Sparsity-Aware OCT Volumetric Data Restoration Using Optical Synthesis Model

In this study, a novel restoration model for the data of optical coherence tomography (OCT) is proposed. An OCT device acquires a tomographic image of a specimen at the scale of a few micrometers using a near-infrared laser and has been frequently adopted to measure the structures of bio-tissues. In certain applications, OCT devices face the problem of extremely weak reflected light and require the help of image processing to estimate the distribution of reflected light hidden in various noises. OCT identifies tomographic structures by searching for peak interference locations and their intensities. Therefore, the challenge of OCT data restoration involves the problem of identifying the interference function and its deconvolution. In this study, a restoration method is given by reducing the problem to a regularized least-squares problem with a hard constraint for the latent refractive index distributions, and an algorithm is derived using a primal-dual splitting (PDS) framework. The PDS has the advantage of requiring no inverse matrix operation and is able to handle high-dimensional data. The significance of the proposed method is verified through simulations using artificial data, followed by an experiment conducted using actual observation of 64 x 64 x 5000 sized voxels.

Automated summary

A novel restoration model is proposed for optical coherence tomography (OCT) data, which tackles the challenge of weak reflected light and identifies tomographic structures using image processing and an algorithm derived from a primal-dual splitting framework. The significance of this method is verified through simulations and experiments, highlighting its importance in restoring OCT data.