Abstract

Aberrations arising from sources such as sample heterogeneity and refractive index mismatches are constant problems in biological imaging. These aberrations reduce image quality and the achievable depth of imaging, particularly in super-resolution microscopy techniques. Adaptive optics (AO) technology has been proven to be effective in correcting for these aberrations, thereby improving the image quality. However, it has not been widely adopted by the biological imaging community due, in part, to difficulty in set-up and operation of AO. The methods for doing so are not novel or unknown, but new users often waste time and effort reimplementing existing methods for their specific set-ups, hardware, sample types, etc. Microscope-AOtools offers a robust, easy-to-use implementation of the essential methods for set-up and use of AO elements and techniques. These methods are constructed in a generalised manner that can utilise a range of adaptive optics elements, wavefront sensing techniques and sensorless AO correction methods. Furthermore, the methods are designed to be easily extensible as new techniques arise, leading to a streamlined pipeline for new AO technology and techniques to be adopted by the wider microscopy community.

Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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2020 (2)

P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, and J. Bright, “Scipy 1.0: fundamental algorithms for scientific computing in python,” Nat. Methods 17(3), 261–272 (2020).
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J. Antonello, A. Barbotin, E. Z. Chong, J. Rittscher, and M. J. Booth, “Multi-scale sensorless adaptive optics: application to stimulated emission depletion microscopy,” Opt. Express 28(11), 16749–16763 (2020).
[Crossref]

2019 (3)

2018 (1)

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

2017 (2)

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

A. Meurer, C. P. Smith, M. Paprocki, O. Čertík, S. B. Kirpichev, M. Rocklin, A. Kumar, S. Ivanov, J. K. Moore, and S. Singh, “Sympy: symbolic computing in python,” PeerJ Comput. Sci. 3, e103e103 (2017).
[Crossref]

2016 (1)

2015 (1)

2014 (3)

K. Wang, D. E. Milkie, A. Saxena, P. Engerer, T. Misgeld, M. E. Bronner, J. Mumm, and E. Betzig, “Rapid adaptive optical recovery of optimal resolution over large volumes,” Nat. Methods 11(6), 625–628 (2014).
[Crossref]

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[Crossref]

S. Van der Walt, J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, and T. Yu, “scikit-image: image processing in python,” PeerJ 2, e453 (2014).
[Crossref]

2013 (1)

2012 (1)

2011 (4)

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]

2009 (1)

J. M. Girkin, S. Poland, and A. J. Wright, “Adaptive optics for deeper imaging of biological samples,” Curr. Opin. Biotechnol. 20(1), 106–110 (2009).
[Crossref]

2008 (2)

M. G. Gustafsson, L. Shao, P. M. Carlton, C. R. Wang, I. N. Golubovskaya, W. Z. Cande, D. A. Agard, and J. W. Sedat, “Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination,” Biophys. J. 94(12), 4957–4970 (2008).
[Crossref]

D. Débarre, E. J. Botcherby, M. J. Booth, and T. Wilson, “Adaptive optics for structured illumination microscopy,” Opt. Express 16(13), 9290–9305 (2008).
[Crossref]

2007 (1)

M. J. Booth, “Adaptive optics in microscopy,” Philos. Trans. R. Soc., A 365(1861), 2829–2843 (2007).
[Crossref]

2006 (1)

E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313(5793), 1642–1645 (2006).
[Crossref]

2005 (1)

2004 (2)

M. Schwertner, M. J. Booth, and T. Wilson, “Characterizing specimen induced aberrations for high na adaptive optical microscopy,” Opt. Express 12(26), 6540–6552 (2004).
[Crossref]

M. Schwertner, M. J. Booth, M. A. Neil, and T. Wilson, “Measurement of specimen-induced aberrations of biological samples using phase stepping interferometry,” J. Microsc. 213(1), 11–19 (2004).
[Crossref]

2003 (1)

J. Fienup and J. Miller, “Aberration correction by maximizing generalized sharpness metrics,” J. Opt. Soc. Am. 20(4), 609–620 (2003).
[Crossref]

2002 (1)

M. J. Booth, M. A. Neil, R. Juškaitis, and T. Wilson, “Adaptive aberration correction in a confocal microscope,” Proc. Natl. Acad. Sci. 99(9), 5788–5792 (2002).
[Crossref]

1999 (1)

1994 (1)

1992 (1)

J. C. Wyant and K. Creath, “Basic wavefront aberration theory for optical metrology,” Applied optics and optical engineering 11, 28–39 (1992).

1976 (1)

1934 (1)

F. Zernike, “Diffraction theory of the cutting process and its improved form, the phase contrast method,” Physica 1, 689–704 (1934).

