Abstract

Non-invasive, real-time imaging and deep focus into tissue are in high demand in biomedical research. However, the aberration that is introduced by the refractive index inhomogeneity of biological tissue hinders the way forward. A rapid focusing with sensor-less aberration corrections, based on machine learning, is demonstrated in this paper. The proposed method applies the Convolutional Neural Network (CNN), which can rapidly calculate the low-order aberrations from the point spread function images with Zernike modes after training. The results show that approximately 90 percent correction accuracy can be achieved. The average mean square error of each Zernike coefficient in 200 repetitions is 0.06. Furthermore, the aberration induced by 1-mm-thick phantom samples and 300-µm-thick mouse brain slices can be efficiently compensated through loading a compensation phase on an adaptive element placed at the back-pupil plane. The phase reconstruction requires less than 0.2 s. Therefore, this method offers great potential for in vivo real-time imaging in biological science.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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  1. N. Ji, “Adaptive optical fluorescence microscopy,” Nat. Methods 14(4), 374–380 (2017).
    [Crossref] [PubMed]
  2. W. Yang and R. Yuste, “In vivo imaging of neural activity,” Nat. Methods 14(4), 349–359 (2017).
    [Crossref] [PubMed]
  3. R. Horstmeyer, H. Ruan, and C. Yang, “Guidestar-assisted wavefront-shaping methods for focusing light into biological tissue,” Nat. Photonics 9(9), 563–571 (2015).
    [Crossref] [PubMed]
  4. C. Rodríguez and N. Ji, “Adaptive optical microscopy for neurobiology,” Curr. Opin. Neurobiol. 50, 83–91 (2018).
    [Crossref] [PubMed]
  5. M. J. Booth, “Adaptive optical microscopy: the ongoing quest for a perfect image,” Light Sci. Appl. 3(4), e165 (2014).
    [Crossref]
  6. J. W. Hardy and L. Thompson, “Adaptive optics for astronomical telescopes,” Phys. Today 53(4), 69 (2000).
    [Crossref]
  7. W. H. Southwell, “Wave-front estimation from wave-front slope measurements,” J. Opt. Soc. Am. 70(8), 998–1006 (1980).
    [Crossref]
  8. J.-W. Cha, J. Ballesta, and P. T. C. So, “Shack-Hartmann wavefront-sensor-based adaptive optics system for multiphoton microscopy,” J. Biomed. Opt. 15(4), 046022 (2010).
    [Crossref] [PubMed]
  9. K. Wang, W. Sun, C. T. Richie, B. K. Harvey, E. Betzig, and N. Ji, “Direct wavefront sensing for high-resolution in vivo imaging in scattering tissue,” Nat. Commun. 6(1), 7276 (2015).
    [Crossref] [PubMed]
  10. P. Yang, Y. Liu, M. Ao, S. Hu, and B. Xu, “A wavefront sensor-less adaptive optical system for a solid-state laser,” Opt. Lasers Eng. 46(7), 517–521 (2008).
    [Crossref]
  11. N. Ji, D. E. Milkie, and E. Betzig, “Adaptive optics via pupil segmentation for high-resolution imaging in biological tissues,” Nat. Methods 7(2), 141–147 (2010).
    [Crossref] [PubMed]
  12. R. Liu, D. E. Milkie, A. Kerlin, B. MacLennan, and N. Ji, “Direct phase measurement in zonal wavefront reconstruction using multidither coherent optical adaptive technique,” Opt. Express 22(2), 1619–1628 (2014).
    [Crossref] [PubMed]
  13. Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4(11), 1437–1443 (2017).
    [Crossref]
  14. W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36(5), 460–468 (2018).
    [Crossref] [PubMed]
  15. J. L. M. Fuentes, E. J. Fernández, P. M. Prieto, and P. Artal, “Interferometric method for phase calibration in liquid crystal spatial light modulators using a self-generated diffraction-grating,” Opt. Express 24(13), 14159–14171 (2016).
    [Crossref] [PubMed]
  16. M. A. Neil, M. J. Booth, and T. Wilson, “Closed-loop aberration correction by use of a modal Zernike wave-front sensor,” Opt. Lett. 25(15), 1083–1085 (2000).
    [Crossref] [PubMed]
  17. J. Schmidhuber, “Deep learning in neural networks: an overview,” Neural Netw. 61, 85–117 (2015).
    [Crossref] [PubMed]
  18. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
    [Crossref]
  19. K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Trans. Image Process. 26(9), 4509–4522 (2017).
    [Crossref] [PubMed]
  20. S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” In Proceedings of the IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 514–517.
    [Crossref]
  21. S. Antholzer, M. Haltmeier, and J. Schwab, “Deep learning for photoacoustic tomography from sparse data,” Inverse Probl. Sci. Eng. 1, 1–19 (2018).
    [Crossref]
  22. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” In Advances in Neural Information Processing Systems (2012), pp. 1097–1105.

