This PDF file contains the front matter associated with SPIE Proceedings Volume 11630, including the Title Page, Copyright information, and Table of Contents.

Ophthalmic OCT: New Applications in its 4th decade

This paper describes method and procedures to perform functional OCTA imaging of cerebral blood flow and hemodynamic in fully awake, behaving mice.

The electrical activity of neurons is invariably accompanied by a flux of ions and the motion of the cell membrane. This leads to subtle changes in the refractive index and birefringence of the sample. We present Superfast Polarization-sensitive Off-axis Full-field Optical Coherence Microscopy (SPoOF OCM), a novel setup to capture these variations. Using two orthogonal spatial modulations for each polarization state and a high-speed camera that can operate at up to 4000 Hz, we demonstrate the ability of our setup to observe label-free electrical activity in both the short (1-10 ms) and long (50-500 ms) term.

Optical coherence microscopy (OCM) captures the optical field with high sensitivity and thus enables computational wavefront retrieval. Compared to a real-time wavefront sensor, wavefront measurement with OCM in post-processing uses coherence and confocal gate to obtain signals with less spatial crosstalk. Also, since this approach only requires a single shot, it is more robust to in vivo sample motion than sensorless adaptive optics. By computational aberration sensing and hardware correction, we demonstrated a physical compensation of the intentionally induced aberration at the white matter layer (~900 μm) in an ex vivo mouse brain.

Recent OCT based investigations in humans and in experimental animals have shown that rods and cones outer segments elongate in response to light stimuli. In this manuscript we describe our efforts to implement phase-based “optoretinograms” (ORG) analysis framework to retinal images acquired with standard raster scanning FD-OCT system, that offers much lower phase stability compared to full field or line field OCT acquisition schemes. Our initial results, acquired in anesthetized mice in vivo, showcase successful extraction of phase-based ORG signal and its favorable comparison with intensity-based ORG signal extracted from the same data sets.

Interferometric Near-Infrared Spectroscopy (iNIRS) is a time-of-flight- (TOF-) resolved method to measure diffuse optical field dynamics from the human brain. Here we demonstrate a non-contact, null source-collector separation iNIRS approach based on polarization splitting, which enables galvanometer-based scanning across large spatial fields-of-view and suppresses single and few-scattered superficial light that degrades the effective dynamic range for deep measurements. We present, for the first time, multi-dimensional TOF- and laterally-resolved data sets that describe human forehead dynamics. The resulting blood flow index images show significant spatial heterogeneity in superficial dynamics, helping to identify optimal regions for subsequent monitoring with improved brain specificity.

Prenatal substance abuse is a leading cause of congenital defects. The severity of the defect caused depends on the substance used and the period of gestation during which the abuse happened. Several studies have documented the effects of alcohol and nicotine, used individually. However, the co-abuse of alcohol and nicotine is very common. There is not much literature on this. While first trimester substance abuse is common, several women continue to abuse substances well into their second trimester of pregnancy. The second trimester marks the peak period for fetal neurogenesis and angiogenesis. Exposure to any teratogen during this period is known to affect fetal brain development. This study uses optical coherence angiography to evaluate changes in fetal brain vasculature due to prenatal exposure to a combination of alcohol and nicotine. These results were compared to results from two other groups that were administered alcohol and nicotine independently. These comparisons showed a statistically significant difference between the nicotine group and the group that was administered both alcohol and nicotine, which was not seen with the alcohol group.

A novel non-iterative digital adaptive optics (DAO) technique is presented in which the wavefront error is calculated using the phase difference between the pupil field and its digital copies translated by a pixel along the horizontal and vertical direction of the defined co-ordinate of the pupil plane. Using this method, a large wavefront error with peak to valley of 20 radians and RMS error of 0.71 waves is detected and corrected in case of a micro-beads phantom sample. Also, human photoreceptor image is recovered from an aberrated retinal OCT acquired at the eccentricity of 2 degrees from fovea in-vivo with a flying spot OCT system using a phase stable akinetic swept source at 1060 nm.

Allying high-resolution with a large field-of-view (FOV) is of great importance in the fields of biology and medicine, but particularly challenging when imaging non-flat living samples such as the retina. Indeed, high-resolution is normally achieved with adaptive optics (AO) and scanning methods, which considerably reduce the useful FOV and increase the system complexity. Here, we introduce coherence gate shaping for FF-OCT, to optically shape the coherence gate geometry to match the retinal curvature, thus achieving a larger FOV than previously possible. Using this instrument, we obtained high-resolution images of photoreceptors close to the foveal center without AO and with a 1x1m² FOV. This novel advance facilitates the extraction of photoreceptor-based biomarkers and 4D monitoring of individual photoreceptors. We compare our findings with AO-assisted ophthalmoscopes, highlighting the potential of FF-OCT, as a compact system, to become a routine clinical imaging technique.

Computational adaptive optics (CAO) is emerging as an attractive alternative to hardware-based solutions for diffraction-limited optical coherence tomography, e.g., of the human retina. Still, to become a reliable and robust solution, many challenges need to be solved. Here, we present CAO based on multiple randomized sub-apertures in combination with suitable filtering to remove disturbing artifacts. We show that this approach can reliably detect aberrations, and we compare results to other algorithms, such as optimization of imaging quality. We also demonstrate that the filtering of reflecting image structures is essential for a robust determination of aberrations.