Agard, D.

P. Kner, Z. Kam, D. Agard, and J. Sedat, “Adaptive optics in wide-field microscopy,” MEMS Adaptive Optics V, vol. 7931 (International Society for Optics and Photonics, 2011), p. 79310K.

Agard, D. A.

M. G. Gustafsson, L. Shao, P. M. Carlton, C. R. Wang, I. N. Golubovskaya, W. Z. Cande, D. A. Agard, and J. W. Sedat, “Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination,” Biophys. J. 94(12), 4957–4970 (2008).
[Crossref]

Antonello, J.

Azucena, O.

Barbotin, A.

Bartsch, D.-U.

Betzig, E.

K. Wang, D. E. Milkie, A. Saxena, P. Engerer, T. Misgeld, M. E. Bronner, J. Mumm, and E. Betzig, “Rapid adaptive optical recovery of optimal resolution over large volumes,” Nat. Methods 11(6), 625–628 (2014).
[Crossref]

D. E. Milkie, E. Betzig, and N. Ji, “Pupil-segmentation-based adaptive optical microscopy with full-pupil illumination,” Opt. Lett. 36(21), 4206–4208 (2011).
[Crossref]

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]

E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313(5793), 1642–1645 (2006).
[Crossref]

Bewersdorf, J.

Bonifacino, J. S.

E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313(5793), 1642–1645 (2006).
[Crossref]

Booth, M.

Booth, M. J.

J. Antonello, A. Barbotin, E. Z. Chong, J. Rittscher, and M. J. Booth, “Multi-scale sensorless adaptive optics: application to stimulated emission depletion microscopy,” Opt. Express 28(11), 16749–16763 (2020).
[Crossref]

M. Žurauskas, I. M. Dobbie, R. M. Parton, M. A. Phillips, A. Göhler, I. Davis, and M. J. Booth, “Isosense: frequency enhanced sensorless adaptive optics through structured illumination,” Optica 6(3), 370–379 (2019).
[Crossref]

D. Burke, B. Patton, F. Huang, J. Bewersdorf, and M. J. Booth, “Adaptive optics correction of specimen-induced aberrations in single-molecule switching microscopy,” Optica 2(2), 177–185 (2015).
[Crossref]

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

S. A. Rahman and M. J. Booth, “Direct wavefront sensing in adaptive optical microscopy using backscattered light,” Appl. Opt. 52(22), 5523–5532 (2013).
[Crossref]

D. Débarre, E. J. Botcherby, M. J. Booth, and T. Wilson, “Adaptive optics for structured illumination microscopy,” Opt. Express 16(13), 9290–9305 (2008).
[Crossref]

M. J. Booth, “Adaptive optics in microscopy,” Philos. Trans. R. Soc., A 365(1861), 2829–2843 (2007).
[Crossref]

M. Schwertner, M. J. Booth, M. A. Neil, and T. Wilson, “Measurement of specimen-induced aberrations of biological samples using phase stepping interferometry,” J. Microsc. 213(1), 11–19 (2004).
[Crossref]

M. Schwertner, M. J. Booth, and T. Wilson, “Characterizing specimen induced aberrations for high na adaptive optical microscopy,” Opt. Express 12(26), 6540–6552 (2004).
[Crossref]

M. J. Booth, M. A. Neil, R. Juškaitis, and T. Wilson, “Adaptive aberration correction in a confocal microscope,” Proc. Natl. Acad. Sci. 99(9), 5788–5792 (2002).
[Crossref]

M. J. Booth, A basic introduction to adaptive optics for microscopy (University of Oxford, 2019).

Botcherby, E. J.

Boulogne, F.

S. Van der Walt, J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, and T. Yu, “scikit-image: image processing in python,” PeerJ 2, e453 (2014).
[Crossref]

Bright, J.

P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, and J. Bright, “Scipy 1.0: fundamental algorithms for scientific computing in python,” Nat. Methods 17(3), 261–272 (2020).
[Crossref]

Bronner, M. E.

K. Wang, D. E. Milkie, A. Saxena, P. Engerer, T. Misgeld, M. E. Bronner, J. Mumm, and E. Betzig, “Rapid adaptive optical recovery of optimal resolution over large volumes,” Nat. Methods 11(6), 625–628 (2014).
[Crossref]

Burke, D.

Burovski, E.

P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, and J. Bright, “Scipy 1.0: fundamental algorithms for scientific computing in python,” Nat. Methods 17(3), 261–272 (2020).
[Crossref]

Cande, W. Z.

