On Optical Coherence Tomography System Research Articles

Ocular Pigmentation Impact on Retinal Versus Choroidal Optical Coherence Tomography Imaging in Preterm Infants.

To evaluate the association of fundus pigmentation with the visibility of retinal versus choroidal layers on optical coherence tomography (OCT) in preterm infants. For infants enrolled in BabySTEPS, ophthalmologists recorded fundus pigmentation (blond, medium, or dark) at the first retinopathy of prematurity (ROP) examination. Bedside OCT imaging was performed at each examination, and a masked grader evaluated all OCT scans from both eyes of each infant for visibility (yes/no) of all retinal layers and of the chorio-scleral junction (CSJ). Multivariable logistic regression was used to assess associations between fundus pigmentation and visibility of all retinal layers and CSJ, controlling for potential confounders (i.e., birth weight, gestational age, sex, OCT system, pupil size, and postmenstrual age at imaging). Among 114 infants (mean birth weight, 943 grams; mean gestational age, 27.6 weeks), 43 infants (38%) had blond, 56 infants (49%) had medium, and 15 infants (13%) had dark fundus pigmentation. Of 1042 scans, all retinal layers were visible in 977 (94%) and CSJ in 895 (86%). Pigmentation was not associated with retinal layer visibility (P = 0.49), but medium and dark pigmentation were associated with decreased CSJ visibility (medium: odds ratio [OR] = 0.34, P = 0.001; dark: OR = 0.24, P = 0.009). For infants with dark pigmentation, retinal layer visibility increased (OR = 1.87 per week; P ≤ 0.001) and CSJ visibility decreased (OR = 0.78 per week; P = 0.01) with increasing age. Although fundus pigmentation was not associated with the visibility of all retinal layers on OCT, darker pigmentation decreased CSJ visibility, and this effect increased with age. The ability of bedside OCT to capture retinal layer microanatomy in preterm infants, regardless of fundus pigmentation, may represent an advantage over fundus photography for ROP telemedicine.

Approach for advanced working distance monitoring and control capability in laser metal deposition processing for additive manufacturing

The working distance is an essential parameter of the laser metal deposition (LMD) process which has to be kept at a constant level for successful applications. Especially when applying LMD in additive manufacturing (AM) to generate large-scale components, distance control is crucial as these parts can take multiple hours to complete. If over time the working distance runs out of the processing limits, the process efficiency will decrease, and this can lead to a process break down destroying the component build. The development approach is to integrate an enhanced distance measurement system based on optical coherence tomography (OCT) into a commercial LMD powder processing head. By deflecting an elliptic measuring track around the processing area, the OCT system can detect the working distance as well as topological information (2D + d) compared to a standard 1D distance measurement (1D + d). By continuously collecting data when moving the processing head, a 3D-scan of the surface can be recorded online. This technology is beneficial for AM as well as LMD repair applications as the system allows the scanning of unknown surfaces prior to LMD processing. In a next development step, the position data of the laser-cell (robot or CNC-Machine) is linked to the 2D + d data gaining full 3D information with high spatial and temporal resolution in real-time. Based on these monitoring capabilities, a closed loop control can be set up by implementing stabilization methods. By adapting LMD process parameters like the powder feed rate or the processing speed, a work distance and LMD track height control can be achieved.

Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images

In the application of deep learning on optical coherence tomography (OCT) data, it is common to train classification networks using 2D images originating from volumetric data. Given the micrometer resolution of OCT systems, consecutive images are often very similar in both visible structures and noise. Thus, an inappropriate data split can result in overlap between the training and testing sets, with a large portion of the literature overlooking this aspect. In this study, the effect of improper dataset splitting on model evaluation is demonstrated for three classification tasks using three OCT open-access datasets extensively used, Kermany’s and Srinivasan’s ophthalmology datasets, and AIIMS breast tissue dataset. Results show that the classification performance is inflated by 0.07 up to 0.43 in terms of Matthews Correlation Coefficient (accuracy: 5% to 30%) for models tested on datasets with improper splitting, highlighting the considerable effect of dataset handling on model evaluation. This study intends to raise awareness on the importance of dataset splitting given the increased research interest in implementing deep learning on OCT data.

