Abstract

The doughnut-shaped beam has been widely applied in the field of super-resolution microscopic imaging, micro-nanostructure lithography, ultra-high-density storage, and laser trapping. However, how to maintain the doughnut-shaped focus inside the scattering medium becomes a challenge, due to the wavefront aberrations. Here we demonstrate a machine learning based adaptive optics method to recover the doughnut-shaped focus with high speed. In our method, the relationship between the distorted doughnut-shaped intensity point spread function and the coefficients of the first 15 Zernike modes for phase correction is established. Experimental results show that the wavefront aberration with 101,784 optical control elements can be predicted within ~17 ms even using a personal computer, and 97.5% correction accuracy can be achieved in 200 repeated tests. Besides, we successfully apply this method in the scanning microscopy theoretically. With a large number of optical control elements and fast operation speed, our method may pave the way for many important applications in bioimaging, such as deep tissue stimulated emission depletion (STED) microscopy.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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    [Crossref] [PubMed]

2018 (4)

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]

E. Nehme, L. E. Weiss, T. Michaeli, and Y. Shechtman, “Deep-STORM: super-resolution single-molecule microscopy by deep learning,” Optica 5(4), 458–464 (2018).
[Crossref]

Y. Jin, Y. Zhang, L. Hu, H. Huang, Q. Xu, X. Zhu, L. Huang, Y. Zheng, H.-L. Shen, W. Gong, and K. Si, “Machine learning guided rapid focusing with sensor-less aberration corrections,” Opt. Express 26(23), 30162–30171 (2018).
[Crossref] [PubMed]

Q. Zhao, X. Shi, X. Zhu, Y. Zheng, C. Wu, H. Tang, L. Hu, Y. Xue, W. Gong, and K. Si, “Large field of view correction by using conjugate adaptive optics with multiple guide stars,” J. Biophotonics 12, e201800225 (2018).
[PubMed]

2017 (2)

2016 (1)

2015 (3)

M. Booth, D. Andrade, D. Burke, B. Patton, and M. Zurauskas, “Aberrations and adaptive optics in super-resolution microscopy,” Microscopy (Oxf.) 64(4), 251–261 (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]

T. W. Wu and M. Cui, “Numerical study of multi-conjugate large area wavefront correction for deep tissue microscopy,” Opt. Express 23(6), 7463–7470 (2015).
[Crossref] [PubMed]

2014 (2)

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

M. O. Lenz, H. G. Sinclair, A. Savell, J. H. Clegg, A. C. Brown, D. M. Davis, C. Dunsby, M. A. Neil, and P. M. French, “3-D stimulated emission depletion microscopy with programmable aberration correction,” J. Biophotonics 7(1-2), 29–36 (2014).
[Crossref] [PubMed]

2013 (1)

B. Harke, W. Dallari, G. Grancini, D. Fazzi, F. Brandi, A. Petrozza, and A. Diaspro, “Polymerization inhibition by triplet state absorption for nanoscale lithography,” Adv. Mater. 25(6), 904–909 (2013).
[Crossref] [PubMed]

2012 (4)

Z. Gan, Y. Cao, B. Jia, and M. Gu, “Dynamic modeling of superresolution photoinduced-inhibition nanolithography,” Opt. Express 20(15), 16871–16879 (2012).
[Crossref]

X. Li, T. H. Lan, C. H. Tien, and M. Gu, “Three-dimensional orientation-unlimited polarization encryption by a single optically configured vectorial beam,” Nat. Commun. 3(1), 998 (2012).
[Crossref] [PubMed]

T. J. Gould, D. Burke, J. Bewersdorf, and M. J. Booth, “Adaptive optics enables 3D STED microscopy in aberrating specimens,” Opt. Express 20(19), 20998–21009 (2012).
[Crossref] [PubMed]

K. Si, R. Fiolka, and M. Cui, “Fluorescence imaging beyond the ballistic regime by ultrasound pulse guided digital phase conjugation,” Nat. Photonics 6(10), 657–661 (2012).
[Crossref] [PubMed]

2011 (1)

2010 (1)

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 (2)

E. Auksorius, B. R. Boruah, C. Dunsby, P. M. Lanigan, G. Kennedy, M. A. Neil, and P. M. French, “Stimulated emission depletion microscopy with a supercontinuum source and fluorescence lifetime imaging,” Opt. Lett. 33(2), 113–115 (2008).
[Crossref] [PubMed]