We present computational refocusing in polarization-sensitive optical coherence tomography (PS-OCT) to improve spatial resolution in the calculated polarimetric parameters and extending the depth-of-field in PS-OCT. To achieve this, we successfully integrated SHARP, a computational aberration correction method compatible with phase unstable systems, into a PS-OCT system with inter--A-line polarization modulation. Together with the spectral binning technique to mitigate chromatic polarization effects in system components, we show image quality enhancement in tissue polarimetry of swine eye anterior segment ex vivo, demonstrating the potential of computational refocusing in PS-OCT.

In this work we present a retinal imaging system that incorporates full-field (FF) swept-source (SS) optical coherence tomography (OCT) with hardware adaptive optics (AO) correction operating in real time. We demonstrate that this configuration permits resolution of foveal cones, with volume rates adequate to measure light-evoked changes in photoreceptors. With the OCT reference arm blocked, the system is a high speed (kHz) AO flood illumination (FI) camera, supporting future studies of optoretinographic responses through common path interference. The characterization and demonstration of the system performance with in vivo human photoreceptor mosaic are presented.

Eye movements are commonly seen as an obstacle to high-resolution ophthalmic imaging. In this context we study the natural axial movements of in vivo human eye and show that they can be used to modulate the optical phase and retrieve tomographic images via time-domain full-field optical coherence tomography (TD-FF-OCT). This approach opens a path to a simplified ophthalmic TD-FF-OCT device, operating without the usual piezo motor-camera synchronization. The device demonstrates in vivo human corneal images under different image retrieval schemes (2-phase and 4-phase) and different exposure times (3.5 ms, 10 ms, 20 ms).

Optical coherence tomography (OCT) has revolutionized diagnostics in ophthalmology. Traditionally, OCT requires an operator and patient cooperation to compensate refraction error as well as align the scanner with the subject’s eye. We have developed a robot-mounted OCT scanner that automatically focuses and aligns with the subjects’ eye by compensating motion, gaze, and refraction error. The system utilizes a combination of face and pupil tracking cameras to align while focusing through digital control of a tunable lens. We demonstrate our system by self-aligning with human eyes subject to physiological motion and gaze change as well as correcting defocus of a phantom eye.

We apply spatiotemporal optical coherence (STOC) manipulation with digital aberration correction to achieve aberration-free, crosstalk-free, high-speed, high-resolution Fourier-domain full-field optical coherence tomography (FD-FF-OCT). We demonstrate structural imaging of the human retina and cornea, and the blood flow imaging of the human retina in vivo.

Intraoperative optical coherence tomography (iOCT) has enabled depth-resolved intraoperative imaging of retinal microstructures. Despite recent advancements, iOCT of surgical maneuvers remains challenging because the imaging field-of-view requires manual adjustment and tracking. To overcome this limitation, we previously demonstrated spectrally encoded coherence tomography and reflectometry (SECTR), which provides OCT imaging and a complementary en face view for visualization of surgical instruments and tool-tracking. Here, we demonstrate ophthalmic imaging with an intraoperative SECTR (iSECTR) system integrated with a surgical microscope. We believe that iSECTR will allow for real-time feedback on the location and depth of surgical instruments to better guide ophthalmic surgery.

We present a flexible OCT engine for acquiring full eye-length, anterior and posterior segment B-scans, as well as 4D live volumes with an effective A-scan rate of up to 2MHz. It is enabled by a MEMS tunable VCSEL with flexible A-scan rates, broad spectral bandwidth and a long instantaneous coherence length. Our GPU based, custom reconstruction and rendering software is able to process and display live volume series at rates of up to 17 volumes per second. We show B-scans and volume series of model eyes.

Optical coherence tomography (OCT) is one of imaging technologies to diagnose ophthalmic diseases and to monitor progresses of treatments. Therefore, OCT and OCT-angiography (OCTA) have actively developed in the field of ophthalmology, and retinal phantoms for evaluation of performance of OCT and OCTA also have been studied. In this presentation, we will show methods and results of a retinal phantom that can evaluate the optical performance of OCT and OCTA. The retinal phantom not only mimicked superficial vascular networks more realistically, but also implemented retinal layers with curvature. It consists of two microfluidic channels corresponding to superficial and deep retinal vessels, two multi-layered thin films, and base plate with retinal curvature. Each was attached with oxygen plasma for 1-min and fluorinated ethylene propylene (FEP) tubes was connected to the inlet and outlet holes. We could obtain cross-sectional OCT images and en-face OCTA images using lab-made OCT system.

Accurate and reproducible OCT angiography (OCTA) measurements are highly dependent on the overall phase stability of the sample. Raster-scanning OCT systems are vulnerable to eye motion, which makes phase correlation impossible if the retinal displacement is too large. Numerical methods exist to correct components of phase shifts due to the axial movement, but that due to lateral movement bigger, then imaging spot are not generally correctable. Real-time eye tracking provides a method to reduce the phase shifts caused by lateral eye movement. Here we report the advancements on monitoring ocular metrics during OCTA acquisition and its effects on image quality.

