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

Qiangjiang Hao,Jiang Liu,Yuhui Ma,Gangjun Liu,Zhengjie Chai,Shenghua Gao,Yan Hu,Jianlong Yang,Yitian Zhao,Kang Zhou

doi:10.1117/1.jbo.25.12.123702

Abstract

.Significance: Reducing the bit depth is an effective approach to lower the cost of an optical coherence tomography (OCT) imaging device and increase the transmission efficiency in data acquisition and telemedicine. However, a low bit depth will lead to the degradation of the detection sensitivity, thus reducing the signal-to-noise ratio (SNR) of OCT images.Aim: We propose using deep learning to reconstruct high SNR OCT images from low bit-depth acquisition.Approach: The feasibility of our approach is evaluated by applying this approach to the quantized 3- to 8-bit data from native 12-bit interference fringes. We employ a pixel-to-pixel generative adversarial network (pix2pixGAN) architecture in the low-to-high bit-depth OCT image transition.Results: Extensively, qualitative and quantitative results show our method could significantly improve the SNR of the low bit-depth OCT images. The adopted pix2pixGAN is superior to other possible deep learning and compressed sensing solutions.Conclusions: Our work demonstrates that the proper integration of OCT and deep learning could benefit the development of healthcare in low-resource settings.

Highlights

Optical coherence tomography (OCT) is a noninvasive cross-sectional high-resolution imaging modality that has been widely used in various medical fields, such as ophthalmology, cardiovascular endoscopy, and dermatology.[1]
An OCT or functional OCT volume usually has a size of hundreds of megabytes or several gigabytes, which requires fast and wide-band analog-to-digital converter (ADC) or frame grabber for data acquisition, and advanced graphics processing units (GPUs) for real-time imaging alignment
We further propose to employ emerging deep learning techniques to compensate for the data quality degeneration caused by the low bit depth mentioned above

Summary

Introduction

Optical coherence tomography (OCT) is a noninvasive cross-sectional high-resolution imaging modality that has been widely used in various medical fields, such as ophthalmology, cardiovascular endoscopy, and dermatology.[1]. With the evolution of OCT techniques, the increased data size becomes a major issue.[4] An OCT or functional OCT volume usually has a size of hundreds of megabytes or several gigabytes, which requires fast and wide-band analog-to-digital converter (ADC) or frame grabber for data acquisition, and advanced graphics processing units (GPUs) for real-time imaging alignment. These kinds of hardware significantly increase the cost of OCT device. The large data size influences the transmission efficiency of OCT data among clinical sites, which further impedes the popularization of telemedicine

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