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.     

Quantitative assessment of optical coherence tomography angiography algorithms for neuroimaging

Optical coherence tomography (OCT) angiography can noninvasively map microvascular networks and quantify blood flow in a cerebral cortex with a resolution of 1 to 10 μm and a penetration depth of 2 to 3 mm incorporating OCT signals and angiography algorithms. Different angiography algorithms have been developed in recent years; however, the performance of the algorithms has not been assessed quantitatively for neuroimaging applications. In this paper, we developed four metrics including vascular connectivity, contrast‐to‐noise ratio, signal‐to‐noise ratio and processing time to quantitatively assess the performance of OCT angiography algorithms in image quality and computation speed. After the imaging of a rat cortex using an OCT system, the cerebral microvascular networks were visualized by seven algorithms, and the performance of the algorithms was quantified and compared. Quantitative performance assessment of the algorithms can provide suggestions for the selection of appropriate OCT angiography algorithms in neuroimaging.     

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.     

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.     

Real-time measurement of repetitive micro bulk motion vector and motion noise removal in optical coherence tomography angiography

In this work, we developed a motion estimation and correction method which real-time obtained the direction and displacement of repetitive micro bulk motion (such as cardiac and respiratory motion) on an SS-OCT system without additional tracking hardware, and reduced the motion noise in optical coherence tomography angiography (OCTA). In the approach, the direction of repetitive micro bulk motion was considered fixed, and proportional relationships between the motion components in three directions were determined; Then we performed one-dimension cross-correlation to obtain depth displacement which was further used to obtain other two motion components, and greatly reduced the computation; The processing speed on a graphic processing unit was 478 pairs of B-Scans per second, and the measurement range was larger than the range of the angiogram-based methods. Lastly, corrupt angiograms were recovered by adaptive scan protocol, and reduced acquisition time in comparison with the previous work.     

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.     

Visibility of microvessels in Optical Coherence Tomography angiography depends on angular orientation

Optical Coherence Tomography angiography (OCTA) is a widespread tool for depth‐resolved imaging of chorioretinal vasculature with single microvessel resolution. To improve the clinical interpretation of OCTA, the conditions affecting visualization of microvessels must be defined. Here we inject a scattering plasma tracer (Intralipid) during OCTA imaging of the anesthetized rat eye. In the retina, we find that interlaminar (vertical) vessels that connect laminae have one‐fourth to one‐third the OCTA red blood cell to tracer (RBC‐to‐tracer) signal ratio of intralaminar (horizontal) vessels. This finding suggests that the OCTA signal from microvessels depends on angular orientation, making vertically‐oriented vessels more difficult to visualize using intrinsic contrast alone. Clinicians should be aware of this potential artifact when interpreting OCTA.     

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.     

Quantification of diabetic macular ischemia using novel three‐dimensional optical coherence tomography angiography metrics

We applied three‐dimensional (3D) analysis to optical coherence tomography angiography (OCTA) to measure macular ischemia in eyes affected by non‐proliferative diabetic retinopathy (DR). A previously validated algorithm was applied to OCTA data in order to obtain 3D visualization of the retinal vasculature. Successively, a global thresholding algorithm was applied and two novel quantitative metrics were introduced: 3D vascular volume and 3D perfusion density. Two‐dimensional (2D) OCTA metrics were also obtained with different binarization thresholds for comparison. Of the 30 patients included, 15 were diagnosed with DR and 15 were controls. The 3D vascular volume and 3D perfusion density were reduced in DR eyes (P < .0001). The 2D variables also significantly differ between groups. The 3D perfusion density had the highest area under the receiver operating characteristic curve (0.964) among tested variables. Assessing quantitative perfusion using 3D analysis is reliable and promising, and with an elevated diagnostic efficacy in identifying DR eyes.     

