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

Go to Nature Website

Your Location：

Home >

Browse articles >

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

Articles

- Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data
- Light: Science & Applications Vol. 10, Issue 9, Pages: 1658-1671(2021)
- 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
  CLC：
- Published：30 September 2021，
  
  Published Online：29 July 2021，
  
  Received：20 March 2021，
  
  Revised：02 July 2021，
  
  Accepted：06 July 2021
- Accepted：
Scan QR Code
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：

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

Keywords

references

Huang, D. et al. Optical coherence tomography.Science254, 1178–1181 (1991)..

Fercher, A. F. et al. Measurement of intraocular distances by backscattering spectral interferometry.Opt. Commun.117, 43–48 (1995)..

Chinn, S. R., Swanson, E. A.&Fujimoto, J. G. Optical coherence tomography using a frequency-tunable optical source.Opt. Lett.22, 340–342 (1997)..

Choma, M. A. et al. Sensitivity advantage of swept source and Fourier domain optical coherence tomography.Opt. Express11, 2183–2189 (2003)..

De Boer, J. F. et al. Improved signal-to-noise ratio in spectral-domain compared with time-domain optical coherence tomography.Opt. Lett.28, 2067–2069 (2003)..

De Boer, J. F., Leitgeb, R.&Wojtkowski, M. Twenty-five years of optical coherence tomography: the paradigm shift in sensitivity and speed provided by Fourier domain OCT [Invited].Biomed. Opt. Express8, 3248–3280 (2017)..

Oh, W. Y. et al. Ultrahigh-speed optical frequency domain imaging and application to laser ablation monitoring.Appl. Phys. Lett.88, 103902 (2006)..

Huber, R., Wojtkowski, M.&Fujimoto, J. G. Fourier Domain Mode Locking (FDML): a new laser operating regime and applications for optical coherence tomography.Opt. Express14, 3225–3237 (2006)..

Huber, R., Adler, D. C.&Fujimoto, J. G. Buffered Fourier domain mode locking: unidirectional swept laser sources for optical coherence tomography imaging at 370, 000 lines/s.Opt. Lett.31, 2975–2977 (2006)..

Yun, S. H. et al. Comprehensive volumetric optical microscopy in vivo.Nat. Med.12, 1429–1433 (2006)..

Adler, D. C. et al. Three-dimensional endomicroscopy using optical coherence tomography.Nat. Photonics1, 709–716 (2007)..

Potsaid, B. et al. Ultrahigh speed spectral/Fourier domain OCT ophthalmic imaging at 70, 000 to 312, 500 axial scans per second.Opt. Express16, 15149–15169 (2008)..

Klein, T.&Huber, R. High-speed OCT light sources and systems [Invited].Biomed. Opt. Express8, 828–859 (2017)..

Wei,X. M. et al. 28 MHz swept source at 1.0 μm for ultrafast quantitative phase imaging.Biomed. Opt. Express6, 3855–3864 (2015)..

Oh, W. Y. et al. 400 kHz repetition rate wavelength-swept laser and application to high-speed optical frequency domain imaging.Optics Lett.35, 2919–2921 (2010)..

Tsai, T. H. et al. Ultrahigh speed endoscopic optical coherence tomography using micromotor imaging catheter and VCSEL technology.Biomed. Opt. Express4, 1119–1132 (2013)..

Singh, M. et al. Phase-sensitive optical coherence elastography at 1.5 million A-Lines per second.Opt. Lett.40, 2588–2591 (2015)..

Wieser, W. et al. High definition live 3D-OCT in vivo: design and evaluation of a 4D OCT engine with 1 GVoxel/s.Biomed. Opt. Express5, 2963–2977 (2014)..

Blatter, C. et al. Ultrahigh-speed non-invasive widefield angiography.J. Biomed. Opt.17, 070505 (2012)..

Baumann, B. et al. Total retinal blood flow measurement with ultrahigh speed swept source/Fourier domain OCT.Biomed. Opt. Express2, 1539–1552 (2011)..

de Haan, K. et al. Deep-learning-based image reconstruction and enhancement in optical microscopy.Proc. IEEE108, 30–50 (2020)..