B. Hein, K. I. Willig, and S. W. Hell, “Stimulated emission depletion (STED) nanoscopy of a fluorescent protein-labeled organelle inside a living cell,” Proc. Natl. Acad. Sci. U.S.A. 105(38), 14271–14276 (2008).
[Crossref] [PubMed]

2004 (1)

2000 (1)

T. A. Klar, S. Jakobs, M. Dyba, A. Egner, and S. W. Hell, “Fluorescence microscopy with diffraction resolution barrier broken by stimulated emission,” Proc. Natl. Acad. Sci. U.S.A. 97(15), 8206–8210 (2000).
[Crossref] [PubMed]

1999 (1)

1994 (1)

1991 (1)

H. Misawa, M. Koshioka, K. Sasaki, N. Kitamura, and H. Masuhara, “Three‐dimensional optical trapping and laser ablation of a single polymer latex particle in water,” J. Appl. Phys. 70(7), 3829–3836 (1991).
[Crossref]

1972 (1)

S. Nyberg, “Optical Determination of the Correlation Coefficient,” Opt. Acta (Lond.) 19(3), 195–201 (1972).
[Crossref]

Andrade, D.

M. Booth, D. Andrade, D. Burke, B. Patton, and M. Zurauskas, “Aberrations and adaptive optics in super-resolution microscopy,” Microscopy (Oxf.) 64(4), 251–261 (2015).
[Crossref] [PubMed]

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]

Auksorius, E.

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]

Bewersdorf, J.

Blanc-Féraud, L.

S. Gazagnes, E. Soubies, and L. Blanc-Féraud, “High density molecule localization for super-resolution microscopy using CEL0 based sparse approximation,” in 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (IEEE, 2017), pp. 28–31.
[Crossref]

Booth, M.

M. Booth, D. Andrade, D. Burke, B. Patton, and M. Zurauskas, “Aberrations and adaptive optics in super-resolution microscopy,” Microscopy (Oxf.) 64(4), 251–261 (2015).
[Crossref] [PubMed]

Booth, M. J.

Boruah, B. R.

Brandi, F.

B. Harke, W. Dallari, G. Grancini, D. Fazzi, F. Brandi, A. Petrozza, and A. Diaspro, “Polymerization inhibition by triplet state absorption for nanoscale lithography,” Adv. Mater. 25(6), 904–909 (2013).
[Crossref] [PubMed]

Brown, A. C.

M. O. Lenz, H. G. Sinclair, A. Savell, J. H. Clegg, A. C. Brown, D. M. Davis, C. Dunsby, M. A. Neil, and P. M. French, “3-D stimulated emission depletion microscopy with programmable aberration correction,” J. Biophotonics 7(1-2), 29–36 (2014).
[Crossref] [PubMed]

Burke, D.

Cao, Y.

Chen, X.

Clegg, J. H.

M. O. Lenz, H. G. Sinclair, A. Savell, J. H. Clegg, A. C. Brown, D. M. Davis, C. Dunsby, M. A. Neil, and P. M. French, “3-D stimulated emission depletion microscopy with programmable aberration correction,” J. Biophotonics 7(1-2), 29–36 (2014).
[Crossref] [PubMed]

Cui, M.

Dallari, W.

B. Harke, W. Dallari, G. Grancini, D. Fazzi, F. Brandi, A. Petrozza, and A. Diaspro, “Polymerization inhibition by triplet state absorption for nanoscale lithography,” Adv. Mater. 25(6), 904–909 (2013).
[Crossref] [PubMed]

Davis, D. M.

M. O. Lenz, H. G. Sinclair, A. Savell, J. H. Clegg, A. C. Brown, D. M. Davis, C. Dunsby, M. A. Neil, and P. M. French, “3-D stimulated emission depletion microscopy with programmable aberration correction,” J. Biophotonics 7(1-2), 29–36 (2014).
[Crossref] [PubMed]

Diaspro, A.

B. Harke, W. Dallari, G. Grancini, D. Fazzi, F. Brandi, A. Petrozza, and A. Diaspro, “Polymerization inhibition by triplet state absorption for nanoscale lithography,” Adv. Mater. 25(6), 904–909 (2013).
[Crossref] [PubMed]

Dunsby, C.

M. O. Lenz, H. G. Sinclair, A. Savell, J. H. Clegg, A. C. Brown, D. M. Davis, C. Dunsby, M. A. Neil, and P. M. French, “3-D stimulated emission depletion microscopy with programmable aberration correction,” J. Biophotonics 7(1-2), 29–36 (2014).
[Crossref] [PubMed]

E. Auksorius, B. R. Boruah, C. Dunsby, P. M. Lanigan, G. Kennedy, M. A. Neil, and P. M. French, “Stimulated emission depletion microscopy with a supercontinuum source and fluorescence lifetime imaging,” Opt. Lett. 33(2), 113–115 (2008).
[Crossref] [PubMed]

Dyba, M.