2018 (3)

C. Rodríguez and N. Ji, “Adaptive optical microscopy for neurobiology,” Curr. Opin. Neurobiol. 50, 83–91 (2018).
[Crossref] [PubMed]

W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36(5), 460–468 (2018).
[Crossref] [PubMed]

S. Antholzer, M. Haltmeier, and J. Schwab, “Deep learning for photoacoustic tomography from sparse data,” Inverse Probl. Sci. Eng. 1, 1–19 (2018).
[Crossref]

2017 (4)

K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Trans. Image Process. 26(9), 4509–4522 (2017).
[Crossref] [PubMed]

Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4(11), 1437–1443 (2017).
[Crossref]

N. Ji, “Adaptive optical fluorescence microscopy,” Nat. Methods 14(4), 374–380 (2017).
[Crossref] [PubMed]

W. Yang and R. Yuste, “In vivo imaging of neural activity,” Nat. Methods 14(4), 349–359 (2017).
[Crossref] [PubMed]

2016 (1)

2015 (3)

J. Schmidhuber, “Deep learning in neural networks: an overview,” Neural Netw. 61, 85–117 (2015).
[Crossref] [PubMed]

R. Horstmeyer, H. Ruan, and C. Yang, “Guidestar-assisted wavefront-shaping methods for focusing light into biological tissue,” Nat. Photonics 9(9), 563–571 (2015).
[Crossref] [PubMed]

K. Wang, W. Sun, C. T. Richie, B. K. Harvey, E. Betzig, and N. Ji, “Direct wavefront sensing for high-resolution in vivo imaging in scattering tissue,” Nat. Commun. 6(1), 7276 (2015).
[Crossref] [PubMed]

2014 (2)

2010 (2)

J.-W. Cha, J. Ballesta, and P. T. C. So, “Shack-Hartmann wavefront-sensor-based adaptive optics system for multiphoton microscopy,” J. Biomed. Opt. 15(4), 046022 (2010).
[Crossref] [PubMed]

N. Ji, D. E. Milkie, and E. Betzig, “Adaptive optics via pupil segmentation for high-resolution imaging in biological tissues,” Nat. Methods 7(2), 141–147 (2010).
[Crossref] [PubMed]

2008 (1)

P. Yang, Y. Liu, M. Ao, S. Hu, and B. Xu, “A wavefront sensor-less adaptive optical system for a solid-state laser,” Opt. Lasers Eng. 46(7), 517–521 (2008).
[Crossref]

2000 (2)

1998 (1)

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

1980 (1)

Antholzer, S.

S. Antholzer, M. Haltmeier, and J. Schwab, “Deep learning for photoacoustic tomography from sparse data,” Inverse Probl. Sci. Eng. 1, 1–19 (2018).
[Crossref]

Ao, M.

P. Yang, Y. Liu, M. Ao, S. Hu, and B. Xu, “A wavefront sensor-less adaptive optical system for a solid-state laser,” Opt. Lasers Eng. 46(7), 517–521 (2008).
[Crossref]

Aristov, A.

W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36(5), 460–468 (2018).
[Crossref] [PubMed]

Artal, P.

Ballesta, J.

J.-W. Cha, J. Ballesta, and P. T. C. So, “Shack-Hartmann wavefront-sensor-based adaptive optics system for multiphoton microscopy,” J. Biomed. Opt. 15(4), 046022 (2010).
[Crossref] [PubMed]

Bengio, Y.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

Betzig, E.

