Developing a high-resolution non-invasive optical coherence tomography angiography (OCTA) method for iris vasculature imaging is essential for diagnosing a wide range of ocular pathologies. However, the current iris-OCTA devices are still limited in imaging quality and penetration depth for dark-colored eyes ranging from brown to dark brown. A spectral domain iris-OCTA system is presented in this paper incorporating a 1300 nm wavelength for deeper tissue penetration, a linear-wavenumber spectrometer for better detection sensitivity, and an iris scan objective lens for better optical focusing across the entire iris over a 12 × 12 mm2 scan field. The −6 dB fall-off range is ∼3 mm, and the maximum sensitivity fall-off is −28.57 dB at 6.94 mm. The axial resolution is 15.1 ± 3.2 μm. The 40 mm focal-length iris scan objective is optimized based on the ocular parameters from 100 Asian participants’ left eyes, and it has a diffraction-limited lateral resolution (14.14 μm) for the iris, in general. OCT distortions were calibrated based on the average ocular parameters, and the maximum residual distortions in both the lateral and axial directions were <0.1 mm (2.0%) for all of the eyes. A pilot study on a constricted pupil was performed to demonstrate high-contrast, wide-field en face iris microvascular imaging by either a horizontal or vertical fast-scan protocol in a dark brown eye. The iris vessels are radially aligned, and each vessel is more visible when it has an angle of ∼65°–90° with respect to the fast-scan direction. A new circular fast-scan protocol could improve image quality for better visualization of the iris features or integration with image-registration algorithms and an eye-tracking system for eye-motion compensation.

No abstract

Optical coherence tomography angiography (OCTA) in dermatology usually suffers from low image quality due to the highly scattering property of the skin, the complexity of cutaneous vasculature, and limited acquisition time. Deep‐learning methods have achieved great success in many applications. However, the deep learning approach to improve dermatological OCTA images has not been investigated due to the requirement of high‐performance OCTA systems and difficulty of obtaining high‐quality images as ground truth. This study aims to generate proper datasets and develop a robust deep learning method to enhance the skin OCTA images. A swept‐source skin OCTA system was employed to create low‐quality and high‐quality OCTA images with different scanning protocols. We propose a model named vascular visualization enhancement generative adversarial network and adopt an optimized data augmentation strategy and perceptual content loss function to achieve better image enhancement effect with small amount of training data. We demonstrate the superiority of the proposed method in skin OCTA image enhancement by quantitative and qualitative comparisons.