T. A. Klar, S. Jakobs, M. Dyba, A. Egner, and S. W. Hell, “Fluorescence microscopy with diffraction resolution barrier broken by stimulated emission,” Proc. Natl. Acad. Sci. U.S.A. 97(15), 8206–8210 (2000).
[Crossref] [PubMed]

Egner, A.

T. A. Klar, S. Jakobs, M. Dyba, A. Egner, and S. W. Hell, “Fluorescence microscopy with diffraction resolution barrier broken by stimulated emission,” Proc. Natl. Acad. Sci. U.S.A. 97(15), 8206–8210 (2000).
[Crossref] [PubMed]

Fazzi, D.

B. Harke, W. Dallari, G. Grancini, D. Fazzi, F. Brandi, A. Petrozza, and A. Diaspro, “Polymerization inhibition by triplet state absorption for nanoscale lithography,” Adv. Mater. 25(6), 904–909 (2013).
[Crossref] [PubMed]

Fiolka, R.

K. Si, R. Fiolka, and M. Cui, “Fluorescence imaging beyond the ballistic regime by ultrasound pulse guided digital phase conjugation,” Nat. Photonics 6(10), 657–661 (2012).
[Crossref] [PubMed]

French, P. M.

M. O. Lenz, H. G. Sinclair, A. Savell, J. H. Clegg, A. C. Brown, D. M. Davis, C. Dunsby, M. A. Neil, and P. M. French, “3-D stimulated emission depletion microscopy with programmable aberration correction,” J. Biophotonics 7(1-2), 29–36 (2014).
[Crossref] [PubMed]

E. Auksorius, B. R. Boruah, C. Dunsby, P. M. Lanigan, G. Kennedy, M. A. Neil, and P. M. French, “Stimulated emission depletion microscopy with a supercontinuum source and fluorescence lifetime imaging,” Opt. Lett. 33(2), 113–115 (2008).
[Crossref] [PubMed]

Gan, Z.

Gazagnes, S.

S. Gazagnes, E. Soubies, and L. Blanc-Féraud, “High density molecule localization for super-resolution microscopy using CEL0 based sparse approximation,” in 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (IEEE, 2017), pp. 28–31.
[Crossref]

Gong, W.

Q. Zhao, X. Shi, X. Zhu, Y. Zheng, C. Wu, H. Tang, L. Hu, Y. Xue, W. Gong, and K. Si, “Large field of view correction by using conjugate adaptive optics with multiple guide stars,” J. Biophotonics 12, e201800225 (2018).
[PubMed]

Y. Jin, Y. Zhang, L. Hu, H. Huang, Q. Xu, X. Zhu, L. Huang, Y. Zheng, H.-L. Shen, W. Gong, and K. Si, “Machine learning guided rapid focusing with sensor-less aberration corrections,” Opt. Express 26(23), 30162–30171 (2018).
[Crossref] [PubMed]

Göröcs, Z.

Gould, T. J.

Grancini, G.

B. Harke, W. Dallari, G. Grancini, D. Fazzi, F. Brandi, A. Petrozza, and A. Diaspro, “Polymerization inhibition by triplet state absorption for nanoscale lithography,” Adv. Mater. 25(6), 904–909 (2013).
[Crossref] [PubMed]

Gu, M.

X. Li, T. H. Lan, C. H. Tien, and M. Gu, “Three-dimensional orientation-unlimited polarization encryption by a single optically configured vectorial beam,” Nat. Commun. 3(1), 998 (2012).
[Crossref] [PubMed]

Z. Gan, Y. Cao, B. Jia, and M. Gu, “Dynamic modeling of superresolution photoinduced-inhibition nanolithography,” Opt. Express 20(15), 16871–16879 (2012).
[Crossref]

Günaydin, H.

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]

Hara, K.

K. Hara, D. Saito, and H. Shouno, “Analysis of function of rectified linear unit used in deep learning,” in 2015 International Joint Conference on Neural Networks (IJCNN), (IEEE, 2015), 1–8.
[Crossref]

Harke, B.

