Investigation of optical attenuation imaging using optical coherence tomography for monitoring of scars undergoing fractional laser treatment

We demonstrate the use of the near‐infrared attenuation coefficient, measured using optical coherence tomography (OCT), in longitudinal assessment of hypertrophic burn scars undergoing fractional laser treatment. The measurement method incorporates blood vessel detection by speckle decorrelation and masking, and a robust regression estimator to produce 2D en face parametric images of the attenuation coefficient of the dermis. Through reliable co‐location of the field of view across pre‐ and post‐treatment imaging sessions, the study was able to quantify changes in the attenuation coefficient of the dermis over a period of ～20 weeks in seven patients. Minimal variation was observed in the mean attenuation coefficient of normal skin and control (untreated) mature scars, as expected. However, a significant decrease (13 ± 5%, mean ± standard deviation) was observed in the treated mature scars, resulting in a greater distinction from normal skin in response to localized damage from the laser treatment. By contrast, we observed an increase in the mean attenuation coefficient of treated (31 ± 27%) and control (27 ± 20%) immature scars, with numerical values incrementally approaching normal skin as the healing progressed. This pilot study supports conducting a more extensive investigation of OCT attenuation imaging for quantitative longitudinal monitoring of scars.

En face 2D OCT attenuation coefficient map of a treated immature scar derived from the pre‐treatment (top) and the post‐treatment (bottom) scans. (Vasculature (black) is masked out.) The scale bars are 0.5 mm.     

Lymph vessels visualization from optical coherence tomography data using depth-resolved attenuation coefficient calculation

Multimodal optical coherent tomography grows popularity with researchers and clinicians over the past decade. One of the modalities is lymphangiography, which allows visualization of the lymphatic vessel networks within optical coherence tomography (OCT) imaging volume. In the present study, it is shown that lymphatic vessel visualization obtained from the depth-resolved attenuation coefficient distributions, corrected for the noise, shows improved contrast and detail in comparison with previously proposed approaches. We also argue that the two most popular approaches for lymphatic vessel visualization, namely simple intensity thresholding and vesselness calculation based on local Hessian matrix eigenvalues, imply different definitions of the lymphatic vessel's appearance in the OCT volume and lead to the different networks.     

N2NSR-OCT: Simultaneous denoising and super-resolution in optical coherence tomography images using semisupervised deep learning

Optical coherence tomography (OCT) imaging shows a significant potential in clinical routines due to its noninvasive property. However, the quality of OCT images is generally limited by inherent speckle noise of OCT imaging and low sampling rate. To obtain high signal-to-noise ratio (SNR) and high-resolution (HR) OCT images within a short scanning time, we presented a learning-based method to recover high-quality OCT images from noisy and low-resolution OCT images. We proposed a semisupervised learning approach named N2NSR-OCT, to generate denoised and super-resolved OCT images simultaneously using up- and down-sampling networks (U-Net (Semi) and DBPN (Semi)). Additionally, two different super-resolution and denoising models with different upscale factors (2× and 4× ) were trained to recover the high-quality OCT image of the corresponding down-sampling rates. The new semisupervised learning approach is able to achieve results comparable with those of supervised learning using up- and down-sampling networks, and can produce better performance than other related state-of-the-art methods in the aspects of maintaining subtle fine retinal structures.     

Preclinical mouse model of optical coherence tomography for subcortical brain imaging without dissection

The purpose of this study was to investigate the feasibility of using optical coherence tomography (OCT) to identify internal brain lesions, specifically intracerebral hemorrhage, without dissection. Mice with artificially injected brain hematomas were used to test the OCT system, and the recorded images were compared with microscopic images of the same mouse brains after hematoxylin and eosin staining. The intracranial structures surrounding the hematomas were clearly visualized by the OCT system without dissection. These images reflect the ability of OCT to determine the extent of a lesion in several planes. OCT is a useful technology, and these findings could be used as a starting point for future research in intraoperative imaging.     

