OCT Intensity Research Articles

Enhancing epidural needle guidance using a polarization-sensitive optical coherence tomography probe with convolutional neural networks.

Epidural anesthesia helpsmanage pain during different surgeries. Nonetheless, the precise placement of the epidural needle remains a challenge. In this study, we developed a probe based on polarization-sensitive optical coherence tomography (PS-OCT) to enhance the epidural anesthesia needle placement. The probe was tested on six porcine spinal samples. The multimodal imaging guidance used the OCT intensity mode and three distinct PS-OCT modes: (1) phase retardation, (2) optic axis, and (3) degree of polarization uniformity (DOPU). Each mode enabled the classification of different epidural tissues through distinct imaging characteristics. To further streamline the tissue recognition procedure, convolutional neural network (CNN) were used to autonomously identify the tissue types within the probe's field of view. ResNet50 models were developed for all four imaging modes. DOPU imaging was found to provide the highest cross-testing accuracy of 91.53%. These results showed the improved precision by PS-OCT in guiding epidural anesthesia needle placement.

Multi-Contrast Jones-Matrix Optical Coherence Tomography—The Concept, Principle, Implementation, and Applications

Jones-matrix optical coherence tomography (JM-OCT) is an extension of OCT that provides multiple types of optical contrasts of biological and clinical samples. JM-OCT measures the spatial distribution of the Jones matrix of the sample and also its time sequence. All contrasts (i.e., multi-contrast OCT images) are then computed from the Jones matrix. The contrasts obtained from the Jones matrix include the conventional and polarization-insensitive OCT intensity, cumulative and local phase retardation (birefringence), degree-of-polarization uniformity quantifying the polarization randomness of the sample, signal attenuation coefficient, sample scatterer density, Doppler OCT, OCT angiography, and dynamic OCT. JM-OCT is a generalized version of OCT because it measures the generalized form of the sample information; i.e., the Jones matrix sequence. This review summarizes the basic conception, mathematical principle, hardware implementation, signal and image processing, and biological and clinical applications of JM-OCT. Advanced technical topics, including JM-OCT-specific noise correction and quantity estimation and JM-OCT's self-calibration nature, are also described.

Synthesizing the degree of polarization uniformity from non-polarization-sensitive optical coherence tomography signals using a neural network.

Degree of polarization uniformity (DOPU) imaging obtained by polarization-sensitive optical coherence tomography (PS-OCT) has the potential to provide biomarkers for retinal diseases. It highlights abnormalities in the retinal pigment epithelium that are not always clear in the OCT intensity images. However, a PS-OCT system is more complicated than conventional OCT. We present a neural-network-based approach to estimate the DOPU from standard OCT images. DOPU images were used to train a neural network to synthesize the DOPU from single-polarization-component OCT intensity images. DOPU images were then synthesized by the neural network, and the clinical findings from ground truth DOPU and synthesized DOPU were compared. There is a good agreement in the findings for RPE abnormalities: recall was 0.869 and precision was 0.920 for 20 cases with retinal diseases. In five cases of healthy volunteers, no abnormalities were found in either the synthesized or ground truth DOPU images. The proposed neural-network-based DOPU synthesis method demonstrates the potential of extending the features of retinal non-PS OCT.

Synthetic polarization-sensitive optical coherence tomography by deep learning

Polarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution label-free optical biomedical imaging modality that is sensitive to the microstructural architecture in tissue that gives rise to form birefringence, such as collagen or muscle fibers. To enable polarization sensitivity in an OCT system, however, requires additional hardware and complexity. We developed a deep-learning method to synthesize PS-OCT images by training a generative adversarial network (GAN) on OCT intensity and PS-OCT images. The synthesis accuracy was first evaluated by the structural similarity index (SSIM) between the synthetic and real PS-OCT images. Furthermore, the effectiveness of the computational PS-OCT images was validated by separately training two image classifiers using the real and synthetic PS-OCT images for cancer/normal classification. The similar classification results of the two trained classifiers demonstrate that the predicted PS-OCT images can be potentially used interchangeably in cancer diagnosis applications. In addition, we applied the trained GAN models on OCT images collected from a separate OCT imaging system, and the synthetic PS-OCT images correlate well with the real PS-OCT image collected from the same sample sites using the PS-OCT imaging system. This computational PS-OCT imaging method has the potential to reduce the cost, complexity, and need for hardware-based PS-OCT imaging systems.

Minimizing OCT quantification error via a surface-tracking imaging probe.