B. Harke, W. Dallari, G. Grancini, D. Fazzi, F. Brandi, A. Petrozza, and A. Diaspro, “Polymerization inhibition by triplet state absorption for nanoscale lithography,” Adv. Mater. 25(6), 904–909 (2013).
[Crossref] [PubMed]

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]

Hein, B.

B. Hein, K. I. Willig, and S. W. Hell, “Stimulated emission depletion (STED) nanoscopy of a fluorescent protein-labeled organelle inside a living cell,” Proc. Natl. Acad. Sci. U.S.A. 105(38), 14271–14276 (2008).
[Crossref] [PubMed]

Hell, S. W.

B. Hein, K. I. Willig, and S. W. Hell, “Stimulated emission depletion (STED) nanoscopy of a fluorescent protein-labeled organelle inside a living cell,” Proc. Natl. Acad. Sci. U.S.A. 105(38), 14271–14276 (2008).
[Crossref] [PubMed]

T. A. Klar, S. Jakobs, M. Dyba, A. Egner, and S. W. Hell, “Fluorescence microscopy with diffraction resolution barrier broken by stimulated emission,” Proc. Natl. Acad. Sci. U.S.A. 97(15), 8206–8210 (2000).
[Crossref] [PubMed]

T. A. Klar and S. W. Hell, “Subdiffraction resolution in far-field fluorescence microscopy,” Opt. Lett. 24(14), 954–956 (1999).
[Crossref] [PubMed]

S. W. Hell and J. Wichmann, “Breaking the diffraction resolution limit by stimulated emission: stimulated-emission-depletion fluorescence microscopy,” Opt. Lett. 19(11), 780–782 (1994).
[Crossref] [PubMed]

Hinton, G.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

Hinton, G. E.

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

Hu, L.

Q. Zhao, X. Shi, X. Zhu, Y. Zheng, C. Wu, H. Tang, L. Hu, Y. Xue, W. Gong, and K. Si, “Large field of view correction by using conjugate adaptive optics with multiple guide stars,” J. Biophotonics 12, e201800225 (2018).
[PubMed]

Y. Jin, Y. Zhang, L. Hu, H. Huang, Q. Xu, X. Zhu, L. Huang, Y. Zheng, H.-L. Shen, W. Gong, and K. Si, “Machine learning guided rapid focusing with sensor-less aberration corrections,” Opt. Express 26(23), 30162–30171 (2018).
[Crossref] [PubMed]

Huang, H.

Huang, L.

Jakobs, S.

T. A. Klar, S. Jakobs, M. Dyba, A. Egner, and S. W. Hell, “Fluorescence microscopy with diffraction resolution barrier broken by stimulated emission,” Proc. Natl. Acad. Sci. U.S.A. 97(15), 8206–8210 (2000).
[Crossref] [PubMed]

Ji, N.

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]

Jia, B.

Jin, Y.

Kennedy, G.

Kitamura, N.

H. Misawa, M. Koshioka, K. Sasaki, N. Kitamura, and H. Masuhara, “Three‐dimensional optical trapping and laser ablation of a single polymer latex particle in water,” J. Appl. Phys. 70(7), 3829–3836 (1991).
[Crossref]

Klar, T. A.

T. A. Klar, S. Jakobs, M. Dyba, A. Egner, and S. W. Hell, “Fluorescence microscopy with diffraction resolution barrier broken by stimulated emission,” Proc. Natl. Acad. Sci. U.S.A. 97(15), 8206–8210 (2000).
[Crossref] [PubMed]

T. A. Klar and S. W. Hell, “Subdiffraction resolution in far-field fluorescence microscopy,” Opt. Lett. 24(14), 954–956 (1999).
[Crossref] [PubMed]

Koshioka, M.

H. Misawa, M. Koshioka, K. Sasaki, N. Kitamura, and H. Masuhara, “Three‐dimensional optical trapping and laser ablation of a single polymer latex particle in water,” J. Appl. Phys. 70(7), 3829–3836 (1991).
[Crossref]

Krizhevsky, A.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

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

Lan, T. H.

X. Li, T. H. Lan, C. H. Tien, and M. Gu, “Three-dimensional orientation-unlimited polarization encryption by a single optically configured vectorial beam,” Nat. Commun. 3(1), 998 (2012).
[Crossref] [PubMed]

Lanigan, P. M.

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]

Lenz, M. O.

M. O. Lenz, H. G. Sinclair, A. Savell, J. H. Clegg, A. C. Brown, D. M. Davis, C. Dunsby, M. A. Neil, and P. M. French, “3-D stimulated emission depletion microscopy with programmable aberration correction,” J. Biophotonics 7(1-2), 29–36 (2014).
[Crossref] [PubMed]

Li, X.

