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

• Published in: arXiv.org
• Main Authors: Qiangjiang Hao, Zhou, Kang, Yang, Jianlong, Fang, Liyang, Chai, Zhengjie, Ma, Yuhui, Hu, Yan, Gao, Shenghua, Liu, Jiang
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 11.02.2020
• Subjects: Deep learning; Degeneration; Image acquisition; Image detection; Image reconstruction; Interference fringes; Machine learning; Medical imaging; Noise reduction; Optical Coherence Tomography; Physics - Optics; Pixels; Signal to noise ratio; System effectiveness; Telemedicine; Tomography; Transmission efficiency
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1910.05498

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.