We introduce two multimodal extensions of our optical coherence tomography (OCT) based tethered capsule endomicroscopy (TCE) platform, tailored towards an enhanced clinical applicability for upper gastrointestinal tract imaging. The first extension presented – white light (RGB)-OCT-based TCE – provides both, true-color visualization of the tissue surface and depth-resolved sub-surface OCT imaging, co-registered in time and space. The second extension presented – fluorescence (FL)-OCT-based TCE – enables the addition of tissue specific molecular contrast. We present compact, modular, easily portable, plug-and-play system designs for both extensions, as well as imaging results in swine esophagus, in vivo.

Idiopathic pulmonary fibrosis (IPF) is a fatal form of fibrotic interstitial lung disease (ILD). Early diagnosis of IPF is essential, but often requires invasive surgery. We conduct a prospective study evaluating the diagnostic accuracy of endobronchial optical coherence tomography (EB-OCT) for IPF diagnosis as compared to concurrent surgical lung biopsy (SLB) and clinical follow-up diagnosis. EB-OCT was performed immediately prior to SLB in 27 ILD patients. EB-OCT was 100% sensitive and 100% specific for both histologic and clinical follow up diagnosis of IPF. The results demonstrate the potential of EB-OCT as minimally-invasive, low-risk in vivo method for microscopic IPF diagnosis.

Optical coherence tomography (OCT) with axial and lateral resolution of 1 µm, termed microscopic OCT (mOCT), is suited for the investigation of dynamic processes on cellular level. An improvement in contrast to visualize cellular structures can be achieved by evaluating inherent signal fluctuation. This so-called dynamic contrast was recently demonstrated for the widely used scanning frequency domain OCT (FD-OCT). Here we show comparative measurements of dynamic microscopic OCT (dmOCT) and multiphoton autofluorescence imaging of ex-vivo trachea. Compared to multiphoton microscopy, the dmOCT provides greater penetration depth, can visualize structures that are not detectable by autofluorescence imaging and has no risk of photodamage.

Optical Coherence Tomography (EOCT) systems can acquire high-resolution in vivo, real-time images of the human esophagus and so, they can be an important tool for the early diagnosis and prognosis of serious esophageal diseases such as Barrett’s, dysplasia and esophageal cancer. In this study, we compare various machine learning methods for tissue segmentation and classification of esophageal tissue using in vivo OCT images. An automated algorithm is proposed able to detect normal tissue from Barrett’s Esophagus (BE) and dysplasia. The classification was based on several features extracted from each A-Scan of EOCT images. The areas of the epithelium were annotated as normal, BE or dysplasia by an expert.The results of various machine learning (ML) classifiers have shown that a neural network based approach provided the best performance, separating BE from dysplasia, for individual A-Scans, with an accuracy of 89%.

The feasibility of ultrahigh-resolution OCT capsule endoscopy working at 800 nm was demonstrated by imaging ex vivo freshly resected human esophageal tissues, and its performance was compared with conventional volumetric laser endomicroscopy (VLE) working at 1300 nm. The results showed that the ultrahigh-resolution capsule can identify histology-confirmed diagnostic features with much finer details and contrast, particularly in mucosal layers, and demonstrated its promising potential of differentiating pathological changes in earlier stages.

In this ex vivo study, we report the first use of texture features and computer vision-based image features acquired from en face scattering coefficient maps to diagnose colorectal diseases. From these maps, texture features were extracted from a gray-level co-occurrence matrix algorithm, and computer vision-based image features were derived using a scale-invariant feature transform algorithm. Twenty-five features were obtained and thirty-three patients were recruited. Machine learning models were trained using an optimal feature set. The trained models achieved 94.7% sensitivity and 94.0% specificity for differentiating abnormal from normal, and 86.9% sensitivity and 85.0% specificity when distinguishing adenomatous polyp from cancer.

In this study, we propose to combine miniaturized optical coherence tomography (OCT) catheter with pattern recognition (PR) OCT for differentiation of normal from neoplastic colorectal tissue in real-time. The OCT catheter has a lateral resolution of 17.15 um and an axial resolution of 6 um. The PR-OCT system is trained by RetinaNet for pattern recognition tasks. Our method leverages the recent advancement in object detection, which localizes and classifies the diagnostic features at real-time, and the integration of an endoscopy, which promises future in vivo studies. According to our previous reports, a sensitivity of 100% and specificity of 99.7% can be reached.

We introduce the concept of a virtual biopsy, by using generative neural networks to synthesize virtual H&E sections from OCT images. To do this, we have developed a novel technique, “optical barcoding”, which has allowed us to construct a large dataset of OCT-H&E image pairs of human skin collected during Mohs surgery. Our results demonstrate the ability to use OCT images to generate high-fidelity virtual H&E sections and entire 3D H&E volumes. We hope that our novel method will allow clinicians to circumvent the time- and resource-consuming step of an invasive biopsy procedure.

We present novel approaches of implementing state-of-the-art deep learning techniques for the processing of optical coherence tomography angiography (OCT-A) images for the classification of diabetic retinopathy (DR) severity. The effects of feature-engineering on a deep neural network’s classification performance is compared against unprocessed OCT-A images. We investigate the effects of lower axial resolution (simulated by using a narrower spectral bandwidth) on the classification of DR severity, and the recovery of lost features using a generative adversarial network. We also explore the relationship between DR severity classification and lateral resolution.