Comparative study of deep neural networks with unsupervised Noise2Noise strategy for noise reduction of optical coherence tomography images

As a powerful diagnostic tool, optical coherence tomography (OCT) has been widely used in various clinical setting. However, OCT images are susceptible to inherent speckle noise that may contaminate subtle structure information, due to low-coherence interferometric imaging procedure. Many supervised learning-based models have achieved impressive performance in reducing speckle noise of OCT images trained with a large number of noisy-clean paired OCT images, which are not commonly feasible in clinical practice. In this article, we conducted a comparative study to investigate the denoising performance of OCT images over different deep neural networks through an unsupervised Noise2Noise (N2N) strategy, which only trained with noisy OCT samples. Four representative network architectures including U-shaped model, multi-information stream model, straight-information stream model and GAN-based model were investigated on an OCT image dataset acquired from healthy human eyes. The results demonstrated all four unsupervised N2N models offered denoised OCT images with a performance comparable with that of supervised learning models, illustrating the effectiveness of unsupervised N2N models in denoising OCT images. Furthermore, U-shaped models and GAN-based models using UNet network as generator are two preferred and suitable architectures for reducing speckle noise of OCT images and preserving fine structure information of retinal layers under unsupervised N2N circumstances.     

Three‐dimensional mapping of the attenuation coefficient in optical coherence tomography to enhance breast tissue microarchitecture contrast

Effective intraoperative tumor margin assessment is needed to reduce re‐excision rates in breast‐conserving surgery (BCS). Mapping the attenuation coefficient in optical coherence tomography (OCT) throughout a sample to create an image (attenuation imaging) is one promising approach. For the first time, three‐dimensional OCT attenuation imaging of human breast tissue microarchitecture using a wide‐field (up to ~45 × 45 × 3.5 mm) imaging system is demonstrated. Representative results from three mastectomy and one BCS specimen (from 31 specimens) are presented with co‐registered postoperative histology. Attenuation imaging is shown to provide substantially improved contrast over OCT, delineating nuanced features within tumors (including necrosis and variations in tumor cell density and growth patterns) and benign features (such as sclerosing adenosis). Additionally, quantitative micro‐elastography (QME) images presented alongside OCT and attenuation images show that these techniques provide complementary contrast, suggesting that multimodal imaging could increase tissue identification accuracy and potentially improve tumor margin assessment.     

Characterizing thrombus with multiple red blood cell compositions by optical coherence tomography attenuation coefficient

Embolectomy is one of the emergency procedures performed to remove emboli. Assessing the composition of human blood clots is an important diagnostic factor and could provide guidance for an appropriate treatment strategy for interventional physicians. Immunostaining has been used to identity compositions of clots as a gold-standard procedure, but it is time-consuming and cannot be performed in situ. Here, we proposed that the optical attenuation coefficient of optical coherence tomography (OCT) can be a reliable indicator as a new imaging modality to differentiate clot compositions. Fifteen human blood clots with multiple red blood cell (RBC) compositions from 21% to 95% were prepared using healthy human whole blood. A homogeneous gelatin phantom experiment and numerical simulation based on the Lambert–Beer's law were examined to verify the validity of the attenuation coefficient estimation. The results displayed that optical attenuation coefficients were strongly correlated with RBC compositions. We reported that attenuation coefficients could be a promising biomarker to guide the choice of an appropriate interventional device in a clinical setting and assist in characterizing blood clots.     

Phase unwrapping for Doppler spectral domain optical coherence tomography flow measurement

Doppler optical coherence tomography (OCT) offers additional flow velocity information, which extends the application of OCT. Phase wrapping is the inherent problem that limits measureable range of Doppler OCT. We propose a phase unwrapping method which is suitable for correcting phase in Doppler OCT images. Points (pixels) in flow region are divided into groups according to the radial distance. Points in the same group are supposed to have close velocity. Phase unwrapping algorithm begins at the boundary layer group and is performed sequentially toward the center. Using the proposed criterion, points in a group are separated into two categories, signal points and noise points. Wrapping rounds are determined for signal points phase unwrapping. Mean value of the corrected signal points replaces the noise points for noise reduction. The method is validated with capillary tube flow phantom and in vivo blood flow.     

