High signal-to-noise ratio reconstruction of low bit-depth optical coherence tomography using deep learning

TL;DR

This paper presents a deep learning method to reconstruct high SNR OCT images from low bit-depth data, improving image quality and potentially reducing system costs in healthcare applications.

Contribution

It introduces a novel deep learning approach using GANs to enhance low bit-depth OCT images, enabling better SNR and image quality compared to traditional methods.

Findings

Significant SNR improvement in low bit-depth OCT images.

Effective reconstruction when bit-depth is ≥ 5 bits.

Potential for cost reduction in OCT systems.

Abstract

Reducing the bit-depth is an effective approach to lower the cost of optical coherence tomography (OCT) systems and increase the transmission efficiency in data acquisition and telemedicine. However, a low bit-depth will lead to the degeneration of the detection sensitivity thus reduce the signal-to-noise ratio (SNR) of OCT images. In this paper, we propose to use deep learning for the reconstruction of the high SNR OCT images from the low bit-depth acquisition. Its feasibility was preliminarily evaluated by applying the proposed method to the quantized $3\sim8$-bit data from native 12-bit interference fringes. We employed a pixel-to-pixel generative adversarial network architecture in the low to high bit-depth OCT image transition. Retinal OCT data of a healthy subject from a homemade spectral-domain OCT system was used in the study. Extensively qualitative and quantitative results show this deep-learning-based approach could significantly improve the SNR of the low bit-depth OCT images especially at the choroidal region. Superior similarity and SNR between the reconstructed images and the original 12-bit OCT images could be derived when the bit-depth $\geq 5$. This work demonstrates the proper integration of OCT and deep learning could benefit the development of healthcare in low-resource settings.