Automated detection and classification of counterfeit banknotes using quantitative features captured by spectral-domain optical coherence tomography

Counterfeiting of banknotes is still a severe crime problem in many countries. One of the most significant issue for solving the crime is to classify the counterfeit types and identify the sources. Most of the current methods to classify counterfeit banknotes rely on manual examination that is time-consuming and labor-intensive. Moreover, these methods only detect surface features which can be easily imitated through advanced printing technology. In this study, an automated method based on optical coherence tomography (OCT) and machine-learning algorithms was proposed to classify different types of banknotes based on the internal features. A spectral-domain OCT (SD-OCT) system was employed for sub-surface imaging and quantitative assessment of banknotes. A total of 29 Chinese 100-Yuan banknotes were collected, in which 4 of them were real and 25 of them were counterfeiting by three different printing processes. Each banknote was imaged 10 times in 3 distinct regions, which resulted in a dataset of 290 samples. Each sample was characterized by extracting 2 A-scan (OCT signal intensity along depth) based features and 14B-scan (cross-sectional OCT images) based features. Several machine-learning models, including logistic regression (LR), support vector machines (SVM), K-nearest neighbor (KNN) and random forest (RF), were built and optimized as the classifiers that were trained using 203 samples and applied to predict 87 testing samples. The best performance was achieved by SVM classifier in which the sensitivity of 96.55% and specificity of 98.85% were obtained in discriminating between authentic and counterfeit banknotes, and the sensitivity of 94.67% and specificity of 98.22% were obtained in predicting the types of counterfeit banknotes. These classifiers were also evaluated using the receiver operating characteristic (ROC) curves. To the best of our knowledge, this is the first study where A-scan and B-scan derived features from OCT images have been used for the detection and classification of different types of counterfeit banknotes.

Denoising Performance Evaluation of Automated Age-Related Macular Degeneration Detection on Optical Coherence Tomography Images

Automated detection of eye diseases using artificial intelligence techniques on optical coherence tomography (OCT) images is widely researched in the field of ophthalmology. Such detections are usually performed with the aid of computers. Using high-level simulations, this study investigates and evaluates three automated age-related macular degeneration (AMD) detection flows in terms of computation time and detection accuracy for future hardware-accelerated designs of intelligent and portable OCT systems. In this study, a block-matching and 3-Dimension filter (BM3DF), a hybrid median filter (HMF), and an adaptive wiener filter (AWF) are used to denoise the OCT images. Support vector machine (SVM), AlexNet, GoogLeNet, and Inception-ResNet are employed for AMD detection. Moreover, Local binary patterns, linear configuration patterns, and transfer learning techniques are used to extract image features. Simulation results reveal that machine-learning-based automated AMD detection realizes a high detection accuracy of 95.91% accompanied by low computation time when using the HMF rather than the BM3DF. When considering deep-learning-based automated AMD detection, the combination of HMF and Inception-ResNet achieves the highest detection accuracy of 98.64% but is accompanied by a dramatic increase in computation time. However, only AlexNet achieves a detection accuracy of 96.40%, accompanied by low computation time. In this study, the tradeoffs between the computation time and detection accuracy have been revealed by comparing the denoising methods for the distinct automated AMD detections.

Real-time assessment and characterization of immobilized lipase onto a natural matrix and qualitative reaction kinetic studies using swept-source optical coherence tomography

The current study presents a method based on Optical Coherence Tomography (OCT) for non-destructive, real-time analysis and portrayal of immobilization efficacy for lipase on a natural matrix namely, eggshell. Subsequently, qualitative biochemical reaction kinetics of immobilized lipase was also studied. Successful immobilization of lipase on eggshell was confirmed by the presence of a clear peak in 'A' scan of OCT image. From immobilization kinetics it is clearly observed that the thickness of the highest peak of the A-scan increases significantly and peak intensity saturated after 90min of incubation. Hydrolysis of oil using immobilized lipase indicated that the release of free fatty acids increased up to 8h during reaction and the result was in accordance with the 'B' scan data of the OCT system. Changes in scattering coefficient-based analysis were performed with respect to incubation time to showcase the immobilization process and hydrolysis reaction of lipase. Scanning electron microscope analysis with smoother surface indicated presence of lipase on eggshell matrices, with no further change after oil hydrolysis.