Comparative study of optical coherence tomography angiography algorithms for rodent retinal imaging

Optical coherence tomography angiography (OCTA) is a functional extension of optical coherence tomography for non-invasive in vivo three-dimensional imaging of the microvasculature of biological tissues. Several algorithms have been developed to construct OCTA images from the measured optical coherence tomography signals. In this study, we compared the performance of three OCTA algorithms that are based on the variance of phase, amplitude, and the complex representations of the optical coherence tomography signals for rodent retinal imaging, namely the phase variance, improved speckle contrast, and optical microangiography. The performance of the different algorithms was evaluated by comparing the quality of the OCTA images regarding how well the vasculature network can be resolved. Quantities that are widely used in ophthalmic studies including blood vessel density, vessel diameter index, vessel perimeter index, vessel complexity index were also compared. Results showed that both the improved speckle contrast and optical microangiography algorithms are more robust than phase variance, and they can reveal similar vasculature features while there are statistical differences in the calculated quantities.     

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.     

Simultaneous detection of cerebral blood perfusion and cerebral edema using swept‐source optical coherence tomography

The progression of ischemic cerebral edema (CE) is closely related to the level of cerebral blood perfusion (CBP) and affects each other. Simultaneous detection of CBP and CE is helpful in understanding the mechanisms of ischemic CE development. In this article, a wide field of view swept‐source optical coherence tomography system was used to detect CE status and CBP levels simultaneously in middle cerebral artery occlusion rats. Images reflecting these two physiological states can be reconstructed with only one C‐scan. We quantify these two physiological states into four parameters, which contain two vascular parameters (vascular displacement distance and vascular perfusion density) and two edema parameters (optical attenuation coefficient and edema area). The association between the two vascular parameters and the two edema parameters was analyzed. The results show that there is a strong linear relationship between blood flow parameters and edema parameters. This work provides a new option for CE in vivo detection, and is very likely to play an important role in the development of relevant drugs or in selection of treatment options.      

Endoscopic optical coherence tomography angiography using a forward imaging piezo scanner probe

A forward imaging endoscope for optical coherence tomography angiography (OCTA) featuring a piezoelectric fiber scanner is presented. Imaging is performed with an optical coherence tomography (OCT) system incorporating an akinetic light source with a center wavelength of 1300 nm, bandwidth of 90 nm and A‐line rate of 173 kHz. The endoscope operates in contact mode to avoid motion artifacts, in particular, beneficial for OCTA measurements, and achieves a transversal resolution of 12 μm in air at a rigid probe size of 4 mm in diameter and 11.3 mm in length. A spiral scan pattern is generated at a scanning frequency of 360 Hz to sample a maximum field of view of 1.3 mm. OCT images of a human finger as well as visualization of microvasculature of the human palm are presented both in two and three dimensions. The combination of morphological tissue contrast with qualitative dynamic blood flow information within this endoscopic imaging approach potentially enables improved early diagnostic capabilities of internal organs for diseases such as bladder cancer.      

Relaxation time constant based optical coherence elastography

This study aimed at visualizing relative relaxation time constant (RTC) in soft tissue by using optical coherence elastography (OCE). We proposed a forced vibration model as a theoretical base to express RTC using axial gradient of periodic vibration phase captured by phase sensitive optical coherence tomography (PhS‐OCT). Validation of the model had been accomplished by experiments with isotropic and double‐layered phantoms. A fresh chicken breast sample treated with focused ultrasound was prepared to test performance of the RTC‐OCE in real tissue. All results were cross‐validated with indentation test and traditional strain‐based elastography. This study first utilized RTC mapping in 2D and 3D that covers the information of both elasticity and viscosity. The generated RTC mapping revealed the same mechanical difference internal sample which is correlated with conventional strain mapping. RTC mapping is potentially to be served as new biomarker for disease diagnosis in the future.     