Accurate retinal layer segmentation, especially the peripapillary retinal nerve fiber layer (RNFL) is critical for the diagnosis of ophthalmic diseases. However, due to the complex morphologies of the peripapillary region, most of the existing methods focus on segmenting the macular region and could not be directly applied to the peripapillary retinal optical coherence tomography (OCT) images. In this paper, we propose a novel graph convolutional network (GCN)-assisted segmentation framework based on a U-shape neural network for peripapillary retinal layer segmentation in OCT images. We argue that the strictly stratified structure of retina layers in addition to the centered optic disc is an ideal objective for GCN. Specifically, a graph reasoning block is inserted between the encoder and decoder of the U-shape neural network to conduct spatial reasoning. In this way, the peripapillary retina in OCT images is segmented into nine layers including RNFL. The proposed method was trained and tested on our collected dataset of peripapillary retinal OCT images. Experimental results showed that our segmentation method outperformed other state-of-the-art methods. In particular, compared with ReLayNet, the average and RNFL Dice coefficients are improved by 1.2% and 2.6%, respectively.

We demonstrate resolution-enhanced optical coherent tomography (RE-OCT) via coherent averaging and computational spatial bandwidth expansion. RE-OCT leverages coherent average of the complex OCT signal across multiple acquisitions to suppress noise. The enhanced sensitivity accommodates expansion of spatial bandwidth via multiplication with a spatial-frequency mask. We achieved resolution enhancement of 1.5 times with 40 acquisitions, without sacrificing signal-to-background ratio of single-shot image, in silicone phantom. In collagen, RE-OCT revealed collagen fiber structures that were not resolvable in single-shot or coherent-averaged image. RE-OCT demonstrates an added flexibility to existing image improvement techniques in coherent imaging—resolution, in addition to contrast, enhancement.

Quantum Optical Coherence Tomography (Q-OCT) is a non-classical equivalent of Optical Coherence Tomog- raphy (OCT) able to provide an increased axial resolution and immunity to even orders of dispersion. The main drawback of Q-OCT is artefacts which are additional elements that clutter an A-scan and lead to a complete loss of structural information for multilayered objects. To retrieve the structure of the object, we propose to use machine learning in Fourier domain Q-OCT. We present preliminary results for a model based on VGG16 architecture and compare it to analytical algorithms. We show on computer-generated data that machine learning outperforms previously proposed analytical algorithms. The trained neural network requires much less input data and achieves much better results than the analytical algorithms. Finally, due to the way the training of the neural network is performed and the increased axial resolution of Q-OCT, machine learning provides super-resolution - it is able to distinguish two peaks which are otherwise unresolved in traditional OCT.

Polarization Sensitive Optical Coherence Tomography (PS-OCT) measures the polarization state of backscattered light and provides information regarding its retardation and depolarization caused by propagation through tissue microstructure. Developing molecular contrast agents could enhance the utility of OCT for physiological, cellular, and molecular imaging. In this study, we characterize for the first time the depolarization signature of gold nanobipyramids (GNBPs) and demonstrate how depolarization can be used to enhance Spectral Contrast OCT based detection of GNBPs for multiplexed contrast agent detection.

Dynamic FFOCT allows us to record the intrinsic motion of biological samples in 3D, over hours. We performed scratch assays on primary porcine RPE and human induced pluripotent stem cells derived RPE cell cultures. We plotted motion maps from the optical flow. For wounds <40µm, the cell layer close the wound at different speeds depending on the type of RPE cells. For bigger wounds, the cell layer retract, mimicking degenerative diseases. Comparison between Dynamic FFOCT images and Immuno-chemistry images showed that mitochondria may contribute to the dynamic profile of cells. Dynamic FFOCT can be useful for the study of regenerative medicine.

This work introduces a method to objectively and non-invasively detect subretinal fibrosis secondary to neovascular age-related macular degeneration. The method exploits the birefringent properties of fibrous tissue: the axis orientation of fibrous tissues is well-defined, whereas the axis orientation of non-fibrous tissues is random. After removal of the influences from birefringent tissues anterior to the fibrosis, fibrotic scar tissues were the only remaining areas with uniform axis orientation and therefore became distinguishable from healthy tissue. The algorithm detected fibrosis congruent to color fundus photography, which is the current gold standard to diagnose fibrosis.

As red blood cells flowing in capillaries pass through the focal volume of an optical coherence tomography (OCT) system, they will induce a transient change in back-scattering, leading to peaks in the intensity of the OCT signal. In recent years, an OCT method has been devised exploiting these intensity fluctuations to estimate the hemodynamic parameters, such as RBC speed and flux. In this work, using concurrent OCT and two-photon microscopy, we assess the accuracy of the technique, highlighting that single red blood cells can indeed be detected in OCT, in specific hemodynamic and experimental settings.

Traditional methods for quantifying micro-fluid velocity using optical coherence tomography (OCT) can be divided into either phase-based or amplitude-based methods. Phase-based methods are less sensitive to the transverse velocity components and suffer from wrapped phase and phase instability problems, while amplitude-based methods focus more on segmenting flow areas instead of quantifying velocities. To solve these problems, we propose a method we termed optical flow optical coherence tomography (OFOCT) that can accurately compute spatially resolved velocity fields. An iterative solution to the proposed method is implemented using the graphics processing unit (GPU) for real-time data processing. The accuracy of the proposed method is verified through phantom flow experiments.