OCT-based quantitative tissue optical properties imaging is a promising technique for intraoperative brain cancer assessment. The attenuation coefficient analysis relies on the depth-dependent OCT intensity profile, thus sensitive to tissue surface positions relative to the imaging beam focus. However, it is almost impossible to maintain a steady tissue surface during intraoperative imaging due to the patient's arterial pulsation and breathing, the operator's motion, and the complex tissue surface geometry of the surgical cavity. In this work, we developed an intraoperative OCT imaging probe with a surface-tracking function to minimize the quantification errors in optical attenuation due to the tissue surface position variations. A compact OCT imaging probe was designed and engineered to have a long working distance of ∼ 41 mm and a large field of view of 4 × 4 mm2 while keeping the probe diameter small (9 mm) to maximize clinical versatility. A piezo-based linear motor was integrated with the imaging probe and controlled based upon real-time feedback of tissue surface position inferred from OCT images. A GPU-assisted parallel processing algorithm was implemented, enabling detection and tracking of tissue surface in real-time and successfully suppressing more than 90% of the typical physiologically induced motion range. The surface-tracking intraoperative OCT imaging probe could maintain a steady beam focus inside the target tissue regardless of the surface geometry or physiological motions and enabled to obtain tissue optical attenuation reliably for assessing brain cancer margins in challenging intraoperative settings.

Compressive sensing for polarization sensitive optical coherence tomography

In this report, we report on the implementation of compressive sensing (CS) and sparse sampling in polarization sensitive optical coherence tomography (PS-OCT) to reduce the number of B-scans (frames consisting of an array of A-scans, where each represents a single depth profile of reflections) required for effective volumetric (3D dataset composed of an array of B-scans) PS-OCT measurements (i.e. OCT intensity, and phase retardation) reconstruction. Sparse sampling of PS-OCT is achieved through randomization of step sizes along the slow-axis of PS-OCT imaging, covering the same spatial ranges as those with equal slow-axis step sizes, but with a reduced number of B-scans. Tested on missing B-scan rates of 25%, 50% and 75%, we found CS could reconstruct reasonably good (as evidenced by a correlation coefficient >0.6) PS-OCT measurements with a maximum reduced B-scan rate of 50%, thereby accelerating and doubling the rate of volumetric PS-OCT measurements.

Polarization-sensitive optical coherence elastography.

Polarization-sensitive optical coherence elastography (PS-OCE) is developed for improved tissue discrimination. It integrates Jones matrix-based PS-optical coherence tomography (PS-OCT) with compression OCE. The method simultaneously measures the OCT intensity, attenuation coefficient, birefringence, and microstructural deformation (MSD) induced by tissue compression. Ex vivo porcine aorta and esophagus tissues were investigated by PS-OCE and histological imaging. The tissue properties measured by PS-OCE are shown as cross-sectional images and a three-dimensional (3-D) depth-trajectory plot. In this trajectory plot, the average attenuation coefficient, birefringence, and MSD were computed at each depth, and the trajectory in the depth direction was plotted in a 3-D feature space of these three properties. The tissue boundaries in a histological image corresponded with the depth-trajectory inflection points. Histogram analysis and t-distributed stochastic neighbour embedding (t-SNE) visualization of the three tissue properties indicated that the PS-OCE measurements provide sufficient information to discriminate porcine esophagus tissues.

OCT feature analysis guided artery-vein differentiation in OCTA.

Differential artery-vein analysis promises better sensitivity for retinal disease detection and classification. However, clinical optical coherence tomography angiography (OCTA) instruments lack the function of artery-vein differentiation. This study aims to verify the feasibility of using OCT intensity feature analysis to guide artery-vein differentiation in OCTA. Four OCT intensity profile features, including i) ratio of vessel width to central reflex, ii) average of maximum profile brightness, iii) average of median profile intensity, and iv) optical density of vessel boundary intensity compared to background intensity, are used to classify artery-vein source nodes in OCT. A blood vessel tracking algorithm is then employed to automatically generate the OCT artery-vein map. Given the fact that OCT and OCTA are intrinsically reconstructed from the same raw spectrogram, the OCT artery-vein map is able to guide artery-vein differentiation in OCTA directly.

Safety and Feasibility of Intrastromal Injection of Cultivated Human Corneal Stromal Keratocytes as Cell-Based Therapy for Corneal Opacities.