Real-time prediction of quality characteristics in laser beam welding using optical coherence tomography and machine learning

Laser beam welding significantly outperforms conventional joining techniques in terms of flexibility and productivity. The process benefits, in particular, from the highly focused energy and thus from a well-defined heat input. The high intensities of brilliant laser radiation, however, induce very dynamic effects and complex processes within the interaction zone. The high process dynamics require a consistent and reliable quality assurance to ensure the required weld quality. A novel sensor concept for laser material processing based on optical coherence tomography (OCT) was used to measure the capillary depth of the keyhole during deep penetration welding. The OCT measurements were compared with analyses of the surface quality of the weld seams. A machine learning approach could be utilized to reveal correlations between the weld depth signal and the weld seam surface quality, underlining the high level of information contained in the OCT signal about characteristic process phenomena that affect the weld seam quality. Fundamental investigations on aluminum, copper, and galvanized steel were carried out to analyze the structure of the data recorded by the OCT sensor. Based on that, evaluation strategies focusing on quality characteristics were developed and validated to enable a valid interpretation of the OCT signal. The topography of the weld seams was used to classify the surface quality and correlated with the weld depth signal of the OCT system. For this purpose, a preprocessing of the OCT data and a detailed analysis of the topographic information were developed. The processed data were correlated using artificial neural networks. It was shown that by using adequate network structures and training methods, the inline process data of the capillary depth can be used to predict the surface quality with decent prediction accuracy.

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales.

Biofilms are a most successful microbial lifestyle and prevail in a multitude of environmental and engineered settings. Understanding biofilm morphogenesis, that is the structural diversification of biofilms during community assembly, represents a remarkable challenge across spatial and temporal scales. Here, we present an automated biofilm imaging system based on optical coherence tomography (OCT). OCT is an emerging imaging technique in biofilm research. However, the amount of data that currently can be acquired and processed hampers the statistical inference of large scale patterns in biofilm morphology. The automated OCT imaging system allows covering large spatial and extended temporal scales of biofilm growth. It combines a commercially available OCT system with a robotic positioning platform and a suite of software solutions to control the positioning of the OCT scanning probe, as well as the acquisition and processing of 3D biofilm imaging datasets. This setup allows the in situ and non-invasive automatedmonitoring of biofilm development and may be further developed to couple OCT imaging with macrophotography and microsensor profiling.

Wide field of view optical coherence tomography for structural and functional diagnoses in dentistry.

We report herein the fabrication and performance response of a three-dimensional (3-D) intraoral scan probe based on optical coherence tomography (OCT) that enables 3-D structural and functional diagnoses of the human teeth. The OCT system was configured using a swept-source OCT (SS-OCT) with a center wavelength of 1310nm. The scan probe was built using an MEMS mirror and an optical collimator. The implemented SS-OCT equipped with the MEMS-based scan probe yielded an axial resolution of 10 μm and a scan range of 8 × 8 mm2. Two-dimensional (2-D) cross-sectional images of the teeth were acquired by the scan probe based on the OCT. The 3-D volume image was acquired by combining a series of 2-D images, which includes internal and structural information of the human teeth. To utilize the OCT system as an intraoral scanner, partially overlapped 3-D volume images were sequentially acquired and stitched. The 3-D stitching was implemented based on an iterative closest point algorithm. The feasibility of the intraoral scan probe is demonstrated based on its ability to image and characterize the structure and function of the human teeth.

Iterative feedback bio-printing-derived cell-laden hydrogel scaffolds with optimal geometrical fidelity and cellular controllability

For three-dimensional bio-printed cell-laden hydrogel tissue constructs, the well-designed internal porous geometry is tailored to obtain the desired structural and cellular properties. However, significant differences often exist between the designed and as-printed scaffolds because of the inherent characteristics of hydrogels and cells. In this study, an iterative feedback bio-printing (IFBP) approach based on optical coherence tomography (OCT) for the fabrication of cell-laden hydrogel scaffolds with optimal geometrical fidelity and cellular controllability was proposed. A custom-made swept-source OCT (SS-OCT) system was applied to characterize the printed scaffolds quantitatively. Based on the obtained empirical linear formula from the first experimental feedback loop, we defined the most appropriate design constraints and optimized the printing process to improve the geometrical fidelity. The effectiveness of IFBP was verified from the second run using gelatin/alginate hydrogel scaffolds laden with C3A cells. The mismatch of the morphological parameters greatly decreased from 40% to within 7%, which significantly optimized the cell viability, proliferation, and morphology, as well as the representative expression of hepatocyte markers, including CYP3A4 and albumin, of the printed cell-laden hydrogel scaffolds. The demonstrated protocol paves the way for the mass fabrication of cell-laden hydrogel scaffolds, engineered tissues, and scaled-up applications of the 3D bio-printing technique.