K. Wang, W. Sun, C. T. Richie, B. K. Harvey, E. Betzig, and N. Ji, “Direct wavefront sensing for high-resolution in vivo imaging in scattering tissue,” Nat. Commun. 6(1), 7276 (2015).
[Crossref] [PubMed]

N. Ji, D. E. Milkie, and E. Betzig, “Adaptive optics via pupil segmentation for high-resolution imaging in biological tissues,” Nat. Methods 7(2), 141–147 (2010).
[Crossref] [PubMed]

Booth, M. J.

Bottou, L.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

Cha, J.-W.

J.-W. Cha, J. Ballesta, and P. T. C. So, “Shack-Hartmann wavefront-sensor-based adaptive optics system for multiphoton microscopy,” J. Biomed. Opt. 15(4), 046022 (2010).
[Crossref] [PubMed]

Feng, D.

S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” In Proceedings of the IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 514–517.
[Crossref]

Fernández, E. J.

Froustey, E.

K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Trans. Image Process. 26(9), 4509–4522 (2017).
[Crossref] [PubMed]

Fuentes, J. L. M.

Göröcs, Z.

Günaydin, H.

Haffner, P.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

Haltmeier, M.

S. Antholzer, M. Haltmeier, and J. Schwab, “Deep learning for photoacoustic tomography from sparse data,” Inverse Probl. Sci. Eng. 1, 1–19 (2018).
[Crossref]

Hao, X.

W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36(5), 460–468 (2018).
[Crossref] [PubMed]

Hardy, J. W.

J. W. Hardy and L. Thompson, “Adaptive optics for astronomical telescopes,” Phys. Today 53(4), 69 (2000).
[Crossref]

Harvey, B. K.

K. Wang, W. Sun, C. T. Richie, B. K. Harvey, E. Betzig, and N. Ji, “Direct wavefront sensing for high-resolution in vivo imaging in scattering tissue,” Nat. Commun. 6(1), 7276 (2015).
[Crossref] [PubMed]

Horstmeyer, R.

R. Horstmeyer, H. Ruan, and C. Yang, “Guidestar-assisted wavefront-shaping methods for focusing light into biological tissue,” Nat. Photonics 9(9), 563–571 (2015).
[Crossref] [PubMed]

Hu, S.

P. Yang, Y. Liu, M. Ao, S. Hu, and B. Xu, “A wavefront sensor-less adaptive optical system for a solid-state laser,” Opt. Lasers Eng. 46(7), 517–521 (2008).
[Crossref]

Ji, N.

C. Rodríguez and N. Ji, “Adaptive optical microscopy for neurobiology,” Curr. Opin. Neurobiol. 50, 83–91 (2018).
[Crossref] [PubMed]

N. Ji, “Adaptive optical fluorescence microscopy,” Nat. Methods 14(4), 374–380 (2017).
[Crossref] [PubMed]

K. Wang, W. Sun, C. T. Richie, B. K. Harvey, E. Betzig, and N. Ji, “Direct wavefront sensing for high-resolution in vivo imaging in scattering tissue,” Nat. Commun. 6(1), 7276 (2015).
[Crossref] [PubMed]

R. Liu, D. E. Milkie, A. Kerlin, B. MacLennan, and N. Ji, “Direct phase measurement in zonal wavefront reconstruction using multidither coherent optical adaptive technique,” Opt. Express 22(2), 1619–1628 (2014).
[Crossref] [PubMed]

N. Ji, D. E. Milkie, and E. Betzig, “Adaptive optics via pupil segmentation for high-resolution imaging in biological tissues,” Nat. Methods 7(2), 141–147 (2010).
[Crossref] [PubMed]

Jin, K. H.

K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Trans. Image Process. 26(9), 4509–4522 (2017).
[Crossref] [PubMed]

Kerlin, A.

Lecun, Y.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

Lelek, M.

W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36(5), 460–468 (2018).
[Crossref] [PubMed]

Liang, D.

S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” In Proceedings of the IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 514–517.
[Crossref]

Liang, F.

S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” In Proceedings of the IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 514–517.
[Crossref]

Liu, R.

Liu, Y.

