Reconstruction of highly and extremely aberrated wavefront for ocular Shack-Hartmann sensor using multi-task Attention-UNet

Abstract

In certain ocular conditions, such as in eyes with keratoconus or after corneal laser surgery, Higher Order Aberrations (HOAs) may be dramatically elevated. Accurately recording interpretable wavefronts in such highly aberrated eyes using Shack-Hartmann sensor is a challenging task. While there are studies that have applied deep neural networks to Shack-Hartmann wavefront reconstructions, they have been limited to low resolution and small dynamic range cases. In this study, we introduce a multi-task learning scheme for High-Resolution and High Dynamic Range Shack-Hartmann wavefront reconstruction using a modified attention-UNet (HR-HDR-SHUNet), which outputs a wavefront map along with Zernike coefficients simultaneously. The HR-HDR-SHUNet was evaluated on three large datasets with different levels of HOAs (regularly, highly, and extremely aberrated), with successful reconstruction of all aberrated wavefronts, at the same time achieving significantly higher accuracy than both traditional methods and other deep learning networks; it is also computationally more efficient than the latter.