M. G. Gustafsson, L. Shao, P. M. Carlton, C. R. Wang, I. N. Golubovskaya, W. Z. Cande, D. A. Agard, and J. W. Sedat, “Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination,” Biophys. J. 94(12), 4957–4970 (2008).
[Crossref]

Carlton, P. M.

M. G. Gustafsson, L. Shao, P. M. Carlton, C. R. Wang, I. N. Golubovskaya, W. Z. Cande, D. A. Agard, and J. W. Sedat, “Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination,” Biophys. J. 94(12), 4957–4970 (2008).
[Crossref]

Certík, O.

A. Meurer, C. P. Smith, M. Paprocki, O. Čertík, S. B. Kirpichev, M. Rocklin, A. Kumar, S. Ivanov, J. K. Moore, and S. Singh, “Sympy: symbolic computing in python,” PeerJ Comput. Sci. 3, e103e103 (2017).
[Crossref]

Chen, D. C.

Chong, E. Z.

Colbert, S. C.

S. v. d. Walt, S. C. Colbert, and G. Varoquaux, “The numpy array: a structure for efficient numerical computation,” Comput. Sci. Eng. 13(2), 22–30 (2011).
[Crossref]

Cournapeau, D.

P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, and J. Bright, “Scipy 1.0: fundamental algorithms for scientific computing in python,” Nat. Methods 17(3), 261–272 (2020).
[Crossref]

Creath, K.

J. C. Wyant and K. Creath, “Basic wavefront aberration theory for optical metrology,” Applied optics and optical engineering 11, 28–39 (1992).

Crest, J.

Davidson, M. W.

E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313(5793), 1642–1645 (2006).
[Crossref]

Davis, I.

de Xivry, G. O.

Débarre, D.

Dobbie, I.

I. Dobbie, N. Hall, and D. Pinto, “Beamdelta: simple alignment tool for optical systems,” Wellcome Open Res. 4, 194 (2019).
[Crossref]

Dobbie, I. M.

Engerer, P.

K. Wang, D. E. Milkie, A. Saxena, P. Engerer, T. Misgeld, M. E. Bronner, J. Mumm, and E. Betzig, “Rapid adaptive optical recovery of optimal resolution over large volumes,” Nat. Methods 11(6), 625–628 (2014).
[Crossref]

Fainman, Y.

Farley, O.

Fernandez, B.

Fienup, J.

J. Fienup and J. Miller, “Aberration correction by maximizing generalized sharpness metrics,” J. Opt. Soc. Am. 20(4), 609–620 (2003).
[Crossref]

Freeman, W. R.

Fu, M.

Garcia, D.

Gavel, D.

Girkin, J. M.

J. M. Girkin, S. Poland, and A. J. Wright, “Adaptive optics for deeper imaging of biological samples,” Curr. Opin. Biotechnol. 20(1), 106–110 (2009).
[Crossref]

Göhler, A.

Golubovskaya, I. N.

M. G. Gustafsson, L. Shao, P. M. Carlton, C. R. Wang, I. N. Golubovskaya, W. Z. Cande, D. A. Agard, and J. W. Sedat, “Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination,” Biophys. J. 94(12), 4957–4970 (2008).
[Crossref]

Gommers, R.

P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, and J. Bright, “Scipy 1.0: fundamental algorithms for scientific computing in python,” Nat. Methods 17(3), 261–272 (2020).
[Crossref]

Gouillart, E.

S. Van der Walt, J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, and T. Yu, “scikit-image: image processing in python,” PeerJ 2, e453 (2014).
[Crossref]

Gustafsson, M. G.

M. G. Gustafsson, L. Shao, P. M. Carlton, C. R. Wang, I. N. Golubovskaya, W. Z. Cande, D. A. Agard, and J. W. Sedat, “Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination,” Biophys. J. 94(12), 4957–4970 (2008).
[Crossref]

Haberland, M.

P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, and J. Bright, “Scipy 1.0: fundamental algorithms for scientific computing in python,” Nat. Methods 17(3), 261–272 (2020).
[Crossref]

Hall, N.

I. Dobbie, N. Hall, and D. Pinto, “Beamdelta: simple alignment tool for optical systems,” Wellcome Open Res. 4, 194 (2019).
[Crossref]

Hell, S. W.

Hess, H. F.

E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313(5793), 1642–1645 (2006).
[Crossref]

Huang, F.

Ivanov, S.

A. Meurer, C. P. Smith, M. Paprocki, O. Čertík, S. B. Kirpichev, M. Rocklin, A. Kumar, S. Ivanov, J. K. Moore, and S. Singh, “Sympy: symbolic computing in python,” PeerJ Comput. Sci. 3, e103e103 (2017).
[Crossref]

Ji, N.

