Fast iterative method empowered by GPU acceleration for Fourier-domain optical coherence tomography image reconstruction

Abstract

Iterative methods could provide high-quality image reconstruction for Fourier-domain optical coherence tomography (FD-OCT) by solving an inverse problem. Compared with the regular IFFT-based reconstruction, a more accurate estimation could be iteratively solved by integrating prior knowledge, however, it is often more time-consuming. To deal with the time problem, we proposed a fast iterative method for FD-OCT image reconstruction empowered by GPU acceleration. An iterative scheme is adopted, including a forward model and an inverse solver. Large-scale parallelism of OCT image reconstruction is performed on B-scans. We deployed the framework on Nvidia GeForce RTX 3090 graphic card that enables parallel processing. With the widely used toolkit Pytorch, the inverse problem of OCT image reconstruction is solved by the stochastic gradient descent (SGD) algorithm. To validate the effectiveness of the proposed method, we compare the computational time and image quality with other iterative approaches including ADMM, AR, and RFIAA method. The proposed method could provide a significant speed enhancement of 1,500 times with comparable image quality to that of ADMM reconstruction. The result indicates a potential for high-quality real-time volumetric OCT image reconstruction via iterative algorithms.