X. Li, T. H. Lan, C. H. Tien, and M. Gu, “Three-dimensional orientation-unlimited polarization encryption by a single optically configured vectorial beam,” Nat. Commun. 3(1), 998 (2012).
[Crossref] [PubMed]

Li, Y.

Masuhara, H.

H. Misawa, M. Koshioka, K. Sasaki, N. Kitamura, and H. Masuhara, “Three‐dimensional optical trapping and laser ablation of a single polymer latex particle in water,” J. Appl. Phys. 70(7), 3829–3836 (1991).
[Crossref]

Michaeli, T.

Milkie, D. E.

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]

Misawa, H.

H. Misawa, M. Koshioka, K. Sasaki, N. Kitamura, and H. Masuhara, “Three‐dimensional optical trapping and laser ablation of a single polymer latex particle in water,” J. Appl. Phys. 70(7), 3829–3836 (1991).
[Crossref]

Nehme, E.

Neil, M. A.

M. O. Lenz, H. G. Sinclair, A. Savell, J. H. Clegg, A. C. Brown, D. M. Davis, C. Dunsby, M. A. Neil, and P. M. French, “3-D stimulated emission depletion microscopy with programmable aberration correction,” J. Biophotonics 7(1-2), 29–36 (2014).
[Crossref] [PubMed]

E. Auksorius, B. R. Boruah, C. Dunsby, P. M. Lanigan, G. Kennedy, M. A. Neil, and P. M. French, “Stimulated emission depletion microscopy with a supercontinuum source and fluorescence lifetime imaging,” Opt. Lett. 33(2), 113–115 (2008).
[Crossref] [PubMed]

Nyberg, S.

S. Nyberg, “Optical Determination of the Correlation Coefficient,” Opt. Acta (Lond.) 19(3), 195–201 (1972).
[Crossref]

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]

Owald, D.

Ozcan, A.

Patton, B.

M. Booth, D. Andrade, D. Burke, B. Patton, and M. Zurauskas, “Aberrations and adaptive optics in super-resolution microscopy,” Microscopy (Oxf.) 64(4), 251–261 (2015).
[Crossref] [PubMed]

Patton, B. R.

Petrozza, A.

B. Harke, W. Dallari, G. Grancini, D. Fazzi, F. Brandi, A. Petrozza, and A. Diaspro, “Polymerization inhibition by triplet state absorption for nanoscale lithography,” Adv. Mater. 25(6), 904–909 (2013).
[Crossref] [PubMed]

Qu, J.

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.

Saito, D.

K. Hara, D. Saito, and H. Shouno, “Analysis of function of rectified linear unit used in deep learning,” in 2015 International Joint Conference on Neural Networks (IJCNN), (IEEE, 2015), 1–8.
[Crossref]

Salakhutdinov, R.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

Sasaki, K.

H. Misawa, M. Koshioka, K. Sasaki, N. Kitamura, and H. Masuhara, “Three‐dimensional optical trapping and laser ablation of a single polymer latex particle in water,” J. Appl. Phys. 70(7), 3829–3836 (1991).
[Crossref]

Savell, A.

M. O. Lenz, H. G. Sinclair, A. Savell, J. H. Clegg, A. C. Brown, D. M. Davis, C. Dunsby, M. A. Neil, and P. M. French, “3-D stimulated emission depletion microscopy with programmable aberration correction,” J. Biophotonics 7(1-2), 29–36 (2014).
[Crossref] [PubMed]

Shechtman, Y.

Shen, H.-L.

Shi, X.

Q. Zhao, X. Shi, X. Zhu, Y. Zheng, C. Wu, H. Tang, L. Hu, Y. Xue, W. Gong, and K. Si, “Large field of view correction by using conjugate adaptive optics with multiple guide stars,” J. Biophotonics 12, e201800225 (2018).
[PubMed]

Shouno, H.

K. Hara, D. Saito, and H. Shouno, “Analysis of function of rectified linear unit used in deep learning,” in 2015 International Joint Conference on Neural Networks (IJCNN), (IEEE, 2015), 1–8.
[Crossref]

Shum, P.

Si, K.