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

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

D. E. Milkie, E. Betzig, and N. Ji, “Pupil-segmentation-based adaptive optical microscopy with full-pupil illumination,” Opt. Lett. 36(21), 4206–4208 (2011).
[Crossref]

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]

Juškaitis, R.

M. J. Booth, M. A. Neil, R. Juškaitis, and T. Wilson, “Adaptive aberration correction in a confocal microscope,” Proc. Natl. Acad. Sci. 99(9), 5788–5792 (2002).
[Crossref]

Kam, Z.

P. Kner, Z. Kam, D. Agard, and J. Sedat, “Adaptive optics in wide-field microscopy,” MEMS Adaptive Optics V, vol. 7931 (International Society for Optics and Photonics, 2011), p. 79310K.

Kawata, S.

Kirpichev, S. B.

A. Meurer, C. P. Smith, M. Paprocki, O. Čertík, S. B. Kirpichev, M. Rocklin, A. Kumar, S. Ivanov, J. K. Moore, and S. Singh, “Sympy: symbolic computing in python,” PeerJ Comput. Sci. 3, e103e103 (2017).
[Crossref]

Kner, P.

P. Kner, Z. Kam, D. Agard, and J. Sedat, “Adaptive optics in wide-field microscopy,” MEMS Adaptive Optics V, vol. 7931 (International Society for Optics and Photonics, 2011), p. 79310K.

Kotadia, S.

Kubby, J.

Kumar, A.

A. Meurer, C. P. Smith, M. Paprocki, O. Čertík, S. B. Kirpichev, M. Rocklin, A. Kumar, S. Ivanov, J. K. Moore, and S. Singh, “Sympy: symbolic computing in python,” PeerJ Comput. Sci. 3, e103e103 (2017).
[Crossref]

Lindwasser, O. W.

E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313(5793), 1642–1645 (2006).
[Crossref]

Lippincott-Schwartz, J.

E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313(5793), 1642–1645 (2006).
[Crossref]

Meurer, A.

A. Meurer, C. P. Smith, M. Paprocki, O. Čertík, S. B. Kirpichev, M. Rocklin, A. Kumar, S. Ivanov, J. K. Moore, and S. Singh, “Sympy: symbolic computing in python,” PeerJ Comput. Sci. 3, e103e103 (2017).
[Crossref]

Milkie, D. E.

K. Wang, D. E. Milkie, A. Saxena, P. Engerer, T. Misgeld, M. E. Bronner, J. Mumm, and E. Betzig, “Rapid adaptive optical recovery of optimal resolution over large volumes,” Nat. Methods 11(6), 625–628 (2014).
[Crossref]

D. E. Milkie, E. Betzig, and N. Ji, “Pupil-segmentation-based adaptive optical microscopy with full-pupil illumination,” Opt. Lett. 36(21), 4206–4208 (2011).
[Crossref]

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]

Miller, J.

J. Fienup and J. Miller, “Aberration correction by maximizing generalized sharpness metrics,” J. Opt. Soc. Am. 20(4), 609–620 (2003).
[Crossref]

Misgeld, T.

K. Wang, D. E. Milkie, A. Saxena, P. Engerer, T. Misgeld, M. E. Bronner, J. Mumm, and E. Betzig, “Rapid adaptive optical recovery of optimal resolution over large volumes,” Nat. Methods 11(6), 625–628 (2014).
[Crossref]

Moore, J. K.

A. Meurer, C. P. Smith, M. Paprocki, O. Čertík, S. B. Kirpichev, M. Rocklin, A. Kumar, S. Ivanov, J. K. Moore, and S. Singh, “Sympy: symbolic computing in python,” PeerJ Comput. Sci. 3, e103e103 (2017).
[Crossref]

Mumm, J.

K. Wang, D. E. Milkie, A. Saxena, P. Engerer, T. Misgeld, M. E. Bronner, J. Mumm, and E. Betzig, “Rapid adaptive optical recovery of optimal resolution over large volumes,” Nat. Methods 11(6), 625–628 (2014).
[Crossref]

Neil, M. A.

M. Schwertner, M. J. Booth, M. A. Neil, and T. Wilson, “Measurement of specimen-induced aberrations of biological samples using phase stepping interferometry,” J. Microsc. 213(1), 11–19 (2004).
[Crossref]

M. J. Booth, M. A. Neil, R. Juškaitis, and T. Wilson, “Adaptive aberration correction in a confocal microscope,” Proc. Natl. Acad. Sci. 99(9), 5788–5792 (2002).
[Crossref]

Noll, R. J.