Optical coherence tomography image denoising using a generative adversarial network with speckle modulation

Optical coherence tomography (OCT) is widely used for biomedical imaging and clinical diagnosis. However, speckle noise is a key factor affecting OCT image quality. Here, we developed a custom generative adversarial network (GAN) to denoise OCT images. A speckle‐modulating OCT (SM‐OCT) was built to generate low speckle images to be used as the ground truth. In total, 210 000 SM‐OCT images were used for training and validating the neural network model, which we call SM‐GAN. The performance of the SM‐GAN method was further demonstrated using online benchmark retinal images, 3D OCT images acquired from human fingers and OCT videos of a beating fruit fly heart. The denoise performance of the SM‐GAN model was compared to traditional OCT denoising methods and other state‐of‐the‐art deep learning based denoise networks. We conclude that the SM‐GAN model presented here can effectively reduce speckle noise in OCT images and videos while maintaining spatial and temporal resolutions.     

Cross‐scanning optical coherence tomography angiography for eye motion correction

We propose a cross‐scanning optical coherence tomography (CS‐OCT) system to correct eye motion artifacts in OCT angiography images. This system employs a dual‐illumination configuration with two orthogonally polarized beams, each of which simultaneously perform raster scanning in perpendicular direction with each other over the same area. In the reference arm, a polarization delay unit is used to acquire the two orthogonally polarized interferograms with a single photo detector by introducing different optical delay lines. The two cross‐scanned volume data are affected by the same eye motion but in two orthogonal directions. We developed a motion correction algorithm, which removes artifacts in the slow axis of each angiogram using the other and merges them through a nonrigid registration algorithm. In this manner, we obtained a motion‐corrected angiogram within a single volume scanning time without additional eye‐tracking devices.     

General model for depth‐resolved estimation of the optical attenuation coefficients in optical coherence tomography

We present the proof of concept of a general model that uses the tissue sample transmittance as input to estimate the depth‐resolved attenuation coefficient of tissue samples using optical coherence tomography (OCT). This method allows us to obtain an image of tissue optical properties instead of intensity contrast, guiding diagnosis and tissues differentiation, extending its application from thick to thin samples. The performance of our method was simulated and tested with the assistance of a home built single‐layered and multilayered phantoms (~100 μm each layer) with known attenuation coefficient on the range of 0.9 to 2.32 mm^?1. It is shown that the estimated depth‐resolved attenuation coefficient recovers the reference values, measured by using an integrating sphere followed by the inverse adding doubling processing technique. That was corroborated for all situations when the correct transmittance value is used with an average difference of 7%. Finally, we applied the proposed method to estimate the depth‐resolved attenuation coefficient for a thin biological sample, demonstrating the ability of our method on real OCT images.     

One‐to‐one registration of en‐face optical coherence tomography attenuation coefficients with histology of a prostatectomy specimen

Optical coherence tomography (OCT), enables high‐resolution 3D imaging of the morphology of light scattering tissues. From the OCT signal, parameters can be extracted and related to tissue structures. One of the quantitative parameters is the attenuation coefficient; the rate at which the intensity of detected light decays in depth. To couple the quantitative parameters with the histology one‐to‐one registration is needed. The primary aim of this study is to validate a registration method of quantitative OCT parameters to histological tissue outcome through one‐to‐one registration of OCT with histology. We matched OCT images of unstained fixated prostate tissue slices with corresponding histology slides, wherein different histologic types were demarcated. Attenuation coefficients were determined by a supervised automated exponential fit (corrected for point spread function and sensitivity roll‐off related signal losses) over a depth of 0.32 mm starting from 0.10 mm below the automatically detected tissue edge. Finally, the attenuation coefficients corresponding to the different tissue types of the prostate were compared. From the attenuation coefficients, we produced the squared relative residue and goodness‐of‐fit metric R². This article explains the method to perform supervised automated quantitative analysis of OCT data, and the one‐to‐one registration of OCT extracted quantitative data with histopathological outcomes.      