Improved accuracy of quantitative birefringence imaging by polarization sensitive OCT with simple noise correction and its application to neuroimaging

Polarization-sensitive optical coherence tomography (PS-OCT) enables three-dimensional imaging of biological tissues based on the inherent contrast provided by scattering and polarization properties. In fibrous tissue such as the white matter of the brain, PS-OCT allows quantitative mapping of tissue birefringence. For the popular PS-OCT layout using a single circular input state, birefringence measurements are based on a straight-forward evaluation of phase retardation data. However, the accuracy of these measurements strongly depends on the signal-to-noise ratio (SNR) and is prone to mapping artifacts when the SNR is low. Here we present a simple yet effective approach for improving the accuracy of PS-OCT phase retardation and birefringence measurements. By performing a noise bias correction of the detected OCT signal amplitudes, the impact of the noise floor on retardation measurements can be markedly reduced. We present simulation data to illustrate the influence of the noise bias correction on phase retardation measurements and support our analysis with real-world PS-OCT image data.     

An automated framework to quantify areas of regional ischemia in retinal vascular diseases with OCT angiography

In this observational and cross‐sectional study, capillary nonperfusion (CNP) and vascular changes in branch retinal vein occlusion (BRVO, sample size [n] = 26) and choroidal neovascularization (CNV, n = 29) were evaluated. Subjects underwent imaging using Optical coherence tomography angiography (Angiovue OCTA, RTVue XR, Optovue Inc., Fremont, California). Local fractal analysis was applied to the OCTA images of superficial, deep and choriocapillaris layer. CNP area (BRVO eyes) and vascular parameters were computed using local fractal‐based method. Sensitivity and specificity of vascular parameters were assessed with receiver operating characteristics curve. Automated CNP area showed excellent agreement with manually quantified CNP areas in both superficial (intraclass coefficient [ICC] = 0.96) and deep (ICC = 0.96) layers. BRVO eyes showed significantly altered (P < .05) vascular parameters in both superficial and deep layer as compared to normal eyes (n = 30). CNVM eyes had significantly higher capillary free zones (P < .001) as compared to normal eyes. In normal vs BRVO eyes, vessel density and spacing between the large vessels had similar area under the curve (AUC) (P > .05) in both superficial (0.97 and 0.97, respectively) and deep layer (0.99 and 0.98, respectively). Further, capillary free zones showed high AUC (0.92) in differentiating CNV eyes from normal eyes.      

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.     

Machine learning in optical coherence tomography angiography

Optical coherence tomography angiography (OCTA) offers a noninvasive label-free solution for imaging retinal vasculatures at the capillary level resolution. In principle, improved resolution implies a better chance to reveal subtle microvascular distortions associated with eye diseases that are asymptomatic in early stages. However, massive screening requires experienced clinicians to manually examine retinal images, which may result in human error and hinder objective screening. Recently, quantitative OCTA features have been developed to standardize and document retinal vascular changes. The feasibility of using quantitative OCTA features for machine learning classification of different retinopathies has been demonstrated. Deep learning-based applications have also been explored for automatic OCTA image analysis and disease classification. In this article, we summarize recent developments of quantitative OCTA features, machine learning image analysis, and classification.     

Assessment of corneal viscoelasticity using elastic wave optical coherence elastography

The corneal viscoelasticity have great clinical significance, such as the early diagnosis of keratoconus. In this work, an analysis method which utilized the elastic wave velocity, frequency and energy attenuation to assess the corneal viscoelasticity is presented. Using phase‐resolved optical coherence tomography, the spatial‐temporal displacement map is derived. The phase velocity dispersion curve and center frequency are obtained by transforming the displacement map into the wavenumber‐frequency domain through the 2D fast Fourier transform (FFT). The shear modulus is calculated through Rayleigh wave equation using the phase velocity in the high frequency. The normalized energy distribution is plotted by transforming the displacement map into the spatial‐frequency domain through the 1D FFT. The energy attenuation coefficient is derived by exponential fitting to calculate the viscous modulus. Different concentrations of tissue‐mimicking phantoms and porcine corneas are imaged to validate this method, which demonstrates that the method has the capability to assess the corneal viscoelasticity.