Nunez-Iglesias, J.

S. Van der Walt, J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, and T. Yu, “scikit-image: image processing in python,” PeerJ 2, e453 (2014).
[Crossref]

Olenych, S.

E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313(5793), 1642–1645 (2006).
[Crossref]

Oliphant, T. E.

P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, and J. Bright, “Scipy 1.0: fundamental algorithms for scientific computing in python,” Nat. Methods 17(3), 261–272 (2020).
[Crossref]

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Osborn, J.

Ota, T.

Paprocki, M.

A. Meurer, C. P. Smith, M. Paprocki, O. Čertík, S. B. Kirpichev, M. Rocklin, A. Kumar, S. Ivanov, J. K. Moore, and S. Singh, “Sympy: symbolic computing in python,” PeerJ Comput. Sci. 3, e103e103 (2017).
[Crossref]

Parton, R. M.

Patterson, G. H.

E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313(5793), 1642–1645 (2006).
[Crossref]

Patton, B.

Peterson, P.

P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, and J. Bright, “Scipy 1.0: fundamental algorithms for scientific computing in python,” Nat. Methods 17(3), 261–272 (2020).
[Crossref]

Phillips, M. A.

Pinto, D.

I. Dobbie, N. Hall, and D. Pinto, “Beamdelta: simple alignment tool for optical systems,” Wellcome Open Res. 4, 194 (2019).
[Crossref]

Poland, S.

J. M. Girkin, S. Poland, and A. J. Wright, “Adaptive optics for deeper imaging of biological samples,” Curr. Opin. Biotechnol. 20(1), 106–110 (2009).
[Crossref]

Pozzi, P.

Rahman, S. A.

Reddy, T.

P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, and J. Bright, “Scipy 1.0: fundamental algorithms for scientific computing in python,” Nat. Methods 17(3), 261–272 (2020).
[Crossref]

Reeves, A.

Reinig, M.

Rittscher, J.

Rocklin, M.

A. Meurer, C. P. Smith, M. Paprocki, O. Čertík, S. B. Kirpichev, M. Rocklin, A. Kumar, S. Ivanov, J. K. Moore, and S. Singh, “Sympy: symbolic computing in python,” PeerJ Comput. Sci. 3, e103e103 (2017).
[Crossref]

Rodríguez, C.

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

Saxena, A.

K. Wang, D. E. Milkie, A. Saxena, P. Engerer, T. Misgeld, M. E. Bronner, J. Mumm, and E. Betzig, “Rapid adaptive optical recovery of optimal resolution over large volumes,” Nat. Methods 11(6), 625–628 (2014).
[Crossref]

Schönberger, J. L.

S. Van der Walt, J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, and T. Yu, “scikit-image: image processing in python,” PeerJ 2, e453 (2014).
[Crossref]

Schwertner, M.

M. Schwertner, M. J. Booth, M. A. Neil, and T. Wilson, “Measurement of specimen-induced aberrations of biological samples using phase stepping interferometry,” J. Microsc. 213(1), 11–19 (2004).
[Crossref]

M. Schwertner, M. J. Booth, and T. Wilson, “Characterizing specimen induced aberrations for high na adaptive optical microscopy,” Opt. Express 12(26), 6540–6552 (2004).
[Crossref]

Sedat, J.

P. Kner, Z. Kam, D. Agard, and J. Sedat, “Adaptive optics in wide-field microscopy,” MEMS Adaptive Optics V, vol. 7931 (International Society for Optics and Photonics, 2011), p. 79310K.

Sedat, J. W.

M. G. Gustafsson, L. Shao, P. M. Carlton, C. R. Wang, I. N. Golubovskaya, W. Z. Cande, D. A. Agard, and J. W. Sedat, “Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination,” Biophys. J. 94(12), 4957–4970 (2008).
[Crossref]

Shao, L.

M. G. Gustafsson, L. Shao, P. M. Carlton, C. R. Wang, I. N. Golubovskaya, W. Z. Cande, D. A. Agard, and J. W. Sedat, “Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination,” Biophys. J. 94(12), 4957–4970 (2008).
[Crossref]

Singh, S.

A. Meurer, C. P. Smith, M. Paprocki, O. Čertík, S. B. Kirpichev, M. Rocklin, A. Kumar, S. Ivanov, J. K. Moore, and S. Singh, “Sympy: symbolic computing in python,” PeerJ Comput. Sci. 3, e103e103 (2017).
[Crossref]

Smith, C. P.

