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Computational high-resolution optical imaging of the living human retina

An Addendum to this article was published on 29 September 2015

This article has been updated


High-resolution in vivo imaging is of great importance for the fields of biology and medicine. The introduction of hardware-based adaptive optics (HAO) has pushed the limits of optical imaging, enabling high-resolution near diffraction-limited imaging of previously unresolvable structures1,2. In ophthalmology, when combined with optical coherence tomography, HAO has enabled a detailed three-dimensional visualization of photoreceptor distributions3,4 and individual nerve fibre bundles5 in the living human retina. However, the introduction of HAO hardware and supporting software adds considerable complexity and cost to an imaging system, limiting the number of researchers and medical professionals who could benefit from the technology. Here we demonstrate a fully automated computational approach that enables high-resolution in vivo ophthalmic imaging without the need for HAO. The results demonstrate that computational methods in coherent microscopy are applicable in highly dynamic living systems.

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Figure 1: Computational aberration correction and imaging of the living human retina.
Figure 2: Computational wavefront correction.
Figure 3: Quantitative stability analysis and correction.

Change history

  • 10 September 2015

    The authors acknowledge that two highly relevant manuscripts should have been cited in this Letter: Meitav, N. & Ribak, E. N. Improving retinal image resolution with iterative weighted shift-and-add. J. Opt. Soc. Am. A 28, 1395–1402 (2011) Meitav, N. & Ribak, E. N. Estimation of the ocular point spread function by retina modeling. Opt Lett. 37, 1466–1468 (2012) These manuscripts report progress towards in vivo high-resolution retinal imaging without using hardware-based adaptive optics by averaging out high-order, temporally changing aberrations, and by applying various image filters to the intensity of backscattered optical signals..


  1. Liang, J., Williams, D. R. & Miller, D. T. Supernormal vision and high-resolution retinal imaging through adaptive optics. J. Opt. Soc. Am. A 14, 2884–2892 (1997).

    Article  ADS  Google Scholar 

  2. Hermann, B. et al. Adaptive-optics ultrahigh-resolution optical coherence tomography. Opt. Express 29, 2142–2144 (2004).

    ADS  Google Scholar 

  3. Zhang, Y. et al. High-speed volumetric imaging of cone photoreceptors with adaptive optics spectral-domain optical coherence tomography. Opt. Express 14, 4380–4394 (2006).

    Article  ADS  Google Scholar 

  4. Felberer, F. et al. Adaptive optics SLO/OCT for 3D imaging of human photoreceptors in vivo. Biomed. Opt. Express 5, 439–456 (2014).

    Article  Google Scholar 

  5. Kocaoglu, O. P. et al. Imaging retinal nerve fiber bundles using optical coherence tomography with adaptive optics. Vision Res. 51, 1835–1844 (2011).

    Article  Google Scholar 

  6. Huang, D. et al. Optical coherence tomography. Science 254, 1178–1181 (1991).

    Article  ADS  Google Scholar 

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

    Book  Google Scholar 

  8. Sánchez-Tocino, H. et al. Retinal thickness study with optical coherence tomography in patients with diabetes. Invest. Ophthalmol. Vis. Sci. 43, 1588–1594 (2002).

    Google Scholar 

  9. Saidha, S. et al. Primary retinal pathology in multiple sclerosis as detected by optical coherence tomography. Brain 134, 518–533 (2011).

    Article  Google Scholar 

  10. Iglesias, I. & Artal, P. High-resolution retinal images obtained by deconvolution from wave-front sensing. Opt. Lett. 25, 1804–1806 (2000).

    Article  ADS  Google Scholar 

  11. Christou, J. C., Roorda, A. & Williams, D. R. Deconvolution of adaptive optics retinal images. J. Opt. Soc. Am. A 21, 1393–1401 (2004).

    Article  ADS  Google Scholar 

  12. Ralston, T. S., Marks, D. L., Carney, P. S. & Boppart, S. A. Interferometric synthetic aperture microscopy. Nature Phys. 3, 129–134 (2007).