To evaluate the safety and feasibility of intrastromal injection of human corneal stromal keratocytes (CSKs) and its therapeutic effect on a rodent early corneal opacity model. Twelve research-grade donor corneas were used in primary culture to generate quiescent CSKs and activated stromal fibroblasts (SFs). Single and repeated intrastromal injections of 2 to 4 × 104 cells to rat normal corneas (n = 52) or corneas with early opacities induced by irregular phototherapeutic keratectomy (n = 16) were performed, followed by weekly examination of corneal response under slit-lamp biomicroscopy and in vivo confocal microscopy with evaluation of haze level and stromal reflectivity, and corneal thickness using anterior segment optical coherence tomography (AS-OCT). Time-lapse tracing of Molday ION-labelled cells was conducted using Spectralis OCT and label intensity was measured. Corneas were collected at time intervals for marker expression by immunofluorescence, cell viability, and apoptosis assays. Injected CSKs showed proper marker expression with negligible SF-related features and inflammation, hence maintaining corneal clarity and stability. The time-dependent loss of injected cells was recovered by repeated injection, achieving an extended expression of human proteoglycans inside rat stroma. In the early corneal opacity model, intrastromal CSK injection reduced stromal reflectivity and thickness, resulting in recovery of corneal clarity, whereas noninjected corneas were thicker and had haze progression. We demonstrated the safety, feasibility, and therapeutic efficacy of intrastromal CSK injection. The cultivated CSKs can be a reliable cell source for potential cell-based therapy for corneal opacities.

Generation and optimization of superpixels as image processing kernels for Jones matrix optical coherence tomography.

Jones matrix-based polarization sensitive optical coherence tomography (JM-OCT) simultaneously measures optical intensity, birefringence, degree of polarization uniformity, and OCT angiography. The statistics of the optical features in a local region, such as the local mean of the OCT intensity, are frequently used for image processing and the quantitative analysis of JM-OCT. Conventionally, local statistics have been computed with fixed-size rectangular kernels. However, this results in a trade-off between image sharpness and statistical accuracy. We introduce a superpixel method to JM-OCT for generating the flexible kernels of local statistics. A superpixel is a cluster of image pixels that is formed by the pixels' spatial and signal value proximities. An algorithm for superpixel generation specialized for JM-OCT and its optimization methods are presented in this paper. The spatial proximity is in two-dimensional cross-sectional space and the signal values are the four optical features. Hence, the superpixel method is a six-dimensional clustering technique for JM-OCT pixels. The performance of the JM-OCT superpixels and its optimization methods are evaluated in detail using JM-OCT datasets of posterior eyes. The superpixels were found to well preserve tissue structures, such as layer structures, sclera, vessels, and retinal pigment epithelium. And hence, they are more suitable for local statistics kernels than conventional uniform rectangular kernels.

Robust and fast characterization of OCT-based optical attenuation using a novel frequency-domain algorithm for brain cancer detection

Cancer is known to alter the local optical properties of tissues. The detection of OCT-based optical attenuation provides a quantitative method to efficiently differentiate cancer from non-cancer tissues. In particular, the intraoperative use of quantitative OCT is able to provide a direct visual guidance in real time for accurate identification of cancer tissues, especially these without any obvious structural layers, such as brain cancer. However, current methods are suboptimal in providing high-speed and accurate OCT attenuation mapping for intraoperative brain cancer detection. In this paper, we report a novel frequency-domain (FD) algorithm to enable robust and fast characterization of optical attenuation as derived from OCT intensity images. The performance of this FD algorithm was compared with traditional fitting methods by analyzing datasets containing images from freshly resected human brain cancer and from a silica phantom acquired by a 1310 nm swept-source OCT (SS-OCT) system. With graphics processing unit (GPU)-based CUDA C/C++ implementation, this new attenuation mapping algorithm can offer robust and accurate quantitative interpretation of OCT images in real time during brain surgery.

Tissue perfusion modelling in optical coherence tomography

BackgroundOptical coherence tomography (OCT) is a well established imaging technique with different applications in preclinical research and clinical practice. The main potential for its application lies in the possibility of noninvasively performing “optical biopsy”. Nevertheless, functional OCT imaging is also developing, in which perfusion imaging is an important approach in tissue function study. In spite of its great potential in preclinical research, advanced perfusion imaging using OCT has not been studied. Perfusion analysis is based on administration of a contrast agent (nanoparticles in the case of OCT) into the bloodstream, where during time it specifically changes the image contrast. Through analysing the concentration-intensity curves we are then able to find out further information about the examined tissue.MethodsWe have designed and manufactured a tissue mimicking phantom that provides the possibility of measuring dilution curves in OCT sequence with flow rates 200, 500, 1000 and 2000 μL/min. The methodology comprised of using bolus of 50 μL of gold nanorods as a contrast agent (with flow rate 5000 μL/min) and continuous imaging by an OCT system. After data acquisition, dilution curves were extracted from OCT intensity images and were subjected to a deconvolution method using an input–output system description. The aim of this was to obtain impulse response characteristics for our model phantom within the tissue mimicking environment. Four mathematical tissue models were used and compared: exponential, gamma, lagged and LDRW.ResultsWe have shown that every model has a linearly dependent parameter on flow (R^2 values from 0.4914 to 0.9996). We have also shown that using different models can lead to a better understanding of the examined model or tissue. The lagged model surpassed other models in terms of the minimisation criterion and R^2 value.ConclusionsWe used a tissue mimicking phantom in our study and showed that OCT can be used for advanced perfusion analysis using mathematical model and deconvolution approach. The lagged model with three parameters is the most appropriate model. Nevertheless, further research have to be performed, particularly with real tissue.