P. Yang, Y. Liu, M. Ao, S. Hu, and B. Xu, “A wavefront sensor-less adaptive optical system for a solid-state laser,” Opt. Lasers Eng. 46(7), 517–521 (2008).
[Crossref]

MacLennan, B.

McCann, M. T.

K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Trans. Image Process. 26(9), 4509–4522 (2017).
[Crossref] [PubMed]

Milkie, D. E.

R. Liu, D. E. Milkie, A. Kerlin, B. MacLennan, and N. Ji, “Direct phase measurement in zonal wavefront reconstruction using multidither coherent optical adaptive technique,” Opt. Express 22(2), 1619–1628 (2014).
[Crossref] [PubMed]

N. Ji, D. E. Milkie, and E. Betzig, “Adaptive optics via pupil segmentation for high-resolution imaging in biological tissues,” Nat. Methods 7(2), 141–147 (2010).
[Crossref] [PubMed]

Neil, M. A.

Ouyang, W.

W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36(5), 460–468 (2018).
[Crossref] [PubMed]

Ozcan, A.

Peng, X.

S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” In Proceedings of the IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 514–517.
[Crossref]

Prieto, P. M.

Richie, C. T.

K. Wang, W. Sun, C. T. Richie, B. K. Harvey, E. Betzig, and N. Ji, “Direct wavefront sensing for high-resolution in vivo imaging in scattering tissue,” Nat. Commun. 6(1), 7276 (2015).
[Crossref] [PubMed]

Rivenson, Y.

Rodríguez, C.

C. Rodríguez and N. Ji, “Adaptive optical microscopy for neurobiology,” Curr. Opin. Neurobiol. 50, 83–91 (2018).
[Crossref] [PubMed]

Ruan, H.

R. Horstmeyer, H. Ruan, and C. Yang, “Guidestar-assisted wavefront-shaping methods for focusing light into biological tissue,” Nat. Photonics 9(9), 563–571 (2015).
[Crossref] [PubMed]

Schmidhuber, J.

J. Schmidhuber, “Deep learning in neural networks: an overview,” Neural Netw. 61, 85–117 (2015).
[Crossref] [PubMed]

Schwab, J.

S. Antholzer, M. Haltmeier, and J. Schwab, “Deep learning for photoacoustic tomography from sparse data,” Inverse Probl. Sci. Eng. 1, 1–19 (2018).
[Crossref]

So, P. T. C.

J.-W. Cha, J. Ballesta, and P. T. C. So, “Shack-Hartmann wavefront-sensor-based adaptive optics system for multiphoton microscopy,” J. Biomed. Opt. 15(4), 046022 (2010).
[Crossref] [PubMed]

Southwell, W. H.

Su, Z.

S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” In Proceedings of the IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 514–517.
[Crossref]

Sun, W.

K. Wang, W. Sun, C. T. Richie, B. K. Harvey, E. Betzig, and N. Ji, “Direct wavefront sensing for high-resolution in vivo imaging in scattering tissue,” Nat. Commun. 6(1), 7276 (2015).
[Crossref] [PubMed]

Thompson, L.

J. W. Hardy and L. Thompson, “Adaptive optics for astronomical telescopes,” Phys. Today 53(4), 69 (2000).
[Crossref]

Unser, M.

K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Trans. Image Process. 26(9), 4509–4522 (2017).
[Crossref] [PubMed]

Wang, H.

Wang, K.

K. Wang, W. Sun, C. T. Richie, B. K. Harvey, E. Betzig, and N. Ji, “Direct wavefront sensing for high-resolution in vivo imaging in scattering tissue,” Nat. Commun. 6(1), 7276 (2015).
[Crossref] [PubMed]

Wang, S.

S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” In Proceedings of the IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 514–517.
[Crossref]

Wilson, T.

Xu, B.

P. Yang, Y. Liu, M. Ao, S. Hu, and B. Xu, “A wavefront sensor-less adaptive optical system for a solid-state laser,” Opt. Lasers Eng. 46(7), 517–521 (2008).
[Crossref]

Yang, C.

R. Horstmeyer, H. Ruan, and C. Yang, “Guidestar-assisted wavefront-shaping methods for focusing light into biological tissue,” Nat. Photonics 9(9), 563–571 (2015).
[Crossref] [PubMed]

Yang, P.