Quantitative monitoring of laser-treated engineered skin using optical coherence tomography.

Nowadays, laser therapy is a common method for treating various dermatological troubles such as acne and wrinkles because of its efficient and immediate skin enhancement. Although laser treatment has become a routine procedure in medical and cosmetic fields, the prevention of side-effects, such as hyperpigmentation, redness and burning, still remains a critical issue that needs to be addressed. In order to reduce the side-effects while attaining efficient therapeutic outcomes, it is essential to understand the light-skin interaction through evaluation of physiological changes before and after laser therapy. In this study, we introduce a quantitative tissue monitoring method based on optical coherence tomography (OCT) for the evaluation of tissue regeneration after laser irradiation. To create a skin injury model, we applied a fractional CO2 laser on a customized engineered skin model, which is analogous to human skin in terms of its basic biological function and morphology. The irradiated region in the skin was then imaged by a high-speed OCT system, and its morphologic changes were analyzed by automatic segmentation software. Volumetric OCT images in the laser treated area clearly visualized the wound healing progress at different time points and provided comprehensive information which cannot be acquired through conventional monitoring methods. The results showed that the laser wound in engineered skins was mostly recovered from within 1~2 days with a fast recovery time in the vertical direction. However, the entire recovery period varied widely depending on laser doses and skin type. Our results also indicated that OCT-guided laser therapy would be a very promising protocol for optimizing laser treatment for skin therapy.

Identification of surface defects on glass by parallel spectral domain optical coherence tomography.

Defects can dramatically degrade glass quality, and automatic inspection is a trend of quality control in modern industry. One challenge in inspection in an uncontrolled environment is the misjudgment of fake defects (such as dust particles) as surface defects. Fortunately, optical changes within the periphery of a surface defect are usually introduced while those of a fake defect are not. The existence of changes within the defect peripheries can be adopted as a criterion for defect identification. However, modifications within defect peripheries can be too small to be noticeable in intensity based optical image of the glass surface, and misjudgments of modifications may occur due to the incorrectness in defect demarcation. Thus, a sensitive and reliable method for surface defect identification is demanded. To this end, a nondestructive method based on optical coherence tomography (OCT) is proposed to precisely demarcate surface defects and sensitively measure surface deformations. Suspected surface defects are demarcated using the algorithm based on complex difference from expectation. Modifications within peripheries of suspected surface defects are mapped by phase information from complex interface signal. In this way, surface defects are discriminated from fake defects using a parallel spectral domain OCT (SD-OCT) system. Both simulations and experiments are conducted, and these preliminary results demonstrate the advantage of the proposed method to identify glass surface defects.

Aqueous Cell Differentiation in Anterior Uveitis Using Fourier-Domain Optical Coherence Tomography

The differential diagnosis of a patient presenting with anterior uveitis is broad and can present a diagnostic challenge. In this study, we evaluate the characteristic findings of inflammatory cells on optical coherence tomography (OCT) both in vitro and in vivo. Blood from two healthy volunteers was prepared using standardized methods for cell sorting with a flow cytometer (FASCAria). Neutrophils, lymphocytes, monocytes, and red blood cells were placed in suspension and scanned with a 26-kHz Fourier-domain OCT system (RTVue) with 5-μm axial resolution. Custom software algorithms were used to identify cells based on their reflectance distribution. These algorithms were then applied to OCT images obtained from uveitis patients with active anterior chamber inflammation. On OCT images the cells appeared as hyperreflective spots. In vitro, cell reflectance was statistically significantly different between all of the cell types (neutrophils, monocytes, lymphocytes, and red blood cells, P < 0.001, Mann-Whitney test). In vivo, the relationship between underlying disease and cell type imaged on OCT was highly statistically significant, with human leukocyte antigen (HLA)-B27-associated uveitis patients having a predominantly polymorphonuclear pattern on OCT and sarcoidosis and inflammatory bowel disease patients having a predominantly mononuclear pattern on OCT (P < 0.001, Fisher's exact test). These in vitro and in vivo data demonstrate the potential of OCT to evaluate cells in the anterior chamber of patients noninvasively. Optical coherence tomography may be a useful adjunct to guide the diagnosis and treatment of ocular inflammatory conditions.

