学术文献库

The area of the foveal avascular zone (FAZ) from en face images of optical coherence tomography angiography (OCTA) is one of the most common measurement based on this technology. However, its use in clinic is limited by the high variation of the FAZ area across normal subjects, while the calculation of the volumetric measurement of the FAZ is limited by the high noise that characterizes OCTA scans. We designed an algorithm that exploits the higher signal-to-noise ratio of en face images to efficiently identify the capillary network of the inner retina in 3-dimensions (3D), under the assumption that the capillaries in separate plexuses do not overlap. The network is then processed with morphological operations to identify the 3D FAZ within the bounding segmentations of the inner retina. The FAZ volume and area in different plexuses were calculated for a dataset of 430 eyes. Then, the measurements were analyzed using linear mixed effect models to identify differences between three groups of eyes: healthy, diabetic without diabetic retinopathy (DR) and diabetic with DR. Results showed significant differences in the FAZ volume between the different groups but not in the area measurements. These results suggest that volumetric FAZ could be a better diagnostic detector than the planar FAZ. The efficient methodology that we introduced could allow the fast calculation of the FAZ volume in clinics, as well as providing the 3D segmentation of the capillary network of the inner retina.