinal neurons are highly transparent and reflect little light, making them extremely difficult to visualize and quantify. Aim: To measure physiologically-induced optical changes of inner retinal cells despite their challenging optical properties. Approach: We developed an imaging method based on adaptive optics and optical coherence tomography (AO-OCT) and a suite of postprocessing algorithms, most notably a new temporal correlation method. Results: We captured the temporal dynamics of entire inner retinal layers, of specific tissue types, and of individual cells across three different timescales from fast (seconds) to extremely slow (one year). Time correlation analysis revealed significant differences in time constant (up to 0.4 s) between the principal layers of the inner retina with the ganglion cell layer (GCL) being the most dynamic. At the cellular level, significant differences were found between individual GCL somas. The mean time constant of the GCL somas ( 0.69 ± 0.17    s ) was ∼ 30 % smaller than that of nerve fiber bundles and inner plexiform layer synapses and processes. Across longer durations, temporal speckle contrast and time-lapse imaging revealed motion of macrophage-like cells (over minutes) and GCL neuron loss and remodeling (over one year). Conclusions: Physiological activity of inner retinal cells is now measurable in the living human eye.

Signal averaging improves signal-to-noise in OCT images: But which approach works best, and when?

The high acquisition speed of state-of-the-art optical coherence tomography (OCT) enables massive signal-to-noise ratio (SNR) improvements by signal averaging. Here, we investigate the performance of two commonly used approaches for OCT signal averaging. We present the theoretical SNR performance of (a) computing the average of OCT magnitude data and (b) averaging the complex phasors, and substantiate our findings with simulations and experimentally acquired OCT data. We show that the achieved SNR performance strongly depends on both the SNR of the input signals and the number of averaged signals when the signal bias caused by the noise floor is not accounted for. Therefore we also explore the SNR for the two averaging approaches after correcting for the noise bias and, provided that the phases of the phasors are accurately aligned prior to averaging, then find that complex phasor averaging always leads to higher SNR than magnitude averaging.

SNR-Adaptive OCT Angiography Enabled by Statistical Characterization of Intensity and Decorrelation With Multi-Variate Time Series Model

In OCT angiography (OCTA), decorrelation computation has been widely used as a local motion index to identify dynamic flow from static tissues, but its dependence on SNR severely degrades the vascular visibility, particularly in low-SNR regions. To mathematically characterize the decorrelation-SNR dependence of OCT signals, we developed a multi-variate time series (MVTS) model. Based on the model, we derived a universal asymptotic linear relation of decorrelation to inverse SNR (iSNR), with the variance in static and noise regions determined by the average kernel size. Accordingly, with the population distribution of static and noise voxels being explicitly calculated in the iSNR and decorrelation (ID) space, a linear classifier is developed by removing static and noise voxels at all SNR, to generate a SNR-adaptive OCTA, termed as ID-OCTA. Then, flow phantom and human skin experiments were performed to validate the proposed ID-OCTA. Both qualitative and quantitative assessments demonstrated that the ID-OCTA offers a superior visibility of blood vessels, particularly in the deep layer. Finally, the implications of this work on both system design and hemodynamic quantification are further discussed.

Optical coherence tomography angiography in preclinical neuroimaging

Preclinical neuroimaging allows for the assessment of brain anatomy, connectivity, and function in laboratory animals, such as mice and this imaging field has been a rapidly growing aimed at bridging the translation gap between animal and human research. The progress in the animal research could be accelerated by high-resolution in vivo optical imaging technologies. Optical coherence tomography-based angiography (OCTA) estimates the scattering from moving red blood cells, providing the visualization of functional micro-vessel networks within tissue beds in vivo without a need for exogenous contrast agents. Recent advancement of OCTA methods have expanded its application to neuroimaging of small animal models of brain disorders. In this paper, we overview the recent development of OCTA techniques for blood flow imaging and its preclinical applications in neuroimaging. In specific, a summary of preclinical OCTA studies for traumatic brain injury, cerebral stroke, and aging brain on mice is reviewed.

Noninvasive Assessment of Corneal Crosslinking With Phase-Decorrelation Optical Coherence Tomography

Purpose

There is strong evidence that abnormalities in corneal biomechanical play a causal role in corneal ectasias, such as keratoconus. Additionally, corneal crosslinking (CXL) treatment, which halts progression of keratoconus, directly appeals to corneal biomechanics. However, existing methods of corneal biomechanical assessment have various drawbacks: dependence on IOP, long acquisition times, or limited resolution. Here, we present a method that may avoid these limitations by using optical coherence tomography (OCT) to detect the endogenous random motion within the cornea, which can be associated with stromal crosslinking.