Retinal oximetry in humans using visible-light optical coherence tomography [Invited

We measured hemoglobin oxygen saturation (sO2) in the retinal circulation in healthy humans using visible-light optical coherence tomography (vis-OCT). The measurements showed clear oxygenation differences between central retinal arteries and veins close to the optic nerve head. Spatial variations at different vascular branching levels were also revealed. In addition, we presented theoretical and experimental results to establish that noises in OCT intensity followed Rice distribution. We used this knowledge to retrieve unbiased estimation of true OCT intensity to improve the accuracy of vis-OCT oximetry, which had inherently lower signal-to-nose ratio from human eyes due to safety and comfort limitations. We demonstrated that the new statistical-fitting sampling strategy could reduce the estimation error in sO2 by three percentage points (pp). The presented work aims to provide a foundation for using vis-OCT to achieve accurate retinal oximetry in clinical settings.

Differential standard deviation of log-scale intensity based optical coherence tomography angiography.

In this paper, a differential standard deviation of log-scale intensity (DSDLI) based optical coherence tomography angiography (OCTA) is presented for calculating microvascular images of human skin. The DSDLI algorithm calculates the variance in difference images of two consecutive log-scale intensity based structural images from the same position along depth direction to contrast blood flow. The en face microvascular images were then generated by calculating the standard deviation of the differential log-scale intensities within the specific depth range, resulting in an improvement in spatial resolution and SNR in microvascular images compared to speckle variance OCT and power intensity differential method. The performance of DSDLI was testified by both phantom and in vivo experiments. In in vivo experiments, a self-adaptive sub-pixel image registration algorithm was performed to remove the bulk motion noise, where 2D Fourier transform was utilized to generate new images with spatial interval equal to half of the distance between two pixels in both fast-scanning and depth directions. The SNRs of signals of flowing particles are improved by 7.3 dB and 6.8dB on average in phantom and in vivo experiments, respectively, while the average spatial resolution of images of in vivo blood vessels is increased by 21%.

OCT intensity and phase fluctuations correlated with activity-dependent neuronal calcium dynamics in the Drosophila CNS [Invited

Phase-resolved OCT and fluorescence microscopy were used simultaneously to examine stereotypic patterns of neural activity in the isolated Drosophila central nervous system. Both imaging modalities were focused on individually identified bursicon neurons known to be involved in a fixed action pattern initiated by ecdysis-triggering hormone. We observed clear correspondence of OCT intensity, phase fluctuations, and activity-dependent calcium-induced fluorescence.

Automated detection of vessel lumen and stent struts in intravascular optical coherence tomography to evaluate stent apposition and neointimal coverage.

Intravascular optical coherence tomography (IV-OCT) is a high-resolution imaging method used to visualize the microstructure of arterial walls in vivo. IV-OCT enables the clinician to clearly observe and accurately measure stent apposition and neointimal coverage of coronary stents, which are associated with side effects such as in-stent thrombosis. In this study, the authors present an algorithm for quantifying stent apposition and neointimal coverage by automatically detecting lumen contours and stent struts in IV-OCT images. The algorithm utilizes OCT intensity images and their first and second gradient images along the axial direction to detect lumen contours and stent strut candidates. These stent strut candidates are classified into true and false stent struts based on their features, using an artificial neural network with one hidden layer and ten nodes. After segmentation, either the protrusion distance (PD) or neointimal thickness (NT) for each strut is measured automatically. In randomly selected image sets covering a large variety of clinical scenarios, the results of the algorithm were compared to those of manual segmentation by IV-OCT readers. Stent strut detection showed a 96.5% positive predictive value and a 92.9% true positive rate. In addition, case-by-case validation also showed comparable accuracy for most cases. High correlation coefficients (R > 0.99) were observed for PD and NT between the algorithmic and the manual results, showing little bias (0.20 and 0.46 μm, respectively) and a narrow range of limits of agreement (36 and 54 μm, respectively). In addition, the algorithm worked well in various clinical scenarios and even in cases with a low level of stent malapposition and neointimal coverage. The presented automatic algorithm enables robust and fast detection of lumen contours and stent struts and provides quantitative measurements of PD and NT. In addition, the algorithm was validated using various clinical cases to demonstrate its reliability. Therefore, this technique can be effectively utilized for clinical trials on stent-related side effects, including in-stent thrombosis and in-stent restenosis.