P. Yang, Y. Liu, M. Ao, S. Hu, and B. Xu, “A wavefront sensor-less adaptive optical system for a solid-state laser,” Opt. Lasers Eng. 46(7), 517–521 (2008).
[Crossref]

Yang, W.

W. Yang and R. Yuste, “In vivo imaging of neural activity,” Nat. Methods 14(4), 349–359 (2017).
[Crossref] [PubMed]

Ying, L.

S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” In Proceedings of the IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 514–517.
[Crossref]

Yuste, R.

W. Yang and R. Yuste, “In vivo imaging of neural activity,” Nat. Methods 14(4), 349–359 (2017).
[Crossref] [PubMed]

Zhang, Y.

Zhu, S.

S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” In Proceedings of the IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 514–517.
[Crossref]

Zimmer, C.

W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36(5), 460–468 (2018).
[Crossref] [PubMed]

Curr. Opin. Neurobiol. (1)

C. Rodríguez and N. Ji, “Adaptive optical microscopy for neurobiology,” Curr. Opin. Neurobiol. 50, 83–91 (2018).
[Crossref] [PubMed]

IEEE Trans. Image Process. (1)

K. H. Jin, M. T. McCann, E. Froustey, and M. Unser, “Deep convolutional neural network for inverse problems in imaging,” IEEE Trans. Image Process. 26(9), 4509–4522 (2017).
[Crossref] [PubMed]

Inverse Probl. Sci. Eng. (1)

S. Antholzer, M. Haltmeier, and J. Schwab, “Deep learning for photoacoustic tomography from sparse data,” Inverse Probl. Sci. Eng. 1, 1–19 (2018).
[Crossref]

J. Biomed. Opt. (1)

J.-W. Cha, J. Ballesta, and P. T. C. So, “Shack-Hartmann wavefront-sensor-based adaptive optics system for multiphoton microscopy,” J. Biomed. Opt. 15(4), 046022 (2010).
[Crossref] [PubMed]

J. Opt. Soc. Am. (1)

Light Sci. Appl. (1)

M. J. Booth, “Adaptive optical microscopy: the ongoing quest for a perfect image,” Light Sci. Appl. 3(4), e165 (2014).
[Crossref]

Nat. Biotechnol. (1)

W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36(5), 460–468 (2018).
[Crossref] [PubMed]

Nat. Commun. (1)

K. Wang, W. Sun, C. T. Richie, B. K. Harvey, E. Betzig, and N. Ji, “Direct wavefront sensing for high-resolution in vivo imaging in scattering tissue,” Nat. Commun. 6(1), 7276 (2015).
[Crossref] [PubMed]

Nat. Methods (3)

N. Ji, D. E. Milkie, and E. Betzig, “Adaptive optics via pupil segmentation for high-resolution imaging in biological tissues,” Nat. Methods 7(2), 141–147 (2010).
[Crossref] [PubMed]

N. Ji, “Adaptive optical fluorescence microscopy,” Nat. Methods 14(4), 374–380 (2017).
[Crossref] [PubMed]

W. Yang and R. Yuste, “In vivo imaging of neural activity,” Nat. Methods 14(4), 349–359 (2017).
[Crossref] [PubMed]

Nat. Photonics (1)

R. Horstmeyer, H. Ruan, and C. Yang, “Guidestar-assisted wavefront-shaping methods for focusing light into biological tissue,” Nat. Photonics 9(9), 563–571 (2015).
[Crossref] [PubMed]

Neural Netw. (1)

J. Schmidhuber, “Deep learning in neural networks: an overview,” Neural Netw. 61, 85–117 (2015).
[Crossref] [PubMed]

Opt. Express (2)

Opt. Lasers Eng. (1)

P. Yang, Y. Liu, M. Ao, S. Hu, and B. Xu, “A wavefront sensor-less adaptive optical system for a solid-state laser,” Opt. Lasers Eng. 46(7), 517–521 (2008).
[Crossref]

Opt. Lett. (1)

Optica (1)

Phys. Today (1)

J. W. Hardy and L. Thompson, “Adaptive optics for astronomical telescopes,” Phys. Today 53(4), 69 (2000).
[Crossref]

Proc. IEEE (1)

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE 86(11), 2278–2324 (1998).
[Crossref]

Other (2)

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” In Advances in Neural Information Processing Systems (2012), pp. 1097–1105.

