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

Yijie Zhang; Tairan Liu; Manmohan Singh; Ege Çetintaş; Yilin Luo; Yair Rivenson; Kirill V. Larin; Aydogan Ozcan

doi:10.1038/s41377-021-00594-7

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

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- Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data
- Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data
- LSA 2021年10卷第9期页码：1658-1671
- Affiliations：
  
  1.Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
  2.Department of Bioengineering, University of California, Los Angeles, CA, 90095, USA
  3.California NanoSystems Institute, University of California, Los Angeles, CA, 90095, USA
  4.Department of Biomedical Engineering, University of Houston, Houston, TX, 77204, USA
  5.Department of Molecular Physiology and Biophysics, Baylor College of Medicine, University of Houston, Houston, TX, 77204, USA
  6.Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Author bio：
  
  Aydogan Ozcan (ozcan@ucla.edu)
- Funds：
- DOI：10.1038/s41377-021-00594-7
  中图分类号：
- 纸质出版日期：2021-09-30，
  
  网络出版日期：2021-07-29，
  
  收稿日期：2021-03-20，
  
  修回日期：2021-07-02，
  
  录用日期：2021-07-06
- Accepted：
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Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data[J]. LSA, 2021,10(9):1658-1671.

Zhang, Y. J. et al. Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data. Light: Science & Applications, 10, 1658-1671 (2021).
Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data[J]. LSA, 2021,10(9):1658-1671. DOI： 10.1038/s41377-021-00594-7.

Zhang, Y. J. et al. Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data. Light: Science & Applications, 10, 1658-1671 (2021). DOI： 10.1038/s41377-021-00594-7.

摘要

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 setup 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 0.59 ms using multiple graphics-processing units (GPUs)

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 3× undersampled spectral data per A-line

with some performance degradation in the reconstructed image quality compared to 2× spectral undersampling. Furthermore

an A-line-optimized undersampling method is presented by jointly optimizing the spectral sampling locations and the corresponding image reconstruction network

which improved the overall imaging performance using less spectral data points per A-line compared to 2× or 3× spectral undersampling results. 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.

关键词

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