Methods

Phase-decorrelation OCT (PhD-OCT), based in the theory of dynamic light scattering, is a method to spatially resolve endogenous random motion by calculating the decorrelation rate, Γ, of the temporally evolving complex-valued OCT signal. PhD-OCT images of ex vivo porcine globes were recorded during CXL and control protocols. In addition, human patients were imaged with PhD-OCT using a clinical OCT system.

Results

In both the porcine cornea and the human cornea, crosslinking results in a reduction of Γ (P < 0.0001), indicating more crosslinks. This effect was repeatable in ex vivo porcine corneas (change in average Γ = −41.55 ± 9.64%, n = 5) and not seen after sham treatments (change in average Γ = 2.83 ± 12.56%, n = 5). No dependence of PhD-OCT on IOP was found, and correctable effects were caused by variations in signal-to-noise ratio, hydration, and motion.

Conclusions

PhD-OCT may be a useful and readily translatable tool for investigating biomechanical properties of the cornea and for enhancing the diagnosis and treatment of patients.

Quantitative depolarization measurements for fiber-based polarization-sensitive optical frequency domain imaging of the retinal pigment epithelium

A full quantitative evaluation of the depolarization of light may serve to assess concentrations of depolarizing particles in the retinal pigment epithelium and to investigate their role in retinal diseases in the human eye. Optical coherence tomography and optical frequency domain imaging use spatial incoherent averaging to compute depolarization. Depolarization depends on accurate measurements of the polarization states at the receiver but also on the polarization state incident upon and within the tissue. Neglecting this dependence can result in artifacts and renders depolarization measurements vulnerable to birefringence in the system and in the sample. In this work, we discuss the challenges associated with using a single input polarization state and traditional depolarization metrics such as the degree-of-polarization and depolarization power. We demonstrate quantitative depolarization measurements based on Jones vector synthesis and polar decomposition using fiber-based polarization-sensitive optical frequency domain imaging of the retinal pigment epithelium in a human eye.

Demonstration of Triband Multi-Focal Imaging with Optical Coherence Tomography

We demonstrate an extended depth of focus optical coherence tomography (OCT) system based on the use of chromatic aberration to create displaced focal planes in the sample. The system uses a wavelength-swept source tuning over three spectral bands and three separate interferometers, each of which interfaces to a single illumination/collection fiber. The resulting three imaged volumes are merged in post-processing to generate an image with a larger depth of focus than is obtained from each band individually. The improvements are demonstrated in structural imaging of a porous phantom and a lipid-cleared murine brain, and by angiographic imaging of human skin. By using a coaxial approach with Gaussian beams, this approach enables an extended focus with relatively simple microscope optics and data-merging algorithms.

Wide-Field Functional Microscopy of Peripheral Nerve Injury and Regeneration

Severe peripheral nerve injuries often result in partial repair and lifelong disabilities in patients. New surgical techniques and better graft tissues are being studied to accelerate regeneration and improve functional recovery. Currently, limited tools are available to provide in vivo monitoring of changes in nerve physiology such as myelination and vascularization, and this has impeded the development of new therapeutic options. We have developed a wide-field and label-free functional microscopy platform based on angiographic and vectorial birefringence methods in optical coherence tomography (OCT). By incorporating the directionality of the birefringence, which was neglected in the previously reported polarization-sensitive OCT techniques for nerve imaging, vectorial birefringence contrast reveals internal nerve microanatomy and allows for quantification of local myelination with superior sensitivity. Advanced OCT angiography is applied in parallel to image the three-dimensional vascular networks within the nerve over wide-fields. Furthermore, by combining vectorial birefringence and angiography, intraneural vessels can be discriminated from those of the surrounding tissues. The technique is used to provide longitudinal imaging of myelination and revascularization in the rodent sciatic nerve model, i.e. imaged at certain sequential time-points during regeneration. The animals were exposed to either crush or transection injuries, and in the case of transection, were repaired using an autologous nerve graft or acellular nerve allograft. Such label-free functional imaging by the platform can provide new insights into the mechanisms that limit regeneration and functional recovery, and may ultimately provide intraoperative assessment in human subjects.