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

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.