Q. Zhao, X. Shi, X. Zhu, Y. Zheng, C. Wu, H. Tang, L. Hu, Y. Xue, W. Gong, and K. Si, “Large field of view correction by using conjugate adaptive optics with multiple guide stars,” J. Biophotonics 12, e201800225 (2018).
[PubMed]

Y. Jin, Y. Zhang, L. Hu, H. Huang, Q. Xu, X. Zhu, L. Huang, Y. Zheng, H.-L. Shen, W. Gong, and K. Si, “Machine learning guided rapid focusing with sensor-less aberration corrections,” Opt. Express 26(23), 30162–30171 (2018).
[Crossref] [PubMed]

K. Si, R. Fiolka, and M. Cui, “Fluorescence imaging beyond the ballistic regime by ultrasound pulse guided digital phase conjugation,” Nat. Photonics 6(10), 657–661 (2012).
[Crossref] [PubMed]

Sinclair, H. G.

M. O. Lenz, H. G. Sinclair, A. Savell, J. H. Clegg, A. C. Brown, D. M. Davis, C. Dunsby, M. A. Neil, and P. M. French, “3-D stimulated emission depletion microscopy with programmable aberration correction,” J. Biophotonics 7(1-2), 29–36 (2014).
[Crossref] [PubMed]

Soubies, E.

S. Gazagnes, E. Soubies, and L. Blanc-Féraud, “High density molecule localization for super-resolution microscopy using CEL0 based sparse approximation,” in 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (IEEE, 2017), pp. 28–31.
[Crossref]

Srivastava, N.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

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]

Sun, X. W.

Sutskever, I.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

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

Tan, Y.

Tang, H.

Q. Zhao, X. Shi, X. Zhu, Y. Zheng, C. Wu, H. Tang, L. Hu, Y. Xue, W. Gong, and K. Si, “Large field of view correction by using conjugate adaptive optics with multiple guide stars,” J. Biophotonics 12, e201800225 (2018).
[PubMed]

Tien, C. H.

X. Li, T. H. Lan, C. H. Tien, and M. Gu, “Three-dimensional orientation-unlimited polarization encryption by a single optically configured vectorial beam,” Nat. Commun. 3(1), 998 (2012).
[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, Q.

Weiss, L. E.

Wichmann, J.

Willig, K. I.

B. Hein, K. I. Willig, and S. W. Hell, “Stimulated emission depletion (STED) nanoscopy of a fluorescent protein-labeled organelle inside a living cell,” Proc. Natl. Acad. Sci. U.S.A. 105(38), 14271–14276 (2008).
[Crossref] [PubMed]

Wu, C.

Q. Zhao, X. Shi, X. Zhu, Y. Zheng, C. Wu, H. Tang, L. Hu, Y. Xue, W. Gong, and K. Si, “Large field of view correction by using conjugate adaptive optics with multiple guide stars,” J. Biophotonics 12, e201800225 (2018).
[PubMed]

Wu, T. W.

Xu, Q.

Xue, Y.

Q. Zhao, X. Shi, X. Zhu, Y. Zheng, C. Wu, H. Tang, L. Hu, Y. Xue, W. Gong, and K. Si, “Large field of view correction by using conjugate adaptive optics with multiple guide stars,” J. Biophotonics 12, e201800225 (2018).
[PubMed]

Yan, W.

Yang, Y.

Ye, T.

Zhang, Y.

Zhao, Q.

Q. Zhao, X. Shi, X. Zhu, Y. Zheng, C. Wu, H. Tang, L. Hu, Y. Xue, W. Gong, and K. Si, “Large field of view correction by using conjugate adaptive optics with multiple guide stars,” J. Biophotonics 12, e201800225 (2018).
[PubMed]

Zheng, Y.

Q. Zhao, X. Shi, X. Zhu, Y. Zheng, C. Wu, H. Tang, L. Hu, Y. Xue, W. Gong, and K. Si, “Large field of view correction by using conjugate adaptive optics with multiple guide stars,” J. Biophotonics 12, e201800225 (2018).
[PubMed]

Y. Jin, Y. Zhang, L. Hu, H. Huang, Q. Xu, X. Zhu, L. Huang, Y. Zheng, H.-L. Shen, W. Gong, and K. Si, “Machine learning guided rapid focusing with sensor-less aberration corrections,” Opt. Express 26(23), 30162–30171 (2018).
[Crossref] [PubMed]

Zhu, X.

Y. Jin, Y. Zhang, L. Hu, H. Huang, Q. Xu, X. Zhu, L. Huang, Y. Zheng, H.-L. Shen, W. Gong, and K. Si, “Machine learning guided rapid focusing with sensor-less aberration corrections,” Opt. Express 26(23), 30162–30171 (2018).
[Crossref] [PubMed]

Q. Zhao, X. Shi, X. Zhu, Y. Zheng, C. Wu, H. Tang, L. Hu, Y. Xue, W. Gong, and K. Si, “Large field of view correction by using conjugate adaptive optics with multiple guide stars,” J. Biophotonics 12, e201800225 (2018).
[PubMed]

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]

Zurauskas, M.