A. Meurer, C. P. Smith, M. Paprocki, O. Čertík, S. B. Kirpichev, M. Rocklin, A. Kumar, S. Ivanov, J. K. Moore, and S. Singh, “Sympy: symbolic computing in python,” PeerJ Comput. Sci. 3, e103e103 (2017).
[Crossref]

Soloviev, O.

Sougrat, R.

E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313(5793), 1642–1645 (2006).
[Crossref]

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Sun, H.-B.

Sun, P.-C.

Tao, X.

Townson, M.

v. d. Walt, S.

S. v. d. Walt, S. C. Colbert, and G. Varoquaux, “The numpy array: a structure for efficient numerical computation,” Comput. Sci. Eng. 13(2), 22–30 (2011).
[Crossref]

Van der Walt, S.

S. Van der Walt, J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, and T. Yu, “scikit-image: image processing in python,” PeerJ 2, e453 (2014).
[Crossref]

Varoquaux, G.

S. v. d. Walt, S. C. Colbert, and G. Varoquaux, “The numpy array: a structure for efficient numerical computation,” Comput. Sci. Eng. 13(2), 22–30 (2011).
[Crossref]

Vdovin, G.

Verhaegen, M.

Virtanen, P.

P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, and J. Bright, “Scipy 1.0: fundamental algorithms for scientific computing in python,” Nat. Methods 17(3), 261–272 (2020).
[Crossref]

Wang, C. R.

M. G. Gustafsson, L. Shao, P. M. Carlton, C. R. Wang, I. N. Golubovskaya, W. Z. Cande, D. A. Agard, and J. W. Sedat, “Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination,” Biophys. J. 94(12), 4957–4970 (2008).
[Crossref]

Wang, K.

K. Wang, D. E. Milkie, A. Saxena, P. Engerer, T. Misgeld, M. E. Bronner, J. Mumm, and E. Betzig, “Rapid adaptive optical recovery of optimal resolution over large volumes,” Nat. Methods 11(6), 625–628 (2014).
[Crossref]

Warner, J. D.

S. Van der Walt, J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, and T. Yu, “scikit-image: image processing in python,” PeerJ 2, e453 (2014).
[Crossref]

Weckesser, W.

P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, and J. Bright, “Scipy 1.0: fundamental algorithms for scientific computing in python,” Nat. Methods 17(3), 261–272 (2020).
[Crossref]

Weisstein, E. W.

E. W. Weisstein, “Gibbs phenomenon,” https://mathworld.wolfram.com/ (2003).

Wichmann, J.

Wilding, D.

Wilson, T.

D. Débarre, E. J. Botcherby, M. J. Booth, and T. Wilson, “Adaptive optics for structured illumination microscopy,” Opt. Express 16(13), 9290–9305 (2008).
[Crossref]

M. Booth, T. Wilson, H.-B. Sun, T. Ota, and S. Kawata, “Methods for the characterization of deformable membrane mirrors,” Appl. Opt. 44(24), 5131–5139 (2005).
[Crossref]

M. Schwertner, M. J. Booth, and T. Wilson, “Characterizing specimen induced aberrations for high na adaptive optical microscopy,” Opt. Express 12(26), 6540–6552 (2004).
[Crossref]

M. Schwertner, M. J. Booth, M. A. Neil, and T. Wilson, “Measurement of specimen-induced aberrations of biological samples using phase stepping interferometry,” J. Microsc. 213(1), 11–19 (2004).
[Crossref]

M. J. Booth, M. A. Neil, R. Juškaitis, and T. Wilson, “Adaptive aberration correction in a confocal microscope,” Proc. Natl. Acad. Sci. 99(9), 5788–5792 (2002).
[Crossref]

Wright, A. J.

J. M. Girkin, S. Poland, and A. J. Wright, “Adaptive optics for deeper imaging of biological samples,” Curr. Opin. Biotechnol. 20(1), 106–110 (2009).
[Crossref]

Wyant, J. C.

J. C. Wyant and K. Creath, “Basic wavefront aberration theory for optical metrology,” Applied optics and optical engineering 11, 28–39 (1992).

Yager, N.

S. Van der Walt, J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, and T. Yu, “scikit-image: image processing in python,” PeerJ 2, e453 (2014).
[Crossref]

Yu, T.

S. Van der Walt, J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, and T. Yu, “scikit-image: image processing in python,” PeerJ 2, e453 (2014).
[Crossref]

Zernike, F.

F. Zernike, “Diffraction theory of the cutting process and its improved form, the phase contrast method,” Physica 1, 689–704 (1934).

Zhu, L.

Zuo, Y.

Žurauskas, M.

