Improving cerebral microvascular image quality of optical coherence tomography angiography with deep learning-based segmentation

Optical coherence tomography angiography (OCTA) can map the microvascular networks of the cerebral cortices with micrometer resolution and millimeter penetration. However, the high scattering of the skull and the strong noise in the deep imaging region will distort the vasculature projections and decrease the OCTA image quality. Here, we proposed a deep learning-based segmentation method based on a U-Net convolutional neural network to extract the cortical region from the OCT image. The vascular networks were then visualized by three OCTA algorithms. The image quality of the vasculature projections was assessed by two metrics, including the peak signal-to-noise ratio (PSNR) and the contrast-to-noise ratio (CNR). The results show the accuracy of the cortical segmentation was 96.07%. The PSNR and CNR values increased significantly in the projections of the selected cortical regions. The OCTA incorporating the deep learning-based cortical segmentation can efficiently improve the image quality and enhance the vasculature clarity.     

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.     

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.     

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.     

Deep-learning-based motion correction in optical coherence tomography angiography

Optical coherence tomography angiography (OCTA) is a widely applied tool to image microvascular networks with high spatial resolution and sensitivity. Due to limited imaging speed, the artifacts caused by tissue motion can severely compromise visualization of the microvascular networks and quantification of OCTA images. In this article, we propose a deep-learning-based framework to effectively correct motion artifacts and retrieve microvascular architectures. This method comprised two deep neural networks in which the first subnet was applied to distinguish motion corrupted B-scan images from a volumetric dataset. Based on the classification results, the artifacts could be removed from the en face maximum-intensity-projection (MIP) OCTA image. To restore the disturbed vasculature induced by artifact removal, the second subnet, an inpainting neural network, was utilized to reconnect the broken vascular networks. We applied the method to postprocess OCTA images of the microvascular networks in mouse cortex in vivo. Both image comparison and quantitative analysis show that the proposed method can significantly improve OCTA image by efficiently recovering microvasculature from the overwhelming motion artifacts.     

Optical coherence tomography angiography for mapping cerebral microvasculature based on normalized differentiation analysis

Optical coherence tomography angiography (OCTA) is a label‐free, noninvasive biomedical imaging modality for mapping microvascular networks and quantifying blood flow velocities in vivo. Simple computation and fast processing are critical for the OCTA in some applications. Herein, we report on a normalized differentiation method for mapping cerebral microvasculature with the advantages of simple analysis and high image quality, benefitting from computation of differentiation and characteristics of normalization. Normalized differentiation values are validated to have a nearly linear relationship with flow velocities in a range using a flow phantom. The measurements in a rat cerebral cortex show that the OCTA based on the normalized differentiation analysis can generate microvascular images with high quality and monitor spatiotemporal dynamics of blood flow with simple computation and fast processing before and after localized ischemia induced by arterial occlusion.     

Interplane bulk motion analysis and removal based on normalized cross‐correlation in optical coherence tomography angiography

Bulk motion seriously degrades the image quality of optical coherence tomography angiography (OCTA). Conventional correction methods focus on in‐plane displacement, while the bulk motion component perpendicular to B‐scans also introduces noise. This work first presents an evaluation of this component using a specific scan protocol and an approximate expression derived from peak‐normalized cross‐correlation values, and then quantitatively assesses how interplane bulk motion noise reduce the sensitivity of cross‐sectional angiograms. Finally, we developed a repetitive bulk motion correction method based on the estimated displacements and redundant volume scans. The correction does not require registration and angiogram reconstruction of low flow sensitivity frames, and the results of in vivo mice skin OCTA imaging experiments show that the proposed method can effectively reduce bulk motion noise caused by cardiac and respiratory motion and occasional shaking, and improve OCTA image quality, which has practical significance for clinical OCTA diagnosis and analysis.     

