ABSTRACT
AIM: To evaluate the imaging characteristics of dense automatic real time B-scan optical coherence tomography angiography(DART-OCTA)in macular-involved branch retinal vein occlusion(BRVO)and the diagnostic value of capillary perfusion imaging in the macular area.METHODS: From June 2020 to December 2020, there were 51 cases of 51 eyes with BRVO diagnosed in Eye Hospital, Wenzhou Medical University. Imaging characteristics of the BRVO macular area were observed by fluorescein angiography(FA), optical coherence tomography angiography(OCTA)and DART-OCTA examination, respectively. According to the retinal capillary perfusion status, the included patients were divided into capillary and non-imaging groups, comparing the results of capillary perfusion imaging in the BRVO macular area among the three examination methods.Furthermore, quantitative analysis of capillary perfusion density in the lesion involved area and the lesion non-involved area was performed in DART-OCTA images. RESULTS: Patients with 51 eyes were included in this study, FA identified 10 eyes of capillary perfusion imaging, OCTA identified 14 eyes of capillary perfusion imaging, DART-OCTA identified 34 eyes of the capillary perfusion imaging.Comparison of the three test methods for capillary perfusion imaging findings in the BRVO macular area showed that DART-OCTA was more sensitive compared to FA and OCTA for capillary perfusion imaging in the ischemic area. In DART-OCTA examination, retinal capillary blood flow density was lower in the lesion-involved areas in both the capillary perfusion imaging group and the non-imaging groups(0.65±0.20/mm vs 1.16±0.31/mm,0.41±0.16/mm vs 1.06±0.38/mm, all P<0.0001).CONCLUSION: DART-OCTA can provide clearer tomographic imaging of retinal capillary perfusion. And the imaging with its observation of BRVO involving the macular area is least affected by macular hemorrhage and it is an important complementary method for BRVO patients with significant retinal hemorrhage.
ABSTRACT
To evaluate the automated segmentation algorithm for detection of intra - retinal layers to process images obtained from ultra- high resolution optical coherence tomography ( OCT ) . Graph theory and the shortest path search based on dynamic programming were applied to automatically segment the 8 intra - retinal layers. We experimentally verified the accuracy and reliability of the algorithm. The results showed that the intra-retinal layer boundaries between automated and manual segmentations matched well. The algorithm successfully segmented the intra- retinal layers in glaucoma, high myopia, and retinitis pigmentosa patients. The proposed automatic segmentation for intra-retinal layers provides a promising tool for quantitative analysis in clinical diagnosis and treatment.