Appl. Opt. (3)

Applied optics and optical engineering (1)

J. C. Wyant and K. Creath, “Basic wavefront aberration theory for optical metrology,” Applied optics and optical engineering 11, 28–39 (1992).

Biophys. J. (1)

M. G. Gustafsson, L. Shao, P. M. Carlton, C. R. Wang, I. N. Golubovskaya, W. Z. Cande, D. A. Agard, and J. W. Sedat, “Three-dimensional resolution doubling in wide-field fluorescence microscopy by structured illumination,” Biophys. J. 94(12), 4957–4970 (2008).
[Crossref]

Comput. Sci. Eng. (1)

S. v. d. Walt, S. C. Colbert, and G. Varoquaux, “The numpy array: a structure for efficient numerical computation,” Comput. Sci. Eng. 13(2), 22–30 (2011).
[Crossref]

Curr. Opin. Biotechnol. (1)

J. M. Girkin, S. Poland, and A. J. Wright, “Adaptive optics for deeper imaging of biological samples,” Curr. Opin. Biotechnol. 20(1), 106–110 (2009).
[Crossref]

Curr. Opin. Neurobiol. (1)

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

J. Microsc. (1)

M. Schwertner, M. J. Booth, M. A. Neil, and T. Wilson, “Measurement of specimen-induced aberrations of biological samples using phase stepping interferometry,” J. Microsc. 213(1), 11–19 (2004).
[Crossref]

J. Opt. Soc. Am. (2)

J. Fienup and J. Miller, “Aberration correction by maximizing generalized sharpness metrics,” J. Opt. Soc. Am. 20(4), 609–620 (2003).
[Crossref]

R. J. Noll, “Zernike polynomials and atmospheric turbulence,” J. Opt. Soc. Am. 66(3), 207–211 (1976).
[Crossref]

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. Methods (4)

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

K. Wang, D. E. Milkie, A. Saxena, P. Engerer, T. Misgeld, M. E. Bronner, J. Mumm, and E. Betzig, “Rapid adaptive optical recovery of optimal resolution over large volumes,” Nat. Methods 11(6), 625–628 (2014).
[Crossref]

P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, and J. Bright, “Scipy 1.0: fundamental algorithms for scientific computing in python,” Nat. Methods 17(3), 261–272 (2020).
[Crossref]

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]

Opt. Express (6)

Opt. Lett. (4)

Optica (2)

PeerJ (1)

S. Van der Walt, J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, and T. Yu, “scikit-image: image processing in python,” PeerJ 2, e453 (2014).
[Crossref]

PeerJ Comput. Sci. (1)

A. Meurer, C. P. Smith, M. Paprocki, O. Čertík, S. B. Kirpichev, M. Rocklin, A. Kumar, S. Ivanov, J. K. Moore, and S. Singh, “Sympy: symbolic computing in python,” PeerJ Comput. Sci. 3, e103e103 (2017).
[Crossref]

Philos. Trans. R. Soc., A (1)

M. J. Booth, “Adaptive optics in microscopy,” Philos. Trans. R. Soc., A 365(1861), 2829–2843 (2007).
[Crossref]

Physica (1)

F. Zernike, “Diffraction theory of the cutting process and its improved form, the phase contrast method,” Physica 1, 689–704 (1934).

Proc. Natl. Acad. Sci. (1)

M. J. Booth, M. A. Neil, R. Juškaitis, and T. Wilson, “Adaptive aberration correction in a confocal microscope,” Proc. Natl. Acad. Sci. 99(9), 5788–5792 (2002).
[Crossref]

Science (1)

E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, “Imaging intracellular fluorescent proteins at nanometer resolution,” Science 313(5793), 1642–1645 (2006).
[Crossref]

Wellcome Open Res. (1)

I. Dobbie, N. Hall, and D. Pinto, “Beamdelta: simple alignment tool for optical systems,” Wellcome Open Res. 4, 194 (2019).
[Crossref]

Other (3)

E. W. Weisstein, “Gibbs phenomenon,” https://mathworld.wolfram.com/ (2003).

P. Kner, Z. Kam, D. Agard, and J. Sedat, “Adaptive optics in wide-field microscopy,” MEMS Adaptive Optics V, vol. 7931 (International Society for Optics and Photonics, 2011), p. 79310K.