A novel automatic 3D stitching algorithm for optical coherence tomography angiography and its application in dermatology

Optical coherence tomography (OCT) and OCT angiography (OCTA) techniques offer numerous advantages in clinical skin applications but the field of view (FOV) of current commercial systems are relatively limited to cover the entire skin lesion. The typical method to expand the FOV is to apply wide field objective lens. However, lateral resolution is often sacrificed when scanning with these lenses. To overcome this drawback, we developed an automated 3D stitching method for creating high-resolution skin structure and vascular volumes with large field of view, which was realized by montaging multiple adjacent OCT and OCTA volumes. The proposed stitching method is demonstrated by montaging 3 × 3 OCT and OCTA volumes (nine OCT/OCTA volumes as one data set with each volume covers 2.5 cm × 2.5 cm area) of healthy thin and thick skin from six volunteers. The proposed stitching protocol achieves high flexibility and repeatable for all the participants. Moreover, according to evaluation of structural similarity index and feature similarity index, our proposed stitched result has a superior similarity to single scanning protocol in large-scaled. We had also verified its improved performance through assessing metrics of vessel contrast-noise-ratio (CNR) from 2.07 ± 0.44 (single large-scaled scanning protocol) to 3.05 ± 0.51 (proposed 3 × 3 sub-volume stitching method).     

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.     

A feasibility study of OCT for anatomical and vascular phenotyping of mouse embryo

The embryo phenotyping of genetic murine model is invaluable when investigating functions of genes underlying embryonic development and birth defect. Although traditional imaging technologies such as ultrasound are very useful for evaluating phenotype of murine embryos, the use of advanced techniques for phenotyping is desirable to obtain more information from genetic research. This letter tests the feasibility of optical coherence tomography (OCT) as a high‐throughput phenotyping tool for murine embryos. Three‐dimensional OCT imaging is performed for live and cleared mouse embryos in the late developmental stage (embryonic day 17.5). By using a dynamic focusing method and OCT angiography (OCTA) approach, our OCT imaging of the embryo exhibits rapid and clean visualization of organ structures deeper than 5 mm and complex microvasculature of perfused blood vessels in the murine embryonic body. This demonstration suggests that OCT imaging can be useful for comprehensively assessing embryo anatomy and angiography of genetically engineered mice.     

Noninvasive multimodal imaging by integrating optical coherence tomography with autofluorescence imaging for dental applications

We report the development of an integrated multifunctional imaging system capable of providing anatomical (optical coherence tomography, OCT), functional (OCT angiography, OCTA) and molecular imaging (light‐induced autofluorescence, LIAF) for in vivo dental applications. Blue excitation light (405 nm) was used for LIAF imaging, while the OCT was powered by a 1310 nm swept laser source. A red‐green‐blue digital camera, with a 450 nm cut‐on broadband optical filter, was used for LIAF detection. The exciting light source and camera were integrated directly with the OCT scanning probe. The integrated system used two noninvasive imaging modalities to improve the speed of in vivo OCT data collection and to better target the regions of interest. The newly designed system maintained the ability to detect differences between healthy and hypomineralized teeth, identify dental biofilm and visualize the microvasculature of gingival tissue. The development of the integrated OCT‐LIAF system provides an opportunity to conduct clinical studies more efficiently, examining changes in oral conditions over time.     

Towards quantitative assessment of burn based on photoacoustic and optical coherence tomography

Accurate and timely assessment of the severity of burn is essential for the treatment of burns. Currently, although most first‐degree and third‐degree burns are easily diagnosed through visual inspection or auxiliary diagnostic methods, the second‐degree burn is still difficult to distinguish due to the ambiguity boundaries of second‐degree with first‐degree and third‐degree burns. In this study, we proposed a non‐invasive technique by combing photoacoustic imaging (PAI) and optical coherence tomography (OCT) to multi‐parameter quantitatively assess the burns. The feasibility and capacity of the dual‐mode PAT/OCT for assessing the burns was first testified by tissue‐mimicking phantom and burn wounds in mouse pinna in vivo. The further experiments conducted on the back of rats showed that the changes in skin scattering structure, vascular morphology and blood flow provided by the dual‐mode PAI/OCT system can determine distinct boundaries and depth of the burns. The experimental results prove that combined PAI/OCT as a novel method can be used to assess the severity of burn, which has the potential to diagnose the burns in clinic.     