Novel approach to characterizing the growth of a fouling layer during membrane filtration via optical coherence tomography.

Fouling control is one of the critical issues in membrane filtration and plays a very important role in water/wastewater treatment. Better understanding of the underlying fouling mechanisms entails novel characterization techniques that can realize a real-time noninvasive observation and provide high resolution images recording the formation of a fouling layer. This work presents a characterization method based on optical coherence tomography (OCT), which is able to detect the internal structures and motions by analyzing the interference signals. An OCT system was incorporated with a laboratory-scale membrane filtration system, and the growth of the fouling layer was observed by using the structural imaging. Taking advantage of the Doppler effects, the OCT-based characterization also provided the velocity profiles of the fluid field, which are of great value in analyzing the formation of the cake layer. The characterization results clearly reveal for the first time the evolution of the morphology of the cake layer under different microhydrodynamic environments. This study demonstrates that OCT-based characterization is a powerful tool for investigating the dynamic processes during membrane fouling.

Wide-Field OCT Angiography at 400 KHz Utilizing Spectral Splitting

Optical angiography systems based on optical coherence tomography (OCT) require dense sampling in order to maintain good vascular contrast. We demonstrate a way to gain acquisition speed and spatial sampling by using spectral splitting with a swept source OCT system. This method splits the recorded spectra into two to several subspectra. Using continuous lateral scanning, the lateral sampling is then increased by the same factor. This allows increasing the field of view of OCT angiography, while keeping the same transverse resolution and measurement time. The performance of our method is demonstrated in vivo at different locations of the human retina and verified quantitatively. Spectral splitting can be applied without any changes in the optical setup, thus offering an easy way to increase the field of view of OCT in general and in particular for OCT angiography.

Improved microcirculation imaging of human skinin vivousing optical microangiography with a correlation mapping mask

Optical microangiography based on optical coherence tomography (OCT) is prone to noise that arises from a static tissue region. Here, we propose a method that can significantly reduce this noise. The method is developed based on an approach that uses the magnitude information of OCT signals to produce tissue microangiograms, especially suitable for the case where a swept-source OCT system is deployed. By combined use of two existing OCT microangiography methods-ultrahigh-sensitive optical microangiography (UHS-OMAG) and correlation mapping OCT (cmOCT)-the final tissue microangiogram is generated by masking UHS-OMAG image using the binary representation of cmOCT image. We find that this process masks the residual static artifacts while preserving the vessel structures. The noise rejection capability of the masked approach (termed as mOMAG) is tested on a tissue-like flow phantom as well as an in vivo human skin tissue. Compared to UHS-OMAG and cmOCT, we demonstrate that the proposed method is capable of achieving improved signal-to-noise ratio in providing microcirculation images. Finally, we show its clinical potential by quantitatively assessing the vascular difference between a burn scar and a normal skin of human subject in vivo.

RPE-Normalized RNFL Attenuation Coefficient Maps Derived from Volumetric OCT Imaging for Glaucoma Assessment

We present spatial retinal nerve fiber layer (RNFL) attenuation coefficient maps for healthy and glaucomatous eyes based on optical coherence tomography (OCT) measurements. Quantitative analyses of differences between healthy and glaucomatous eyes were performed. Peripapillary volumetric images of 10 healthy and 8 glaucomatous eyes were acquired by a Spectralis OCT system. Per A-line, the attenuation coefficient of the RNFL was determined based on a method that uses the retinal pigment epithelium as a reference layer. The attenuation coefficient describes the attenuation of light in tissue due to scattering and absorption. En-face maps were constructed and visually inspected. Differences between healthy and glaucomatous eyes were analyzed (Mann-Whitney U test), both globally (average values) and spatially (concentric and per segment). RNFL attenuation coefficient maps of healthy eyes showed relatively high and uniform values. For glaucomatous eyes, the attenuation coefficients were much lower and showed local defects. Normal and glaucomatous average RNFL attenuation coefficients were highly significantly different (P < 0.0001) and fully separable. The RNFL attenuation coefficient decreased with increasing optic nerve head distance for both groups, with highly significant differences for all distances (P < 0.001). The angular dependency showed high superio- and inferiotemporal and low nasal values, with most significant differences superio- and inferiotemporally. Maps of RNFL attenuation coefficients provide a novel way of assessing the health of the RNFL and are relatively insensitive to imaging artifacts affecting signal intensity. The highly significant difference between normal and glaucomatous eyes suggests using RNFL attenuation coefficient maps as a new clinical tool for diagnosing and monitoring glaucoma.