En‐face optical coherence tomography for the detection of cancer in prostatectomy specimens: Quantitative analysis in 20 patients

The increase histopathological evaluation of prostatectomy specimens rises the workload on pathologists. Automated histopathology systems, preferably directly on unstained specimens, would accelerate the pathology workflow. In this study, we investigate the potential of quantitative analysis of optical coherence tomography (OCT) to separate benign from malignant prostate tissue automatically. Twenty fixated prostates were cut, from which 54 slices were scanned by OCT. Quantitative OCT metrics (attenuation coefficient, residue, goodness‐of‐fit) were compared for different tissue types, annotated on the histology slides. To avoid misclassification, the poor‐quality slides, and edges of annotations were excluded. Accurate registration of OCT data with histology was achieved in 31 slices. After removing outliers, 56% of the OCT data was compared with histopathology. The quantitative data could not separate malignant from benign tissue. Logistic regression resulted in malignant detection with a sensitivity of 0.80 and a specificity of 0.34. Quantitative OCT analysis should be improved before clinical use.     

Detecting mouse squamous cell carcinoma from submicron full-field optical coherence tomography images by deep learning

The standard medical practice for cancer diagnosis requires histopathology, which is an invasive and time-consuming procedure. Optical coherence tomography (OCT) is an alternative that is relatively fast, noninvasive, and able to capture three-dimensional structures of epithelial tissue. Unlike most previous OCT systems, which cannot capture crucial cellular-level information for squamous cell carcinoma (SCC) diagnosis, the full-field OCT (FF-OCT) technology used in this paper is able to produce images at sub-micron resolution and thereby facilitates the development of a deep learning algorithm for SCC detection. Experimental results show that the SCC detection algorithm can achieve a classification accuracy of 80% for mouse skin. Using the sub-micron FF-OCT imaging system, the proposed SCC detection algorithm has the potential for in-vivo applications.     

Comparison of the Iowa Reference Algorithm to the Heidelberg Spectralis optical coherence tomography segmentation algorithm

For spectral‐domain optical coherence tomography (SD‐OCT) studies of neurodegeneration, it is important to understand how segmentation algorithms differ in retinal layer thickness measurements, segmentation error locations and the impact of manual correction. Using macular SD‐OCT images of frontotemporal degeneration patients and controls, we compare the individual and aggregate retinal layer thickness measurements provided by two commonly used algorithms, the Iowa Reference Algorithm and Heidelberg Spectralis, with manual correction of significant segmentation errors. We demonstrate small differences of most retinal layer thickness measurements between these algorithms. Outer sectors of the Early Treatment Diabetic Retinopathy Study grid require a greater percent of eyes to be corrected than inner sectors of the retinal nerve fiber layer (RNFL). Manual corrections affect thickness measurements mildly, resulting in at most a 5% change in RNFL thickness. Our findings can inform researchers how to best use different segmentation algorithms when comparing retinal layer thicknesses.     

Investigating the healing mechanisms of an angiogenesis‐promoting topical treatment for diabetic wounds using multimodal microscopy

Impaired skin wound healing is a significant comorbid condition of diabetes that is caused by poor microcirculation, among other factors. Studies have shown that angiogenesis, a critical step in the wound healing process in diabetic wounds, can be promoted under hypoxia. In this study, an angiogenesis‐promoting topical treatment for diabetic wounds, which promotes angiogenesis by mimicking a hypoxic environment via inhibition of prolyl hydroxylase resulting in elevation or maintenance of hypoxia‐inducible factor, was investigated utilizing a custom‐built multimodal microscopy system equipped with phase‐variance optical coherence tomography (PV‐OCT) and fluorescence lifetime imaging microscopy (FLIM). PV‐OCT was used to track the regeneration of the microvasculature network, and FLIM was used to assess the in vivo metabolic response of mouse epidermal keratinocytes to the treatment during healing. Results show a significant decrease in the fluorescence lifetime of intracellular reduced nicotinamide adenine dinucleotide, suggesting a hypoxic‐like environment in the wounded skin, followed by a quantitative increase in blood vessel density assessed by PV‐OCT. Insights gained in these studies could lead to new endpoints for evaluation of the efficacy and healing mechanisms of wound‐healing drugs in a setting where delayed healing does not permit available methods for evaluation to take place.      