Characterizing optical properties of nano contrast agents by using cross-referencing OCT imaging

We report a cross-referencing method to quickly and accurately characterize the optical properties of nanoparticles including the extinction, scattering, absorption and backscattering cross sections by using an OCT system alone. Among other applications, such a method is particularly useful for developing nanoparticle-based OCT imaging contrast agents. The method involves comparing two depth-dependent OCT intensity signals collected from two samples (with one having and the other not having the nanoparticles), to extract the extinction and backscattering coefficient, from which the absorption coefficient can be further deduced (with the help of the established scattering theories for predicting the ratio of the backscattering to total scattering cross section). The method has been experimentally validated using test nanoparticles and was then applied to characterizing gold nanocages. With the aid of this method, we were able to successfully synthesize scattering dominant gold nanocages for the first time and demonstrated the highest contrast enhancement ever achieved by the gold nanocages (and by any nanoparticles of a similar size and concentration) in an in vivo mouse tumor model. This method also enables quantitative analysis of contrast enhancement and provides a general guideline on choosing the optimal concentration and optical properties for the nanoparticle-based OCT contrast agents.

GPU accelerated real-time multi-functional spectral-domain optical coherence tomography system at 1300nm

We present a GPU accelerated multi-functional spectral domain optical coherence tomography system at 1300nm. The system is capable of real-time processing and display of every intensity image, comprised of 512 pixels by 2048 A-lines acquired at 20 frames per second. The update rate for all four images with size of 512 pixels by 2048 A-lines simultaneously (intensity, phase retardation, flow and en face view) is approximately 10 frames per second. Additionally, we report for the first time the characterization of phase retardation and diattenuation by a sample comprised of a stacked set of polarizing film and wave plate. The calculated optic axis orientation, phase retardation and diattenuation match well with expected values. The speed of each facet of the multi-functional OCT CPU-GPU hybrid acquisition system, intensity, phase retardation, and flow, were separately demonstrated by imaging a horseshoe crab lateral compound eye, a non-uniformly heated chicken muscle, and a microfluidic device. A mouse brain with thin skull preparation was imaged in vivo and demonstrated the capability of the system for live multi-functional OCT visualization.

Logarithmic intensity and speckle-based motion contrast methods for human retinal vasculature visualization using swept source optical coherence tomography

We formulate a theory to show that the statistics of OCT signal amplitude and intensity are highly dependent on the sample reflectivity strength, motion, and noise power. Our theoretical and experimental results depict the lack of speckle amplitude and intensity contrasts to differentiate regions of motion from static areas. Two logarithmic intensity-based contrasts, logarithmic intensity variance (LOGIV) and differential logarithmic intensity variance (DLOGIV), are proposed for serving as surrogate markers for motion with enhanced sensitivity. Our findings demonstrate a good agreement between the theoretical and experimental results for logarithmic intensity-based contrasts. Logarithmic intensity-based motion and speckle-based contrast methods are validated and compared for in vivo human retinal vasculature visualization using high-speed swept-source optical coherence tomography (SS-OCT) at 1060 nm. The vasculature was identified as regions of motion by creating LOGIV and DLOGIV tomograms: multiple B-scans were collected of individual slices through the retina and the variance of logarithmic intensities and differences of logarithmic intensities were calculated. Both methods captured the small vessels and the meshwork of capillaries associated with the inner retina in en face images over 4 mm2 in a normal subject.

In vivo imaging of the microcirculation of the volar forearm using correlation mapping optical coherence tomography (cmOCT)

Correlation mapping optical coherence tomography (cmOCT) is a recently proposed technique that extends the capabilities of OCT to enable mapping of vasculature networks. The technique is achieved as a processing step on OCT intensity images that does not require any modification to existing OCT hardware. In this paper we apply the cmOCT processing technique to in vivo human imaging of the volar forearm. We illustrate that cmOCT can produce maps of the microcirculation that clearly follow the accepted anatomical structure. We demonstrate that the technique can extract parameters such as capillary density and vessel diameter. These parameters are key clinical markers for the early changes associated with microvascular diseases. Overall the presented results show that cmOCT is a powerful new tool that generates microcirculation maps in a safe non-invasive, non-contact technique which has clear clinical applications.