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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.

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.

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.

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.

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.

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.

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.

We present a new OCT method for flow speed quantification and directional velocimetry: particle streak velocimetry-OCT (PSV-OCT). PSV-OCT generates two-dimensional, 2.5-vector component (vx ,|vy |,vz ) maps of microscale flow velocity fields. Knowledge of 2.5-vector components also enables the estimation of total flow speed. The enabling insight behind PSV-OCT is that tracer particles in sparsely-seeded fluid flow trace out streaks in (x,z,t)-space. The streak orientations in x-t and z-t yield vx and vz , respectively. The in-plane (x-z plane) residence time yields the out-of-plane speed |vy |. Vector component values are generated by fitting streaks to a model of image formation that incorporates equations of motion in 3D space. We demonstrate cross-sectional estimation of (vx ,|vy |,vz ) in two important animal models in ciliary biology: Xenopus embryos (tadpoles) and mouse trachea.

OCT is a popular cross-sectional microscale imaging modality in medicine and biology. While structural imaging using OCT is a mature technology in many respects, flow and motion estimation using OCT remains an intense area of research. In particular, there is keen interest in maximizing information extraction from the complex-valued OCT signal. Here, we introduce a Bayesian framework into the data workflow in OCT-based velocimetry. We demonstrate that using prior information in this Bayesian framework can significantly improve velocity estimate precision in a correlation-based, model-based framework for Doppler and transverse velocimetry. We show results in calibrated flow phantoms as well as in vivo in a Drosophila melanogaster (fruit fly) heart. Thus, our work improves upon the current approaches in terms of improved information extraction from the complex-valued OCT signal.