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.     

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.     

Vascular changes precede tomographic changes in diabetic eyes without retinopathy and improve artificial intelligence diagnostics

The purpose of this study was to evaluate early vascular and tomographic changes in the retina of diabetic patients using artificial intelligence (AI). The study included 74 age‐matched normal eyes, 171 diabetic eyes without retinopathy (DWR) eyes and 69 mild non‐proliferative diabetic retinopathy (NPDR) eyes. All patients underwent optical coherence tomography angiography (OCTA) imaging. Tomographic features (thickness and volume) were derived from the OCTA B‐scans. These features were used in AI models. Both OCT and OCTA features showed significant differences between the groups (P < .05). However, the OCTA features indicated early retinal changes in DWR eyes better than OCT (P < .05). In the AI model using both OCT and OCTA features simultaneously, the best area under the curve of 0.91 ± 0.02 was obtained (P < .05). Thus, the combined use of AI, OCT and OCTA significantly improved the early diagnosis of diabetic changes in the retina.     

The use of intensity‐based Doppler variance method for single vessel response to functional neurovascular activation

This study presents 1 use of optical coherence tomography (OCT) angiography technique to examine neurovascular coupling effect. Repeated B‐scans OCT recording is performed on the rat somatosensory cortex with cranial window preparation while its contralateral forepaw is electrically stimulated to activate the neurons in rest. We use an intensity‐based Doppler variance (IBDV) algorithm mapped cerebral blood vessels in the cortex, and the temporal alteration in blood perfusion during neurovascular activation is analyzed using the proposed IBDV quantitative parameters. By using principal component analysis‐based Fuzzy C Means clustering method, the stimulus‐evoked vasomotion patterns were classified into 3 categories. We found that the response time of small vessels (resting diameter 14.9 ±6.6 μm), middle vessels (resting diameter 21.1 ±7.9 μm) and large vessels (resting diameter 50.7 ±6.5 μm) to achieve 5% change of vascular dilation after stimulation was 1.5, 2 and 5.5 seconds, respectively. Approximately 5% peak change of relative blood flow (RBF) in both small and middle vessels was observed. The large vessels react slowly and their responses nearly 4 seconds delayed, but no significant change in RBF of the large vessels was seen.      

Jones matrix‐based speckle‐decorrelation angiography using polarization‐sensitive optical coherence tomography

We show that polarization‐sensitive optical coherence tomography angiography (PS‐OCTA) based on full Jones matrix assessment of speckle decorrelation offers improved contrast and depth of vessel imaging over conventional OCTA. We determine how best to combine the individual Jones matrix elements and compare the resulting image quality to that of a conventional OCT scanner by co‐locating and imaging the same skin locations with closely matched scanning setups. Vessel projection images from finger and forearm skin demonstrate the benefits of Jones matrix‐based PS‐OCTA. Our study provides a promising starting point and a useful reference for future pre‐clinical and clinical applications of Jones matrix‐based PS‐OCTA.     

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.     

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.     

Contrast‐enhanced optical coherence tomography for melanoma detection: An in vitro study

Optical coherence tomography (OCT), with a high‐spatial resolution (<10 microns), intermediate penetration depth (~1.5 mm) and volumetric imaging capability is a great candidate to be used as a diagnostic‐assistant modality in dermatology. At this time, the accuracy of OCT for melanoma detection is lower than anticipated. In this letter, we studied for the first time, the use of a novel contrast agent consist of ultra‐small nanoparticles conjugated to a melanoma biomarker to improve the accuracy of OCT for differentiation of melanoma cells from nonmelanoma cells, in vitro. We call this approach SMall nanoparticle Aggregation‐enhanced Radiomics of Tumor (SMART)‐OCT imaging. This initial proof of concept study is the first step toward the broad utilization of this method for high accuracy all types of tumor detection applications.