One limitation of phase shifting optical coherence microscopy (OCM) is that any changes in imaging conditions, caused by either system-level instability or movement of dynamic samples, will inevitably lead to reduction in image quality. Here we present a novel implementation of OCM which permits accurate measurements of phase and intensity, by simultaneously capturing four phase-shifted images on a single camera sensor. Uniquely, our approach enables probing the sample with unpolarized light, producing four phase-shifted interferograms, separated by π2 in two orthogonally polarized detection channel pairs. The proposed set-up is compatible with spatially incoherent illumination sources, enabling high resolution imaging due to its inherently reduced susceptibility to speckle noise. By simultaneously capturing the four phase-shifted images on a single CCD camera chip we demonstrate the capacity to acquire stable phase recordings with a phase sensitivity of 0.1 nm rms error at 500 Hz frame rates, a field-of-view of 250x250 μm, and 1μm lateral resolution. Instantaneous, coherence-gated, phase-sensitive imaging at high frame rates enables observations of the dynamics in cells and tissues, providing instant feedback on metabolic activity and tissue states. We present the novel design and methodology that enables ultrasensitive label-free coherence-gated measurements of intracellular dynamics in cells and cellular networks.

Living cells exhibit active intracellular molecular motion that reflect their functional states. Traditional microscopy techniques that solely capture high resolution static images of cells misses out on the opportunity to tap into the wealth of information provided by complex intracellular activity. We recently developed dynamic micro-OCT (DμOCT), an extension of μOCT that achieves near-isotropic sub-cellular resolution in all three dimensions (2 µm lateral x 1 µm axial). DμOCT substantially enhanced the contrast of cells and organelles while revealing stratified, depth-dependent dynamics in the epithelial layers. In this work, we have expanded the application DμOCT to encompass imaging of human skin in vivo, and detection of pharmacologic-induced changes in human melanoma spheroids and murine tissues.

We demonstrated computational multi-directional optical coherence tomography to visualize the microstructural directionality of a tissue. It uses numerical sub-aperture masks to the spatial frequency spectrum of en face OCT signals. An OCT signal consists of the intensity and phase, and they are processed independently. By applying the sub-aperture masks with several directions to the intensity signal’s spectrum, we can determine the dominant en face direction of the microstructure. In addition, sub-resolution depth-orientation of the microstructure is obtained by sub-aperture processing of the OCT phase signal. The microstructural directionality of bovine Achilles tendon and chicken breast muscle samples were visualized.

Structural optical coherence tomography (OCT) images stand to benefit from increased contrast. The OCT attenuation coefficient has previously been explored as a means of additional contrast, as it is sensitive to sub-resolution physical properties of the sample, which could be advantageous in differentiating various tissue pathologies. We have developed a new method for OCT attenuation coefficient quantification which combines advantages of prior methods of exponential fitting and the depth-resolved algorithm, and additionally calculates a layer-resolved backscattering fraction. Together, these quantitative scattering parameters provide enhanced accuracy and contrast that could aid in image interpretation in clinical applications such as intravascular OCT.

We present a new no-invasive label-free OCT-based tissue dynamics/subcellular motion imaging method to visualize and quantify the tissue activity of tumor spheroid. Our method is based on the statistical analysis of the OCT intensity fluctuations of the rapidly acquired OCT signals. The analysis includes log intensity variance (LIV) and OCT correlation decay speed (OCDS). The presented methods have been utilized to visualize and quantify the necrotic activity of the human originated tumor spheroids along 20 hours as cross-sectional and 3-D tomography. This necrotic activity of the spheroid has been not only visualized as an image, but also quantification of the necrotic cell ratio in the spheroid region has been presented.

Dynamic-focusing optical coherence tomography (OCT) can provide ~1-μm isotropic resolution in extended depth of field, and Mirau design is preferred because of lower weight. In previous works, we reported a Mirau-type OCT based on high-radiance spatially-incoherent source. However, the illumination intensity is limited by the etendue. Besides, coherent artifacts increases with the partially coherent illumination scheme. In this report, we introduce a method to narrow illumination linewidth to 5 μm and reduce coherence area to 1 μm2. With spatially incoherent illumination, optimized throughput and low-weight design, low-speckle B-scan can be acquired within 0.25 second.

Label-free high-resolution imaging in vivo plays an important role in biomedical studies of vasculature. Based on Gabor-domain optical coherence microscopy (GD-OCM), an innovative dynamic 3D imaging technique named ultrahigh resolution factor angiography (URFA) was developed by modeling the OCM datasets as latent variables, with the common factor representing the variance shared among the datasets, and the anisotropic unique factor representing tissue motion in specific dataset. By separately modeling the tissue motion, the associated artifacts were largely removed. Compared to a direct differentiation of OCM signals, the URFA algorithm improves the visualization of capillaries with reduced motion artifacts.

Bulk phase error (BPE) caused by environmental fluctuations will be a crucial problem for complex numerical processing of optical coherence tomography (OCT). We established a fully numerical phase stabilization method so-called smart-integration-path (SIP) method, which first estimates the vectorial gradient of BPE from a volumetric OCT data, then computes BPE from the gradient, and finally corrects BPE. The performance of SIP was subjectively evaluated by computational refocusing performance of ex vivo samples. The performance was also objectively evaluated by the standard deviations of spatial frequency spectra of en face OCT images. Both analyses showed positive effects of the BPE correction.

