Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data

TL;DR

This paper introduces a deep learning framework for reconstructing high-quality OCT images from undersampled spectral data, enabling faster imaging without hardware modifications.

Contribution

It presents a neural network-based method that reconstructs OCT images from undersampled spectral data, reducing data acquisition requirements and processing time.

Findings

Successfully reconstructed images from 2x undersampled spectral data with high fidelity.

Achieved real-time reconstruction (~6.73 ms) on a desktop computer.

Extended the framework to 3x undersampling with some quality trade-offs.

Abstract

Optical Coherence Tomography (OCT) is a widely used non-invasive biomedical imaging modality that can rapidly provide volumetric images of samples. Here, we present a deep learning-based image reconstruction framework that can generate swept-source OCT (SS-OCT) images using undersampled spectral data, without any spatial aliasing artifacts. This neural network-based image reconstruction does not require any hardware changes to the optical set-up and can be easily integrated with existing swept-source or spectral domain OCT systems to reduce the amount of raw spectral data to be acquired. To show the efficacy of this framework, we trained and blindly tested a deep neural network using mouse embryo samples imaged by an SS-OCT system. Using 2-fold undersampled spectral data (i.e., 640 spectral points per A-line), the trained neural network can blindly reconstruct 512 A-lines in ~6.73 ms using a desktop computer, removing spatial aliasing artifacts due to spectral undersampling, also presenting a very good match to the images of the same samples, reconstructed using the full spectral OCT data (i.e., 1280 spectral points per A-line). We also successfully demonstrate that this framework can be further extended to process 3x undersampled spectral data per A-line, with some performance degradation in the reconstructed image quality compared to 2x spectral undersampling. This deep learning-enabled image reconstruction approach can be broadly used in various forms of spectral domain OCT systems, helping to increase their imaging speed without sacrificing image resolution and signal-to-noise ratio.