AIM: To assess circumpapillary retinal vessel density (RVD) profiles and correlate them with retinal nerve fibre layer (RNFL) thickness measured by Fourier domain optical coherence tomography (FD-OCT). METHODS: RNFL thickness of 106 healthy volunteers was measured using Cirrus FD-OCT. A proprietary software was developed in MATLAB to assess the thickness and position of circumpapillary retinal vessels using the scanning laser ophthalmoscopy fundus image, centred on the optic disc. The individual retinal vessel positions and thickness values were integrated in a 256-sector RVD profile, and intrasubject and intersubject correlations were calculated. RESULTS: The mean value ± SD for intrasubject correlation between RVD and RNFL was 0.5349 ± 0.1639, with 101 of 106 subjects presenting significant correlation (p<0.05). 181 (out of 256) sectors presented a significant correlation between RVD and RNFL, with a mean value ± SD of 0.2600 ± 0.1140 (p<0.05). CONCLUSIONS: Using our model of the circumpapillary retinal vessel distribution, 70% of the RNFL thickness is influenced by RVD. On average, 7% of the interindividual variance of the RNFL thickness may be explained by RVD. A normative database that takes into account the circumpapillary blood vessels might slightly improve the diagnostic power of RNFL measurement.
AIM: To assess circumpapillary retinal vessel density (RVD) profiles and correlate them with retinal nerve fibre layer (RNFL) thickness measured by Fourier domain optical coherence tomography (FD-OCT). METHODS: RNFL thickness of 106 healthy volunteers was measured using Cirrus FD-OCT. A proprietary software was developed in MATLAB to assess the thickness and position of circumpapillary retinal vessels using the scanning laser ophthalmoscopy fundus image, centred on the optic disc. The individual retinal vessel positions and thickness values were integrated in a 256-sector RVD profile, and intrasubject and intersubject correlations were calculated. RESULTS: The mean value ± SD for intrasubject correlation between RVD and RNFL was 0.5349 ± 0.1639, with 101 of 106 subjects presenting significant correlation (p<0.05). 181 (out of 256) sectors presented a significant correlation between RVD and RNFL, with a mean value ± SD of 0.2600 ± 0.1140 (p<0.05). CONCLUSIONS: Using our model of the circumpapillary retinal vessel distribution, 70% of the RNFL thickness is influenced by RVD. On average, 7% of the interindividual variance of the RNFL thickness may be explained by RVD. A normative database that takes into account the circumpapillary blood vessels might slightly improve the diagnostic power of RNFL measurement.
Authors: Seungwoo Hong; Hongli Yang; Stuart K Gardiner; Haomin Luo; Christy Hardin; Glen P Sharpe; Joseph Caprioli; Shaban Demirel; Christopher A Girkin; Jeffrey M Liebmann; Christian Y Mardin; Harry A Quigley; Alexander F Scheuerle; Brad Fortune; Balwantray C Chauhan; Claude F Burgoyne Journal: Am J Ophthalmol Date: 2019-05-13 Impact factor: 5.258
Authors: Sieun Lee; Morgan Heisler; Paul J Mackenzie; Marinko V Sarunic; Mirza Faisal Beg Journal: Transl Vis Sci Technol Date: 2017-02-28 Impact factor: 3.283