Optical coherence tomography can acquire the entire depth-resolved profile of the sample at a single shot (A-scan). However, the lateral resolution changes with depth, achieving the smallest focus at the focal plane and increasing significantly with the defocusing distance, especially for the high numerical aperture lens. In order to maintain a high resolution over a large depth range, a novel diffractive optical element is developed to construct a needle-shaped beam with a depth-of-focus of 10- to 150-fold Rayleigh lengths, a relatively constant diameter, a uniform axial intensity distribution, and negligible sidelobes. The lateral resolution improvement is experimentally demonstrated.

We demonstrate that the coherence roll-off and dynamic range of OCT systems using Fourier-domain mode-locked (FDML) lasers can be significantly improved by a high-finesse fiber Fabry-Perot tunable filter (FFP-TF). The newly developed high-finesse FFP-TFs have a finesse of more than 3000, a more than fivefold improvement over previous designs. We show that this results in reduced instantaneous laser linewidth and reduced noise for a 1310 nm FDML laser with 1.6 MHz sweep rate. Since in practice, OCT image range is limited by data acquisition bandwidth, we demonstrate OCT imaging over many centimeters by reducing the sweep range of the laser. In contrast to previous work, standard resampling using a pre-acquired signal (as in SD-OCT) with no k-clocking is sufficient for both small and large sweep range, significantly reducing the system complexity. Live 3D-OCT video rate imaging at 20 cm imaging range is demonstrated.

Measuring the point spread function (PSF) rolloff is a time-consuming part of spectral/Fourier domain Optical Coherence Tomography (SD-OCT) system characterization. Here, we introduce a method that predicts the PSF (sensitivity and axial resolution) rolloff without an interferometer. Instead, the method analyzes correlations of incoherent excess noise from the light source across the detector array to characterize the spectrometer response. We demonstrate this technique using supercontinuum and superluminescent diode sources, showing that a noise time course of just 0.5 seconds predicts axial imaging performance, as confirmed by meticulous measurements using a Michelson interferometer. This approach promises to facilitate SD-OCT system development.

Fourier-domain mode-locked (FDML) laser sources allow OCT imaging systems with sweep rates reaching 1.6 MHz. However, physical limitations of conventional scanning systems require consideration. Resonant scanners fix imaging frequency, whereas galvanometer scanner (GS) systems require either raster scanning with large flyback that reduce usable data, or bi-directional scans that require many repeated scans. To overcome these limitations, we employed an effective 1.6 MHz stepped bi-directional scanning protocol, allowing 2 BM-scan OCT-angiography (OCTA) imaging using high-speed FDML. We present the ideal acquisition protocol and processing algorithm for optimum OCTA quality, with results demonstrating high vasculature contrast at 1.6 MHz scan rates.

Low NEP balanced receivers generally do not result in better sensitivity in a shot-noise-limited swept source OCT system. However, there is an advantage if RIN is significant. A lower NEP, even in the shot-noise-limited case, does allow for lower reference arm powers. This, in turn, reduces fixed pattern artifact signals caused by stray optical component reflections inside a swept source laser cavity. An NEP reduction of √10 allows the reference power to be reduced 10 dB while maintaining SNR. This reduces the pattern to noise ratio by 10 dB because pattern noise is directly detected (20 dB scaling per decade reference power), whereas the image signal is heterodyne detected (10 dB scaling per decade of reference power). We present sensitivity and fixed pattern measurements taken with six commercial balanced receivers, including an APD receiver. We also present an NEP survey of 23 commercial receivers over a wide range of bandwidths and transimpedances.

Optical Coherence Tomography (OCT) has evolved into a powerful clinical tool, with a wide range of applications in ophthalmology. However, most OCT systems for real-time volumetric (3D) and in vivo imaging suffer from image distortion due to motion artifacts induced by involuntary and physiological movements of the living tissue. Several methods have been proposed to obtain motion-free images, yet they are generally limited in their applicability due to long acquisition times, requiring multiple volumes [1], and/or the need for additional hardware [2]. Here we propose and analyze a motion-compensated 3D-OCT imaging system that uses a higher-order regression analysis and show that it can effectively correct the motion artifacts within 0 to 5 Hz in real time without requiring additional hardware.

Image artifacts due to the bulk motion of the sample are well known and described. Some methods of correction in Full-Filed Swept-Source OCT were presented. For the measurement of dynamic sample motion, the axial motion artifacts can significantly influence measured signals. Here, we investigate the axial shift phenomena for measurements with low cost, reduced speed swept laser. Simulation results have led us to the way of correction of the axial shift artifact by manipulation of the phase of OCT fringes. Results of disturbed and undisturbed measurement of vibrating speaker membrane or induced deformations of the porcine eye are presented.

In this pilot study, a multi-modal optical coherence microscopy (OCM) and fluorescence imaging system in the visible wavelength region was utilized to investigate the intra-tumor heterogeneity of lower grade brain tumor samples. OCM intensity data derived attenuation coefficients were calculated and cell density maps were created from intensity en-face projections. Sequentially preoperatively administrated 5-ALA fluorescence intensities were evaluated using the multi-modal setup. Optical imaging data results were compared to histology-derived cell density and cell proliferation rate maps.