M. Booth, D. Andrade, D. Burke, B. Patton, and M. Zurauskas, “Aberrations and adaptive optics in super-resolution microscopy,” Microscopy (Oxf.) 64(4), 251–261 (2015).
[Crossref] [PubMed]

Adv. Mater. (1)

B. Harke, W. Dallari, G. Grancini, D. Fazzi, F. Brandi, A. Petrozza, and A. Diaspro, “Polymerization inhibition by triplet state absorption for nanoscale lithography,” Adv. Mater. 25(6), 904–909 (2013).
[Crossref] [PubMed]

Appl. Opt. (1)

J. Appl. Phys. (1)

H. Misawa, M. Koshioka, K. Sasaki, N. Kitamura, and H. Masuhara, “Three‐dimensional optical trapping and laser ablation of a single polymer latex particle in water,” J. Appl. Phys. 70(7), 3829–3836 (1991).
[Crossref]

J. Biophotonics (2)

M. O. Lenz, H. G. Sinclair, A. Savell, J. H. Clegg, A. C. Brown, D. M. Davis, C. Dunsby, M. A. Neil, and P. M. French, “3-D stimulated emission depletion microscopy with programmable aberration correction,” J. Biophotonics 7(1-2), 29–36 (2014).
[Crossref] [PubMed]

Q. Zhao, X. Shi, X. Zhu, Y. Zheng, C. Wu, H. Tang, L. Hu, Y. Xue, W. Gong, and K. Si, “Large field of view correction by using conjugate adaptive optics with multiple guide stars,” J. Biophotonics 12, e201800225 (2018).
[PubMed]

J. Mach. Learn. Res. (1)

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res. 15, 1929–1958 (2014).

Microscopy (Oxf.) (1)

M. Booth, D. Andrade, D. Burke, B. Patton, and M. Zurauskas, “Aberrations and adaptive optics in super-resolution microscopy,” Microscopy (Oxf.) 64(4), 251–261 (2015).
[Crossref] [PubMed]

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. (2)

X. Li, T. H. Lan, C. H. Tien, and M. Gu, “Three-dimensional orientation-unlimited polarization encryption by a single optically configured vectorial beam,” Nat. Commun. 3(1), 998 (2012).
[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]

Nat. Methods (1)

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]

Nat. Photonics (1)

K. Si, R. Fiolka, and M. Cui, “Fluorescence imaging beyond the ballistic regime by ultrasound pulse guided digital phase conjugation,” Nat. Photonics 6(10), 657–661 (2012).
[Crossref] [PubMed]

Opt. Acta (Lond.) (1)

S. Nyberg, “Optical Determination of the Correlation Coefficient,” Opt. Acta (Lond.) 19(3), 195–201 (1972).
[Crossref]

Opt. Express (5)

Opt. Lett. (4)

Optica (2)

Photon. Res. (1)

Proc. Natl. Acad. Sci. U.S.A. (2)

B. Hein, K. I. Willig, and S. W. Hell, “Stimulated emission depletion (STED) nanoscopy of a fluorescent protein-labeled organelle inside a living cell,” Proc. Natl. Acad. Sci. U.S.A. 105(38), 14271–14276 (2008).
[Crossref] [PubMed]

T. A. Klar, S. Jakobs, M. Dyba, A. Egner, and S. W. Hell, “Fluorescence microscopy with diffraction resolution barrier broken by stimulated emission,” Proc. Natl. Acad. Sci. U.S.A. 97(15), 8206–8210 (2000).
[Crossref] [PubMed]

Other (5)

S. Gazagnes, E. Soubies, and L. Blanc-Féraud, “High density molecule localization for super-resolution microscopy using CEL0 based sparse approximation,” in 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (IEEE, 2017), pp. 28–31.
[Crossref]

N. Ketkar, “Convolutional neural networks,” in Deep Learning with Python (Springer, 2017), pp. 63–78.

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

B. Graham, “Fractional max-pooling,” arXiv preprint arXiv:1412.6071 (2014).

K. Hara, D. Saito, and H. Shouno, “Analysis of function of rectified linear unit used in deep learning,” in 2015 International Joint Conference on Neural Networks (IJCNN), (IEEE, 2015), 1–8.
[Crossref]