    Article  ADS  Google Scholar 

  13. Adie, S. G., Graf, B. W., Ahmad, A., Carney, P. S. & Boppart, S. A. Computational adaptive optics for broadband optical interferometric tomography of biological tissue. Proc. Natl Acad. Sci. USA 109, 7175–7180 (2012).

    Article  ADS  Google Scholar 

  14. Kumar, A., Drexler, W. & Leitgeb, R. A. Subaperture correlation based digital adaptive optics for full field optical coherence tomography. Opt. Express 21, 10850–10866 (2013).

    Article  ADS  Google Scholar 

  15. Adie, S. G. et al. Guide-star-based computational adaptive optics for broadband interferometric tomography. Appl. Phys. Lett. 101, 221117 (2012).

    Article  ADS  Google Scholar 

  16. Shemonski, N. D. et al. Stability in computed optical interferometric tomography (part I): stability requirements. Opt. Express 22, 19183–19197 (2014).

    Article  ADS  Google Scholar 

  17. Ralston, T. S., Marks, D. L., Carney, P. S. & Boppart, S. A. Phase stability technique for inverse scattering in optical coherence tomography. Proc. 3rd IEEE Int. Symp. on Biomed. Imaging: Nano to Macro 578–581 (2006).

    Google Scholar 

  18. Ahmad, A. et al. Real-time in vivo computed optical interferometric tomography. Nature Photon. 7, 444–448 (2013).

    Article  ADS  Google Scholar 

  19. Shemonski, N. D. et al. Stability in computed optical interferometric tomography (part II): in vivo stability assessment. Opt. Express 22, 19314–19326 (2014).

    Article  ADS  Google Scholar 

  20. Liu, Y.-Z. et al. Computed optical interferometric tomography for high-speed volumetric cellular imaging. Biomed. Opt. Express 5, 2988–3000 (2014).

    Article  Google Scholar 

  21. Martinez-Conde, S., Macknik, S. L. & Hubel, D. H. The role of fixational eye movements in visual perception. Nature Rev. Neurosci. 5, 229–240 (2004).

    Article  Google Scholar 

  22. Curcio, C. A., Sloan, K. R., Kalina, R. E. & Hendrickson, A. E. Human photoreceptor topography. J. Comp. Neurol. 292, 497–523 (1990).

    Article  Google Scholar 

  23. Yadlowsky, M. J., Schmitt, J. M. & Bonner, R. F. Multiple scattering in optical coherence microscopy. Appl. Opt. 34, 5699–5707 (1995).

    Article  ADS  Google Scholar 

  24. Castejon-Mochon, J. F., Lopez-Gil, N., Benito, A. & Artal, P. Ocular wave-front aberration statistics in a normal young population. Vision Res. 42, 1611–1617 (2002).

    Article  Google Scholar 

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The authors thank D. Spillman and E. Chaney from the Beckman Institute for Advanced Science and Technology for their assistance with operations and human study protocol support, respectively. This research was supported in part by grants from the National Institutes of Health (NIBIB, 1 R01 EB013723, 1 R01 EB012479) and the National Science Foundation (CBET 14-45111).

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Authors and Affiliations



N.D.S. constructed the optical system, collected data, analysed data and wrote the paper. F.A.S., Y-Z.L, and S.G.A. collected and analysed data, and assisted in writing the paper. P.S.C. contributed the theoretical and mathematical basis for these methods, reviewed and edited the manuscript, and helped obtain funding. S.A.B. conceived of the study, analysed the data, reviewed and edited the manuscript and helped obtain funding.

Corresponding author

Correspondence to Stephen A. Boppart.

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Competing interests

S.A.B. and P.S.C. are co-founders of Diagnostic Photonics, which is licensing intellectual property from the University of Illinois at Urbana-Champaign related to interferometric synthetic aperture microscopy. S.A.B. also receives royalties from the Massachusetts Institute of Technology for patents related to optical coherence tomography. S.G.A., P.S.C. and S.A.B. are listed as inventors on a patent application (application no. 20140050382) related to the work presented in this manuscript. All other authors have nothing to disclose.

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10.1038/nrrheum.2015.85 (PDF 1540 kb)

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Shemonski, N., South, F., Liu, YZ. et al. Computational high-resolution optical imaging of the living human retina. Nature Photon 9, 440–443 (2015).

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