Enhanced Visualization of Retinal Microvasculature via Deep Learning on OCTA Image Quality

Yishuang Xu; Yu Su; Dihao Hua; Peter Heiduschka; Wenliang Zhang; Tianyue Cao; Jingcheng Liu; Zhenyu Ji; Nicole Eter

doi:10.1155/2021/1373362

Enhanced Visualization of Retinal Microvasculature via Deep Learning on OCTA Image Quality

Dis Markers. 2021 Jun 16:2021:1373362. doi: 10.1155/2021/1373362. eCollection 2021.

Authors

Yishuang Xu^{1

2}, Yu Su¹, Dihao Hua^{1

2}, Peter Heiduschka², Wenliang Zhang², Tianyue Cao¹, Jingcheng Liu³, Zhenyu Ji¹, Nicole Eter²

Affiliations

¹ Eye Center, Renmin Hospital of Wuhan University, Wuhan, China.
² Department of Ophthalmology, University of Muenster Medical Center, Muenster, Germany.
³ Department of Endocrinology & Metabolism, Renmin Hospital of Wuhan University, Wuhan, China.

Abstract

Purpose: To investigate the impact of denoising on the qualitative and quantitative parameters of optical coherence tomography angiography (OCTA) images of the optic nerve and macular area.

Methods: OCTA images of the optic nerve and macular area were obtained using a Canon-HS100 OCT device for 48 participants (48 eyes). Multiple image averaging (MIA) and denoising techniques were used to improve the quality of the OCTA images. The peak signal-to-noise ratio (PSNR) as an image quality parameter and vessel density (VD) as a quantitative parameter were obtained from single-scan, MIA, and denoised OCTA images. The parameters were compared, and the correlation was analyzed between different imaging protocols.

Results: In the optic nerve area, there were significant differences in the PSNR and VD in all measured regions between the three groups (P < 0.0001). The PSNR of the denoised group was significantly higher than that of the other two groups (P < 0.0001). The VD in the denoised group was significantly lower than that in the single-scan group in all measured regions (P < 0.0001). In the macular area, there were significant differences in the PSNR and VD in all measured regions among the three groups. The PSNR of the denoised group was significantly higher than that of the other two groups (P < 0.0001). The VD in the denoised group was significantly lower than that in the single-scan group in all measured regions. The VD around the optic nerve in the denoised group was correlated with that in the single-scan group (R = 0.9403, P < 0.0001), but the VD in the MIA group was not correlated with that in the single-scan group (R = 0.2505, P = 0.2076). The VD around the fovea in the denoised and MIA images was correlated with that in the single-scan group (R = 0.7377, P < 0.0001; R = 0.7005, P = 0.0004, respectively).

Conclusion: Denoising could provide an easy and quick way to improve image quality parameters, such as PSNR. It shows great potential in improving the sensitivity of OCTA images as retinal disease markers.

MeSH terms

Adult
Aged
Angiography
Deep Learning
Female
Humans
Macula Lutea / blood supply*
Macula Lutea / diagnostic imaging
Male
Middle Aged
Optic Nerve / diagnostic imaging*
Radiographic Image Interpretation, Computer-Assisted / instrumentation
Radiographic Image Interpretation, Computer-Assisted / methods*
Retinal Vessels / diagnostic imaging*
Signal-To-Noise Ratio
Tomography, Optical Coherence