M. J. Booth, A basic introduction to adaptive optics for microscopy (University of Oxford, 2019).

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Figures (10)

Fig. 1.
Fig. 1. Flowchart depicting the general process for building a system utilising AO. a) System Design Phase. User decides whether the system needs AO and, if so, what type. b) Installation Phase. The chosen AO elements are added to the system. c) Set-up Phase. The chosen AO element is calibrated. Typically this involves mapping the variable components of the AO element (e.g. deformable mirror actuators) to a useful set of basis functions which represent optical aberrations. d) Sample Correction Phase Here the user designs the methods to be used for correcting their desired sample.
Fig. 2.
Fig. 2. (a) An example observed wavefront obtained during the calibration process for an Alpao-69 actuator deformable mirror. The $26^{th}$ actuator is at the first of the 5 positions. (b) A simulated wavefront created from the 69 Zernike mode amplitudes measured in the observed wavefront. (c) The difference in the observed and simulated wavefronts. (d) Plot of row vector, $\boldsymbol {z_{1}}$, of the 69 Zernike mode amplitudes measured for the observed wavefront (e) The influence function fitting for Zernike mode 11 (Noll index) for the $26^{\textrm {th}}$ actuator.
Fig. 3.
Fig. 3. (a) Flowchart depicting the generalised calibration routine implemented in Microscope-AOtools (b) Flowchart depicting the process for calibrating the $h$-th actuator of the deformable mirror, the dashed blue process in (a). The influence functions returned are $b_{g,h}$ described in Eq. (2). This process is performed for each of $N$ actuators and used to obtain $\boldsymbol {C}$ described in Eq. (5).
Fig. 4.
Fig. 4. Flowchart depicting the process for characterising an adaptive element as implemented in Microscope-AOtools.
Fig. 5.
Fig. 5. (a) An ideal characterisation assay, measuring the recreation accuracy of 68 Zernike modes with applied amplitude of 1 for each, (b) An example of a characterisation assay obtained from a calibrated Alpao-69 actuator deformable mirror, measuring the recreation accuracy of 68 Zernike modes with applied amplitude of 1 for each
Fig. 6.
Fig. 6. Flowchart depicting the process for correcting directly measured wavefront as implemented in Microscope-AOtools
Fig. 7.
Fig. 7. (a) An example aberrated wavefront. RMS wavefront error = 3.818 radians (b) The example wavefront after 20 iterations of correction. RMS wavefront error = 0.986 radians. RMS wavefront error of the central 95% of the phase wavefront = 0.712 radians (c) The Zernike modes measured in the aberrated (blue) and corrected (red) wavefronts (a) - (b) are all presented on the same colour scale (in radians of 543 nm HeNe laser) and were obtained via interferometry
Fig. 8.
Fig. 8. Principle of sensorless AO correction. The inset images are Drosophila Neuro-muscular Junction (NMJ). For each Zernike mode, $Z_i$, NMJ images are acquired for different amplitudes of the $i$-th Zernike mode. A value of the image quality metric, $S$, is obtained for each (blue dots). A Gaussian function is then fitted to these values and the amplitude, $a$ corresponding to the maximum image quality, $S_{max}$, is obtained (green dot). The inset figure for the green spot shows the NMJ image acquired after the correction for the $i$-th Zernike mode was applied
Fig. 9.
Fig. 9. Flowcharts depicting the sensorless correction routine options (a) An image for each amplitude of the $i$-th Zernike mode is taken and the image quality metric is immediately evaluated. Once all the images for the $i$-th Zernike mode have been taken, the best Zernike amplitude is found as described in Fig. 8 and applied, (b) All $M$ images are taken, then the quality metric is obtained for all $M$ images, the best Zernike amplitude is found and applied, (c) All the images for all the $N$ Zernike modes are obtained with no correction applied in between modes. The image quality metric then measured for every image and the best amplitude for each Zernike mode is found. The correction for all modes is applied simultaneously at the end of the workflow.
Fig. 10.
Fig. 10. (a) A simulated IsoSense pattern created with a 4-beam interference. (b) A diagram of a 4 beam interference pattern in Fourier space.

Equations (7)

Equations on this page are rendered with MathJax. Learn more.

S ( x , y ) = h = 1 N d h ϕ h ( x , y ) ,
ϕ h ( x , y ) = g = 1 M b g , h z g ( x , y ) ,
S ( x , y ) = h = 1 N d h [ g = 1 M b g , h z g ( x , y ) ] = g = 1 M ( h = 1 N d h b g , h ) z g ( x , y ) = g = 1 M a g z g ( x , y ) ,
a g = h = 1 N b g , h d h ~for~ g = 1 , 2 , , M .
a ¯ = B d ¯ d ¯ = C a ¯ ,
z = [ z 1 z 2 z g z m ] ,
A h = [ z 1 z 2 z p ] = [ z 1 , 1 z 1 , 2 z 1 , m z 2 , 1 z 2 , 2 z 2 , m z p , 1 z p , 2 z p , m ] .