The wall-to-lumen ratio (WLR) of the vasculature is a promising early marker of retinal microvascular changes. Recently, adaptive optics scanning laser ophthalmoscopy (AOSLO) enabled direct and noninvasive visualization of the arterial wall. Using AOSLO, we analyzed the correlation between age and WLR in 51 normal subjects. In addition, correlations between blood pressure and WLR were analyzed in 73 subjects (51 normal subjects and 22 hypertensive patients). WLR showed a strong correlation with age (r = 0.68, P < 0.0001), while outer diameter and inner diameter did not show significant correlation with age in the normal group (r = 0.13, P = 0.36 and r = −0.12, P = 0.41, respectively). In the normal and hypertensive groups, WLR showed a strong correlation with systolic and diastolic blood pressure (r = 0.60, P < 0.0001 and r = 0.65, P < 0.0001, respectively). In conclusion, AOSLO provided noninvasive and reproducible arterial measurements. WLR is an early marker of morphological changes in the retinal arteries due to age and blood pressure.

Background: To investigate the effects of deep learning denoising on quantitative vascular measurements and the quality of optical coherence tomography angiography (OCTA) images. Methods: U-Net-based deep learning denoising with an averaged OCTA data set as teacher data was used in this study. One hundred and thirteen patients with various retinal diseases were examined. An OCT HS-100 (Canon inc., Tokyo, Japan) performed a 3 × 3 mm² superficial capillary plexus layer slab scan centered on the fovea 10 times. A single-shot image was defined as the original image and the 10-frame averaged image and denoised image generated from the original image using deep learning denoising for the analyses were obtained. The main parameters measured were the OCTA image acquisition time, contrast-to-noise ratio (CNR), peak signal-to-noise ratio (PSNR), vessel density (VD), vessel length density (VLD), vessel diameter index (VDI), and fractal dimension (FD) of the original, averaged, and denoised images. Results: One hundred and twelve eyes of 108 patients were studied. Deep learning denoising removed the background noise and smoothed the rough vessel surface. The image acquisition times for the original, averaged, and denoised images were 16.6 ± 2.4, 285 ± 38, and 22.1 ± 2.4 s, respectively (P < 0.0001). The CNR and PSNR of the denoised image were significantly higher than those of the original image (P < 0.0001). There were significant differences in the VLD, VDI, and FD (P < 0.0001) after deep learning denoising. Conclusions: The deep learning denoising method achieved high speed and high quality OCTA imaging. This method may be a viable alternative to the multiple image averaging technique.

Purpose.: To investigate the source of moving particles in parafoveal capillaries detected by adaptive optics scanning laser ophthalmoscopy (AO-SLO). Methods.: AO-SLO videos were acquired from the parafoveal areas of eyes of healthy subjects. The gray-scale values inside and outside the moving particles were measured and compared. Thereafter, successive frames of the captured videos were analyzed under higher magnification to detect changes in the gray values of bright spots inside the capillaries, before and during passage of the particles. Simultaneously, changes in the gray values of areas without the bright spots were measured for comparison. Then, the authors analyzed the packing arrangements of the bright spots in the particles, and measured the particle velocity using spatiotemporal images of the target capillary. Results.: There were no significant differences in the gray values between the moving particles and the cone mosaic outside the parafoveal capillaries adjacent to the particles. The gray value of the bright spots in the dark shadow of the vessels increased when the particles passed through, while the dark areas without bright spots remained dark. There were no significant differences in the packing arrangements between the bright spots and surrounding cone mosaic. Further, the concordance rate of packing arrangements of bright spots between two consecutive moving particles in the capillary was 95.8%. The mean particle velocity was 1.34 ± 0.42 mm/s. Conclusions.: The particles moving in the capillaries are suggested to be reflections of photoreceptor aggregates that pass through circulating transparent objects such as leukocytes or plasma gaps.