Application of generative adversarial networks in the restoration of blurred optical coherence tomography images caused by optical media opacity in eyes.

Purpose: To assess the application of generative adversarial networks (GANs) to restore the blurred optical coherence tomography (OCT) images caused by optical media opacity in eyes.

Methods: In this cross-sectional study, a spectral-domain OCT (Zeiss Cirrus 5000, Germany) was used to scan the macula of 510 eyes from 272 Chinese subjects. Optical media opacity was simulated with an algorithm for training set (420 normal eyes). Images for three test sets were from the following: 56 normal eyes before and after fitting neutral density filter (NDF), 34 eyes before and after cataract surgeries and 90 eyes processed by algorithm. GANs of pix2pix was trained with training set and restored blurred images in test sets. Structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) were used to evaluate the performance of GANs.

Results: PSNR for test sets before and after image restoration was 18.37±0.44 and 19.94±0.29 for NDF (p＜0.01), 16.65±0.99 and 16.91±0.26 for cataract (p=0.68) and 18.33±0.55 and 20.83±0.41 for algorithm regenerated graph (p＜0.01), respectively. SSIM for test sets before and after image restoration was 0.85±0.02 and 1.00±0.00 for NDF (p＜0.01), 0.92±0.07 and 0.97±0.02 for cataract (p＜0.01) and 0.86±0.02 and 0.99±0.01 for algorithm regenerated graph (p＜0.01), respectively.

Conclusions: GANs can be used to restore blurred OCT images caused by optical media opacity in eyes. Future studies are warranted to optimise this technique before the application in clinical practice.