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Figures (8)

Fig. 1
Fig. 1 (a) Experimental setup of machine learning based adaptive optics system for a doughnut-shaped beam. (b) The ideal spiral phase pattern to generate the vortex laser beam for illumination. (c) The corresponding IPSF on the focal plane generated by (b). (d) The distorted phase pattern with a random phase mask added on the ideal spiral phase pattern. (e) The corresponding distorted IPSF on the focal plane generated by (d). (L1, L2, L3 are relay lenses; M, mirror; AP, aperture; HW, half-wave plate; PBS, polarization beam splitter; BS, non-polarizing beam splitter; BB, black block; SLM, spatial light modulator; CMOS, complementary metal oxide semiconductor camera). The image size in (b) and (d) are 900 × 900 pixels. The image size in (c) and (e) are 64 × 64 pixels. Each pixel size is 6 μm.
Fig. 2
Fig. 2 Phase pattern loaded on the SLM. (a) Initial phase pattern loaded on the SLM to produce an ideal doughnut-shaped IPSF. (b) Phase pattern added with a random phase mask to generate the distorted doughnut-shaped IPSF. The color bar represents 16-bit gray value, 0-65536. The size of each pixel is 15 μm.
Fig. 3
Fig. 3 Specific CNN framework. The learning network is based on Alexnet. It consists of 5 convolutional (conv) layers and 3 fully connected (fc) layers. The input is a series of images with 64 × 64 pixels and the output are Zernike coefficients vectors. The convolution filters have 32 kernels of size 5 × 5 in the first two convolutional layers and 64 kernels of size 3 × 3 in the last three convolutional layers. The first two fully connected layers have 512 neurons each, while the last one has 12 neurons to generate a 1 × 12 vector. ReLU, Rectified Linear Unit, a kind of activation function. Maxpooling, a kind of down-sampling method. The size of the down-sampling is in the parentheses. Dropout, temporarily discards some neural network units from the network with a certain probability. The ratio of dropout is in the parentheses.
Fig. 4
Fig. 4 Comparison of the doughnut-shaped IPSFs (a) before and (c) after machine learning based AO correction. (b), (d) IPSF profiles along line I and II before (blue) and after (magenta) correction. (e) Zernike coefficients comparison between origin and predict values. The scale bar is 60 μm.
Fig. 5
Fig. 5 Compensation effects of doughnut-shaped IPSF in experiment system. (a) The IPSF scattered by phase-mask and compensated by CNN learning model. M is the correlation coefficients between the corrected doughnut-shaped IPSF and ideal doughnut-shaped IPSF. (b) The statistical distribution of 200 test data for correlation coefficient M. The value mainly falls between 0.82 and 0.88. The scale bar is 120 μm.
Fig. 6
Fig. 6 Schematic diagram of the imaging system. The CMOS camera is used to detect the distorted IPSFs for aberration correction, while PMT is used to image the fluorescent beads. The scanning process in each sub-region is achieved by adding phase slope consisting of Tip and Tilt (Zernike modes 2 and 3) on the SLM, as shown in the dashed box. DM, dichroic mirror.
Fig. 7
Fig. 7 Fluorescence images of the 1-μm diameter sparsely distributed beads illuminated by doughnut-shaped IPSF. The wavefront distortion is introduced by adding random phase mask at the back pupil plane of the objective. (a) Intensity distribution without aberration (ideal). (b) Intensity distribution with aberration before compensation (scattered). (c) Intensity distribution with aberration after compensation (corrected). Inside the green box is the enlargement of the corresponding region. The intensity is normalized. The scale bar is 500 μm in (a-c) and 250 μm in the magnified images.
Fig. 8
Fig. 8 Fluorescence images of the 1-μm diameter sparsely distributed beads illuminated by doughnut-shaped IPSF. The wavefront distortion is introduced by adding a 1-mm thick phantom above the fluorescent beams in the imaging space. (a) Intensity distribution without aberration (ideal). (b) Intensity distribution with aberration before compensation (scattered). (c) Intensity distribution with aberration after compensation (corrected). (d) Inside the colored dotted frame is the magnification of each case in (a-c). (e) Intensity profile along the white dotted line in (a-c). The intensity is normalized. The scale bar is 500 μm in (a-c) and 250 μm in (d).

Tables (2)

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Table 1 Coefficient range of 4th~15th Zernike modes

Tables Icon

Table 2 MSE of each Zernike coefficient in 200 tests

Equations (1)

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M=corrcoef( I corr , I ideal )