Clinical optical coherence tomography combined with multiphoton tomography of patients with skin diseases

We report on the first clinical study based on optical coherence tomography (OCT) in combination with multiphoton tomography (MPT) and dermoscopy. 47 patients with a variety of skin diseases and disorders such as skin cancer, psoriasis, hemangioma, connective tissue diseases, pigmented lesions, and autoimmune bullous skin diseases have been investigated with (i) state-of-the-art OCT systems for dermatology including multibeam swept source OCT, (ii) the femtosecond laser multiphoton tomograph, and (iii) dermoscopes. Dermoscopy provides two-dimensional color images of the skin surface. OCT images reflect modifications of the intratissue refractive index whereas MPT is based on nonlinear excitation of endogenous fluorophores and second harmonic generation. A stack of cross-sectional OCT "wide field" images with a typical field of view of 5 x 2 mm(2) gave fast information on the depth and the volume of the lesion. Multiphoton tomography provided 0.36 x 0.36 mm(2) horizontal/diagonal optical sections within seconds of a particular region of interest with superior submicron resolution down to a tissue depth of 200 mum. The combination of OCT and MPT provides a unique powerful optical imaging modality for early detection of skin cancer and other skin diseases as well as for the evaluation of the efficiency of treatments.

Effects of axial resolution improvement on optical coherence tomography (OCT) imaging of gastrointestinal tissues

Optical coherence tomography (OCT) is an emerging medical imaging technology which generates high resolution, cross-sectional images in situ, without the need for excisional biopsy. Previous clinical studies using endoscopic OCT with standard 10-15 microm axial resolution have demonstrated its capability in diagnosing Barrett's esophagus (BE) and high-grade dysplasia (HGD). Quantitative OCT image analysis has shown promise for detecting HGD in Barrett's esophagus patients. We recently developed an endoscopic OCT system with an improved axial resolution of approximately 5 microm. The goal in this manuscript is to compare standard resolution OCT and ultrahigh resolution OCT (UHR-OCT) for image quality and computer-aided detection using normal and Barrett's esophagus. OCT images of gastrointestinal (GI) tissues were obtained using UHR-OCT (5.5 microm) and standard resolution OCT (13 microm). Image quality including the speckle size and sharpness was compared. Texture features of endoscopic OCT images from normal and Barrett's esophagus were extracted using quantitative metrics including spatial frequency analysis and statistical texture analysis. These features were analyzed using principal component analysis (PCA) to reduce the vector dimension and increase the discriminative power, followed by linear discrimination analysis (LDA). UHR-OCT images of GI tissues improved visualization of fine architectural features compared to standard resolution OCT. In addition, the quantitative image feature analysis showed enhanced discrimination of normal and Barrett's esophagus with UHR-OCT. The ability of UHR-OCT to resolve tissue morphology at improved resolution enables visualization of subtle features in OCT images, which may be useful in disease diagnosis. Enhanced classification of image features using UHR-OCT promises to help in the computer-aided diagnosis of GI diseases.

Noninvasive monitoring of glucose concentration with optical coherence tomography

We have proposed a tested in tissue phantoms and in vivo a novel sensor based on optical coherence tomography (OCT) for noninvasive and continuous monitoring of blood glucose concentration. OCT images were obtained from pig and rabbit skin before and after glucose administration. Slopes of OCT signals decreased substantially (~40% in tissues in vivo) and linearly with the increase of blood glucose concentration from 4 to 30 mM, typical for normal and diabetic subjects. Phantom studies demonstrated 1% accuracy of scattering-coefficient measurement. Our theoretical and experimental studies suggest that glucose concentration can potentially be measured noninvasively with high sensitivity and accuracy with OCT systems.