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

arXiv: https://arxiv.org/abs/1910.05498 · HTML: https://mlsanity.com/paper/1910.05498/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [Multi-scale reconstruction of undersampled spectral-spatial OCT data for coronary imaging using deep learning](https://mlsanity.com/paper/2204.11769/) (2022)
- [Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network](https://mlsanity.com/paper/2307.11130/) (2023)

## Abstract (arXiv)

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 3~8-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 ≥ 5. This work demonstrates the proper integration of OCT and deep learning could benefit the development of healthcare in low-resource settings.