Barbastathis, G., Ozcan, A.&Situ, G. On the use of deep learning for computational imaging.Optica6, 921–943 (2019)..

Rivenson, Y. et al. Deep learning microscopy.Optica4, 1437–1443 (2017)..

Wang, H. D. et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy.Nat. Methods16, 103–110 (2019)..

De Haan, K. et al. Resolution enhancement in scanning electron microscopy using deep learning.Sci. Rep.9, 12050 (2019)..

Boyd, N. et al. DeepLoco: fast 3D localization microscopy using neural networks. Preprint athttps://www.biorxiv.org/content/10.1101/267096v1https://www.biorxiv.org/content/10.1101/267096v1(2018).

Ouyang, W. et al. Deep learning massively accelerates super-resolution localization microscopy.Nat. Biotechnol.36, 460–468 (2018)..

Nehme, E. et al. Deep-STORM: super-resolution single-molecule microscopy by deep learning.Optica5, 458–464 (2018)..

Luo, Y. L. et al. Single-shot autofocusing of microscopy images using deep learning.ACS Photonics8, 625–638 (2021)..

Pinkard, H. et al. Deep learning for single-shot autofocus microscopy.Optica6, 794–797 (2019)..

Pitkäaho, T., Manninen, A.&Naughton, T. J. Performance of autofocus capability of deep convolutional neural networks in digital holographic microscopy. InProceedings of Digital Holography and Three-Dimensional Imaging. JeJu Island, Korea, Optical Society of America, 2017, W2A. 5(2017).

Wu, Y. C. et al. Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning.Nat. Methods16, 1323–1331 (2019)..

Yang, X. L. et al. Deep learning-based virtual refocusing of images using an engineered point-spread function.ACS Photonics8, 2174–2182, https://doi.org/10.1021/acsphotonics.1c00660 (2021)..

Huang, L. Z. et al. Recurrent neural network-based volumetric fluorescence microscopy.Light Sci. Appl.ume 10, 62 (2021)..

Rivenson, Y. et al. Phase recovery and holographic image reconstruction usingdeep learning in neural networks.Light Sci. Appl.ume 7, 17141 (2018)..

Wu, Y. C. et al. Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery.Optica5, 704–710 (2018)..

Liu, T. R. et al. Deep learning-based color holographic microscopy.J. Biophotonics12, e201900107 (2019)..

Liu, T. R. et al. Deep learning-based holographic polarization microscopy.ACS Photonics7, 3023–3034 (2020)..

Nguyen, T. et al. Deep learning approach for Fourier ptychography microscopy.Opt. Express26, 26470–26484 (2018)..

Helgadottir, S., Argun, A.&Volpe, G. Digital video microscopy enhanced by deep learning.Optica6, 506–513 (2019)..

Nguyen, T. et al. Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection.Opt. Express25, 15043–15057 (2017)..

Hershko, E. et al. Multicolor localization microscopy and point-spread-function engineering by deep learning.Opt. Express27, 6158–6183 (2019)..

Drexler, W.&Fujimoto, J. G.Optical Coherence Tomography: Technology and Applications(Springer, Berlin, 2008).

Sara, U., Akter, M.&Uddin, M. S. Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study.J. Comput. Commun.7, 8–18 (2019)..

Singh, M. et al. Applicability, usability, and limitations of murine embryonic imaging with optical coherence tomography and optical projection tomography.Biomed. Opt. Express7, 2295–2310 (2016)..

Ronneberger, O., Fischer, P.&Brox, T. U-net: convolutional networks for biomedical image segmentation. InProceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich, Germany: Springer, 2015234–241 (2015).

Kingma, D. P.&Ba, J. Adam: a method for stochastic optimization. Preprint athttps://arxiv.org/abs/1412.6980https://arxiv.org/abs/1412.6980(2014).

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

No data

Related Author

No data

Related Institution

No data

⁰