S. Wang, Z. Su, L. Ying, X. Peng, S. Zhu, F. Liang, D. Feng, and D. Liang, “Accelerating magnetic resonance imaging via deep learning,” In Proceedings of the IEEE International Symposium on Biomedical Imaging (IEEE, 2016), pp. 514–517.
[Crossref]

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Figures (6)

Fig. 1
Fig. 1 The schematic diagram of the machine learning guided fast AO system. A 637nm laser beam is filtered by a pinhole and expanded by telescope system L3-L4 before passing through a half-wave plate, a PBS, and a polarizer sequentially. After that, the laser beam projects and reflects perpendicularly to the SLM plane by mounting a beam splitter (BS) at the front of SLM. L5-L6 forms a relay system which conjugates the SLM to the back-pupil plane of the objective OBJ1. The objective OBJ2 is used to collect light information and the point spread function can be detected by a CMOS camera through a relay lens L7. (L, lens; M, mirror; OBJ1 and OBJ2, both are objective lenses (RMS4X, Olympus, 4X / 0.10 NA); PH, pinhole; AP, aperture; HWP, half-wave plate; PBS, polarized beam splitter; BS, non-polarizing beam splitter; P, linear polarizer; SLM, spatial light modulator).
Fig. 2
Fig. 2 The principle of machine learning guided fast AO correction method. (a) Expression and name of each Zernike mode from 1st to 15th. (b) Flowchart of the machine learning algorithm.
Fig. 3
Fig. 3 (a) Description of Tip-tilt correction. Transparent red point spread function is at the ideal position and red point spread function has a dx and dy displacement in horizontal and vertical directions relative to the ideal position. (b) The network architecture of the training model based on Alexnet. (c) Radar map of Mean Square Error (RMSE) about calculated Zernike coefficient with KNN, ELM, MLP, and CNN, respectively.
Fig. 4
Fig. 4 Four groups of results in 200 repetitions compensating for random phase-masks. (a) Four groups of the point spread functions before (left) and after (right) correction at the focal plane gained by CMOS camera and the intensity is normalized. (b) The comparison of Zernike coefficient amplitudes between phase-mask (in blue bars) and reconstructed phase (in orange bars). (c) is the reconstructed phase pattern loaded on SLM for each group. (d) The intensity profile at the center section of Airy (ideal spot), NO AO (without AO corrected point spread function), T-T corr (tip-tilt corrected point spread function) and ML-AO (machine learning guided AO corrected point spread function) of four groups mentioned in (a).
Fig. 5
Fig. 5 Experimental compensation results of the 1-mm-thick phantom slice. (a)–(c) Point spread functions scattered by three different areas in a 1-mm-thick phantom sample (up) and corrected by our machine learning guided AO system (down). Inside the colored dotted boxes are the enlarged views of each point spread function. (d) Section intensity profile of the point spread functions without correction (NO AO), after tip-tilt correction (T-T corr) and after machine learning fully correction (ML-AO). The scale bar in (a)–(c) is 100 μm.
Fig. 6
Fig. 6 Experiment compensation results of 300-µm-thick mouse brain slices. (a)–(b) are two typical scattered (NO AO) and corrected (ML-AO) point spread functions. The corresponding blue-dashed and magenta-dashed ROI are enlarged as below. (c)–(d) Intensity profile at the center of the point spread function before and after correction (indicated with blue and magenta arrows respectively). (e)–(f) Amplitude distribution of Zernike coefficients calculated with our method. The inserted pictures demonstrate the compensate phase pattern loaded on SLM. The scale bar in (a)–(b) is 100 μm.

Equations (4)

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ψ p h a s e ( x , y ) = a 1 Z 1 ( x , y ) + a 2 Z 2 ( x , y ) + a 3 Z 3 ( x , y ) + + a 10 Z 10 ( x , y ) +
a 2 = π d x λ f D 2
a 3 = π d y λ f D 2
f ( x ) = max ( 0 , x ) ,

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