Cross-domain retinopathy classification with optical coherence tomography images via a novel deep domain adaptation method

Deep learning based retinopathy classification with optical coherence tomography (OCT) images has recently attracted great attention. However, existing deep learning methods fail to work well when training and testing datasets are different due to the general issue of domain shift between datasets caused by different collection devices, subjects, imaging parameters, etc. To address this practical and challenging issue, we propose a novel deep domain adaptation (DDA) method to train a model on a labeled dataset and adapt it to an unlabelled dataset (collected under different conditions). It consists of two modules for domain alignment, that is, adversarial learning and entropy minimization. We conduct extensive experiments on three public datasets to evaluate the performance of the proposed method. The results indicate that there are large domain shifts between datasets, resulting a poor performance for conventional deep learning methods. The proposed DDA method can significantly outperform existing methods for retinopathy classification with OCT images. It achieves retinopathy classification accuracies of 0.915, 0.959 and 0.990 under three cross-domain (cross-dataset) scenarios. Moreover, it obtains a comparable performance with human experts on a dataset where no labeled data in this dataset have been used to train the proposed DDA method. We have also visualized the learnt features by using the t-distributed stochastic neighbor embedding (t-SNE) technique. The results demonstrate that the proposed method can learn discriminative features for retinopathy classification.     

Long‐term and in vivo assessment of Aβ protein‐induced brain atrophy in a zebrafish model by optical coherence tomography

In this study, a neurotoxicity model of zebrafish induced by amyloid beta (Aβ) protein was developed and evaluated in vivo by optical coherence tomography (OCT). Aβ protein and phosphate buffer saline (PBS) were separately injected into the head of two groups of adult zebrafish (n = 6 per group). Congo‐red staining results confirmed that Aβ protein had penetrated into brain tissue. All zebrafish were imaged with OCT on the 0th, 5th, 10th, 15th and 20th day postinjection. OCT images showed that PBS is not toxic to brain tissue. However, significant brain atrophy could be seen in the OCT images of zebrafish injected with Aβ‐protein that was verified by histological consequences. In addition, zebrafish in the model group showed memory decline in behavioral tests. This study verified the feasibility of in vivo long‐term assessment of Aβ protein‐induced brain atrophy in adult zebrafish by OCT that has great potential to be applied in the neurological diseases research.     

Evaluation of dental enamel caries assessment using Quantitative Light Induced Fluorescence and Optical Coherence Tomography

An in vitro study of morphological alterations between sound dental structure and artificially induced white spot lesions in human teeth, was performed through the loss of fluorescence by Quantitative Light‐Induced Fluorescence (QLF) and the alterations of the light attenuation coefficient by Optical Coherence Tomography (OCT). To analyze the OCT images using a commercially available system, a special algorithm was applied, whereas the QLF images were analyzed using the software available in the commercial system employed. When analyzing the sound region against white spot lesions region by QLF, a reduction in the fluorescence intensity was observed, whilst an increase of light attenuation by the OCT system occurred. Comparison of the percentage of alteration between optical properties of sound and artificial enamel caries regions showed that OCT processed images through the attenuation of light enhanced the tooth optical alterations more than fluorescence detected by QLF System.

QLF versus OCT imaging of enamel caries: a photonics assessment     

Orthogonal-polarization-gating optical coherence tomography for human sweat ducts in vivo

We propose an orthogonal-polarization-gating optical coherence tomography (OPG-OCT) for human sweat ducts in vivo. OPG-OCT is composed of the orthogonal linearly polarized light of a sample arm individually interfering with orthogonal linearly polarized lights of the reference arms, where OPG-OCT induces two images, one reflecting the projection intensity and the other the horizontal linear diattenuation (HLD). The results demonstrate that OPG-OCT projection intensity could improve the image quality of sweat ducts. HLD also clearly illustrates the spiral shape of the sweat ducts. Finally, sweat ducts in intensity image are segmented by employing convolutional neural networks (CNN). The proportions of left-handed and right-handed ducts are extracted to characterize the sweat ducts based on HLD. Therefore, the OPG-OCT technique employing CNN for the human sweat glands has the potential to automatically identify the human sweat ducts in vivo.