A three-dimensional multi-contrast tissue dynamics imaging method based on polarization-sensitive optical coherence tomography is presented to visualize microvascular tissue activity of mouse livers. Temporal variance of birefringence, temporal polarization uniformity and logarithmic OCT intensity variance are used to access the tissue dynamics. These methods are applied to time-course microvasculature activity visualization of dissected normal and inflammatory mouse liver. Multi-contrast projection images are generated to visualize vascular network of the liver. Cross-sectional and en face dynamics images show high activity around the periportal region of mouse liver at initial time point. Degradation of tissue activity is demonstrated by time-lapse imaging.

The aim of this study was to evaluate the application of attenuation coefficients calculated from the cross-polarization optical coherence tomography (CP OCT) data for differentiation of breast cancer from non-tumorous breast tissues. For this purpose, surgically obtained breast specimens from 45 patients were examined using CP OCT in order to construct color-coded en-face OCT maps based on calculation of three optical coefficients (the commonly used rate of attenuation in the co- polarization channel; and, additionally, the attenuation in the cross- polarization channel; and the interchannel attenuation difference). It was shown that the use of these optical coefficients significantly increased the amount of obtained information from the OCT data in comparison with unprocessed images, enabling objective quantification for differentiating non-tumorous and tumorous tissue (adipose tissue, normal stroma, tumor stroma and agglomerates of tumor cells). The attenuation in the cross- polarization channel and the interchannel attenuation difference provided greater contrast for the visualization of the different breast cancer structures compared to the attenuation coefficient in the co- polarization channel. The findings of this study suggest that assessment of CP OCT images of breast cancer based on optical coefficients calculation may in perspective enable real-time feedback about accurate resection margin in patient with breast cancer to the surgeon.

Optical coherence tomography (OCT) has the potential to become an additional imaging modality for surgical guidance in the field of neurosurgery, especially when it comes to the detection of different infiltration grades of glioblastoma multiforme at the tumor border. Interpretation of the images, however, is still a big challenge. A method to create a labeled OCT dataset based on ex vivo brain samples is introduced. The tissue samples were embedded in an agarose mold giving them a distinctive shape before images were acquired with two OCT systems (spectral domain (SD) and swept source (SS) OCT) and histological sections were created and segmented by a neuropathologist. Based on the given shape, the corresponding OCT images for each histological image can be determined. The transfer of the labels from the histological images onto the OCT images was done with a non-affine image registration approach based on the tissue shape. It was demonstrated that finding OCT images of a tissue sample corresponding to segmented histological images without any color or laser marking is possible. It was also shown that the set labels can be transferred onto OCT images. The accuracy of method is 26 ± 11 pixel, which translates to 192 ± 75 μm for the SS-OCT and 94 ± 43 μm for the SD-OCT. The dataset consists of several hundred labeled OCT images, which can be used to train a classification algorithm.

The separation of tumorous brain tissue and healthy brain tissue is still a big challenge in the field of neurosurgery, especially when it comes to the detection of different infiltration grades of glioblastoma multiforme at the tumor border. On the basis of a recently created labelled OCT dataset of ex vivo glioblastoma multiforme tumor samples the detection of brain tumor tissue and the identification of zones with varying degrees of infiltration of tumor cells was investigated. The identification was based on the optical properties, which were extracted by an exponential fit function. The results showed that a separation of tumorous tissue and healthy white matter based on these optical properties is possible. A support vector machine was trained on the optical properties to separate tumor from healthy white matter tissue, which achieved a sensitivity of 91% and a specificity of 76% on an independent training dataset.

Corneal biomechanical properties are useful for the diagnosis of ocular diseases and monitoring of therapeutic interventions. Elastography is one method of assessing corneal biomechanical properties but typically relies on an active source of displacement. Here, we present a passive elastography technique known as heartbeat optical coherence elastography (Hb-OCE), where corneal stiffness is quantified by measuring its response to the ocular pulse. The technique was performed before and after crosslinking, which stiffens the cornea. First, Hb-OCE was performed in situ to assess the feasibility of the technique. Next, the technique was performed in vivo, suggesting that Hb-OCE may be feasible for clinical assessment of corneal stiffness without the need for any contact or external excitation.

Pulmonary vein isolation with radiofrequency ablation (RFA) has become the most common procedure to treat Atrial Fibrillation (AF). However, current RFA lesion formation is guided only with indirect information (e.g. temperature, impedance, contact force), which does not guarantee transmural lesions. Non-transmural lesions are understood to contribute to AF recurrence. We have previously demonstrated that polarization-sensitive optical coherence tomography (PSOCT) can monitor RFA lesion transmurality in the left atrium (LA) of living swine. However, it requires expert interpretation. Here, we demonstrate quantitative image quality and lesion transmurality evaluation metrics applied to the in vivo LA RFA PSOCT lesion monitoring data.

