Aging alterations in dermal blood vessels have been investigated using Optical Coherence Tomography Angiography (OCTA). However, classifying the vessel’s type was previously limited. In this study, we focused on diameter-dependent vascular alterations in facial skin with age, developing 3D analytical methods to the OCTA data with removing tail-artifact. As a result, it was found that the number of micro-vessels, defined at 20–39 microns, decreased with age, which was inversely true for thick vessels (160–179 micron diameter). Our results suggest that the aging degree of dermal vessels may be uniquely assessed by the diameter-dependent vascular alterations using the OCTA.
Pulsatile signals from the cutaneous blood flow could be informative for evaluating the health condition of an individual. One of the popular optical measuring devices, photoplethysmogram (PPG) is often used to detect the pulse signal from skin. However, the origin of the PPG signal still remains controversial. Benefiting from the non-invasive, label-free, 3D imaging tool, optical coherence tomography (OCT) is able to capture the intrinsic tissue signals at different penetration depth in high spatial and temporal resolution. Periodic pulse signal was observed by taking advantage of the optical microangiography (OMAG) algorithm which is sensitive to the motion of blood flow. The pulsatile pattern from the capillary and arteriole was successfully differentiated and their morphology showed distinct property at different local blood pressure. The pulse signal from the arteriole is more consistent and has similar waveform as the PPG signals. The result indicated that the PPG signal could be deceive by the mixing signal from the capillary bed and arterioles since it measures the total blood volume change in the plexuses. This study may shed some new light on understanding the mechanical property of how blood travel through different types of vasculature networks and elucidate its potential application in disease assessments.
Optical coherence tomography angiography (OCT-A) is a novel non-invasive imaging technique that provide the visualization of retinal microvasculature. However, the quantification and evaluation of OCT-A are still a challenge for the diagnosis for ophthalmology. Deep convolutional neural network (CNN) architectures were initially designed for the task of natural image classification, delivering promising precision in computer vision tasks and recent research has applied deep CNN to biomedical image processing tasks and produces impressive outcomes. However, so far, there is no report relating to the application of large deep neural networks on a small annotated OCT-A dataset. We collected and annotated OCT-A datasets that contain diabetic retinopathy (DR), uveitis, dry age-related macular degeneration (AMD) patients, and normal cases. We propose a transfer learning CNN model for automated disease classification using clinical OCT-A images. The CNN model is pre-trained on the ImageNet dataset and fine-tuned the top feedforward layers of the model to fit the classification task during the training process. The proposed approach can offer a real-time evaluation and discrimination of retinal pathologies with a depth-encoded OCT-A projected image. Our results show great accuracy of transfer learning CNN model on the classification task with the limited dataset. The CNN model with the pre-trained weights has better performance in comparison with an SVM using HOG feature approach.
Optical coherence tomography angiography (OCTA) is a promising imaging modality that enables a label-free, high-resolution and high-contrast image of biological tissue microvasculature. Typically, the blood flow contrast is implemented by mathematically analyzing the temporal dynamics of light scattering, and setting a threshold to distinguish the dynamic blood flow from the static tissue bed. However, high flow contrast is degraded by the residual overlap that results in misclassification errors between dynamic and static signals. Our study has demonstrated that flow contrast can be enhanced using a single-shot angular compounded OCTA (AC-OCTA). Because a continuous modulation is induced by the offset that the probing beam is away from the beam center in the typical OCT sample arm, different incidence angles in the probing beam are encoded in B-scan modulation frequencies. The complex-valued spectral interferogram is reconstructed by removing the conjugate terms in the depth space and its Fourier transform along the transversal fast-scan direction generates a wide conjugate-free B-scan modulation spectrum in the full space of the spatial domain. By splitting the modulation spectrum, angle-resolved independent sub-angiograms are generated and then compounded to enhance the flow contrast. Both flow phantom and in vivo animal cerebral vascular imaging demonstrated that the proposed angular compounded OCTA can offer a ~50% decrease of misclassification errors and an improved flow contrast and vessel connectivity. This AC-OCTA is beneficial to facilitating the interpretation of OCT angiograms in clinical applications.
Optical coherence tomography angiography (Angio-OCT), mainly based on the temporal dynamics of OCT scattering signals, has found a range of potential applications in clinical and scientific research. Based on the model of random phasor sums, temporal statistics of the complex-valued OCT signals are mathematically described. Statistical distributions of the amplitude differential and complex differential Angio-OCT signals are derived. The theories are validated through the flow phantom and live animal experiments. Using the model developed, the origin of the motion contrast in Angio-OCT is mathematically explained, and the implications in the improvement of motion contrast are further discussed, including threshold determination and its residual classification error, averaging method, and scanning protocol. The proposed mathematical model of Angio-OCT signals can aid in the optimal design of the system and associated algorithms.
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