Optical coherence tomography (OCT) can provide exquisite details of tissue microstructure without traditional tissue sectioning, with potential diagnostic and intraoperative applications in a variety of clinical areas. In thyroid surgery, OCT could provide information to reduce the risk of damaging normal tissue. Thyroid tissue's follicular structure alters in case of various pathologies including the non-malignant ones which can be imaged using OCT. The success of deep learning for medical image analysis encourages its application on OCT thyroid images for quantitative analysis of tissue microstructure. To investigate the potential of a deep learning approach to segment the follicular structure in OCT images, a 2D U-Net was trained on b-scan OCT images acquired from ex vivo adult human thyroid samples a effected by a range of pathologies. Results on a pool of 104 annotated images showed a mean Dice score of 0.74±0.19 and 0.92±0.09 when segmenting the follicular structure and the surrounding tissue on the test dataset (n=10 images). This study shows that a deep learning approach for tissue microstructure segmentation in OCT images is possible. The achieved performance without requiring manual intervention encourages the application of a deep-learning method for real-time analysis of OCT data.

Optical coherence tomography (OCT) has been widely used for imaging biological sample due to its capability of three-dimensional (3-D) reconstruction. Recently, polarization-sensitive optical coherence tomography (PSOCT) has been used to investigate polarization properties of samples such as retina and muscle [1,2]. Such extensions provide additional contrasts other than traditional reflected intensity and offer a 3-D multi-functional reconstruction of materials and biological tissue. PS-OCT uses A-scan wise computation to obtain the polarization properties from a set of OCT images. In this computation, the lateral resolution is implicitly assumed as infinitely high. However, the birefringence measurement itself will be affected by the lateral optical resolution, defocus, and aberrations. One simple way to obtain high-resolution birefringent data is to use an objective with higher NA. However, OCT also suffers from the trade-off between the lateral resolution and the depth-of-focus (DOF), which might limit its application in thick samples. There have been various methods to overcome this issue. Hardware solutions such as mechanical depth scanning [3] have been reported, but the additional configurations would increase the complexity of system. On the other hand, computational methods do not require additional hardware set-up and thus can be easily adopted, such as interferometric synthetic aperture microscopy (ISAM) [4] and forward model based computational refocusing [5]. However, to our acknowledgement, there are only few reports of long imaging depth for 3-D birefringence imaging. One presented work combined the ISAM with PS-OCT [6]. But the impact of refocusing on multi-contrast imaging, such as artifact in birefringence measurement, has not been thoroughly investigated. In this paper, we present a DOF extended polarization-sensitive imaging by applying computational refocusing to Jones-matrix based PS-OCT (JM-OCT). Computational refocusing is applied to each of four polarization channels (images) of JM-OCT, and the birefringence and degree-of-polarization-uniformity (DOPU) images are computed from the refocused OCT images. Enhanced lateral resolution away from the focus plane and hence extended DOF are demonstrated through phantom and ex vivo porcine muscle measurements. The ex vivo porcine muscle measurement also suggests that the refocusing may reduce the birefringence artifacts.

Deep convolutional neural network (CNN) based estimators for optical coherence tomography (OCT) are presented. The CNN analyze local OCT speckle patterns and estimate the sample’s scatterer density and OCT resolutions. This estimator is intensity invariant, i.e., it does not use the net signal strength of OCT even to estimate the scatterer density. The CNN is trained by a huge training dataset that was generated by a simple simulator of OCT imaging. And hence, the CNN is trained without experimental datasets. The performance of CNN was evaluated by numerically generated OCT images, and good accuracies of the estimation were shown.

Optical coherence tomography (OCT) can be an important diagnostic tool in optics R&D and manufacturing. We illustrate this with four applications. (1) Imaging of vibration modes of MEMS membranes. (2) Strain mapping of VCSEL chips. (3) Optical frequency domain reflectometry (OFDR) of laser cavities. (4) Combined responsivity and 3D mapping of photodiode packages.

In this study, we developed a novel dual-side view OCT (DSV-OCT) system for thickness measurement on opaque materials. The dual-side view was achieved on conventional swept source OCT platform by creating two symmetrical sampling arms. This allows to image both sides of the material simultaneously and produce the surface contours of the two sides in a single C-scan. Finally, the thickness of the opaque material can be calculated from the two surface contours above. We evaluated the performance of our DSV-OCT using a microscope slide as sample. The results demonstrated that our DSV-OCT has a good capability for thickness measurement on opaque materials with an accuracy of about 3 μm.

Here, we analytically study the signal digitization procedure in FD-OCT and propose a novel mixed-signal framework to model its time-domain image formation. It turns out that FD-OCT is a shift-variant system, if the conventional IDFT-based technique is used to reconstruct the A-lines. Specifically, both amplitude and phase responses of the system are dependent on the axial location of the input sample. We believe this finding could provide us with new insights towards the image reconstruction of FD-OCT and guide researchers to develop better reconstruction algorithms in the future.

FD-OCT is a widely used technology which could provide high-resolution 3D reconstruction images. Conventional OCT uses IDFT reconstruction method, which could obtain an FFT-limited axial resolution. Recently, several optimization-based methods have reportedly achieved a resolution improvement, while undesired noise artifacts might appear in their result, which could degrade the image quality. In this work, we proposed an iterative error reduction method in order to remove the artifacts as well as improve the resolution. A numerical simulation is designed to validate our algorithm. Two reconstruction methods including IDFT reconstruction, and l1 minimization are selected for a comparative study. Specifically, we conduct the simulation at four different noise levels. The result shows that our proposed method could greatly suppress the artifact and obtain a great reconstruction even when the SNR is reduced.