Abstract

Focusing light through a step-index multimode optical fiber (MMF) using wavefront control enables minimally-invasive endoscopy of biological tissue. The point spread function (PSF) of such an imaging system is spatially variant, and this variation limits compensation for blurring using most deconvolution algorithms as they require a uniform PSF. However, modeling the spatially variant PSF into a series of spatially invariant PSFs re-opens the possibility of deconvolution. To achieve this we developed svmPSF: an open-source Java-based framework compatible with ImageJ. The approach takes a series of point response measurements across the field-of-view (FOV) and applies principal component analysis to the measurements' co-variance matrix to generate a PSF model. By combining the svmPSF output with a modified Richardson-Lucy deconvolution algorithm, we were able to deblur and regularize fluorescence images of beads and live neurons acquired with a MMF, and thus effectively increasing the FOV.

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2019 (4)

R. Turcotte, Y. Liang, M. Tanimoto, Q. Zhang, Z. Li, M. Koyama, E. Betzig, and N. Ji, “Dynamic super-resolution structured illumination imaging in the living brain,” Proc. Natl. Acad. Sci. U. S. A. 116(19), 9586–9591 (2019).
[Crossref]

T. Yan, C. J. Richardson, M. Zhang, and A. Gahlmann, “Computational correction of spatially variant optical aberrations in 3D single-molecule localization microscopy,” Opt. Express 27(9), 12582–12599 (2019).
[Crossref]

M. Anastasopoulou, D. Gorpas, M. Koch, E. Liapis, S. Glasl, U. Klemm, A. Karlas, T. Lasser, and V. Ntziachristos, “Fluorescence imaging reversion using spatially variant deconvolution,” Sci. Rep. 9(1), 18123 (2019).
[Crossref]

R. Turcotte, C. C. Schmidt, N. J. Emptage, and M. J. Booth, “Focusing light in biological tissue through a multimode optical fiber: refractive index matching,” Opt. Lett. 44(10), 2386–2389 (2019).
[Crossref]

2018 (3)

S. A. Vasquez-Lopez, R. Turcotte, V. Koren, M. Plöschner, Z. Padamsey, M. J. Booth, T. Čižmár, and N. J. Emptage, “Subcellular spatial resolution achieved for deep-brain imaging in vivo using a minimally invasive multimode fiber,” Light: Sci. Appl. 7(1), 110 (2018).
[Crossref]

S. Ohayon, A. Caravaca-Aguirre, R. Piestun, and J. J. DiCarlo, “Minimally invasive multimode optical fiber microendoscope for deep brain fluorescence imaging,” Biomed. Opt. Express 9(4), 1492–1509 (2018).
[Crossref]

S. Turtaev, I. T. Leite, T. Altwegg-Boussac, J. M. P. Pakan, N. L. Rochefort, and T. Čižmár, “High-fidelity multimode fibre-based endoscopy for deep brain in vivo imaging,” Light: Sci. Appl. 7(1), 92 (2018).
[Crossref]

2017 (3)

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An open-source software for deconvolution microscopy,” Methods 115, 28–41 (2017).
[Crossref]

D. Loterie, D. Psaltis, and C. Moser, “Bend translation in multimode fiber imaging,” Opt. Express 25(6), 6263–6273 (2017).
[Crossref]

R. Lu, W. Sun, Y. Liang, A. Kerlin, J. Bierfeld, J. D. Seelig, D. E. Wilson, B. Scholl, B. Mohar, M. Tanimoto, M. Koyama, D. Fitzpatrick, M. B. Orger, and N. Ji, “Video-rate volumetric functional imaging of the brain at synaptic resolution,” Nat. Neurosci. 20(4), 620–628 (2017).
[Crossref]

2016 (1)

2015 (5)

2014 (2)

J. Tønnesen, G. Katona, B. Rózsa, and U. V. Nägerl, “Spine neck plasticity regulates compartmentalization of synapses,” Nat. Neurosci. 17(5), 678–685 (2014).
[Crossref]

S. B. Hadj, L. Blanc-Feraud, and G. Aubert, “Space Variant Blind Image Restoration,” SIAM J. Imaging Sci. 7(4), 2196–2225 (2014).
[Crossref]

2013 (1)

2012 (1)

T. Čižmár and K. Dholakia, “Exploiting multimode waveguides for pure fibre-based imaging,” Nat. Commun. 3(1), 1027 (2012).
[Crossref]

2011 (2)

2010 (1)

2006 (1)

N. Dey, L. Blanc-Feraud, C. Zimmer, P. Roux, Z. Kam, J.-C. Olivo-Marin, and J. Zerubia, “Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution,” Microsc. Res. Tech. 69(4), 260–266 (2006).
[Crossref]

2005 (1)

1991 (1)

L. Stoppini, P. A. Buchs, and D. Muller, “A Simple Method for Organotypic Cultures of Nervous-Tissue,” J. Neurosci. Methods 37(2), 173–182 (1991).
[Crossref]

Agard, D. A.

Altwegg-Boussac, T.

S. Turtaev, I. T. Leite, T. Altwegg-Boussac, J. M. P. Pakan, N. L. Rochefort, and T. Čižmár, “High-fidelity multimode fibre-based endoscopy for deep brain in vivo imaging,” Light: Sci. Appl. 7(1), 92 (2018).
[Crossref]

Anastasopoulou, M.

M. Anastasopoulou, D. Gorpas, M. Koch, E. Liapis, S. Glasl, U. Klemm, A. Karlas, T. Lasser, and V. Ntziachristos, “Fluorescence imaging reversion using spatially variant deconvolution,” Sci. Rep. 9(1), 18123 (2019).
[Crossref]

Arigovindan, M.

Aubert, G.

S. B. Hadj, L. Blanc-Feraud, and G. Aubert, “Space Variant Blind Image Restoration,” SIAM J. Imaging Sci. 7(4), 2196–2225 (2014).
[Crossref]

Becker, J.-M.

L. Denis, E. Thiébaut, F. Soulez, J.-M. Becker, and R. Mourya, “Fast approximations of shift-variant blur,” Int. J. Comput. Vis. 115(3), 253–278 (2015).
[Crossref]

Betzig, E.

R. Turcotte, Y. Liang, M. Tanimoto, Q. Zhang, Z. Li, M. Koyama, E. Betzig, and N. Ji, “Dynamic super-resolution structured illumination imaging in the living brain,” Proc. Natl. Acad. Sci. U. S. A. 116(19), 9586–9591 (2019).
[Crossref]

Bianchi, S.

Bierfeld, J.

R. Lu, W. Sun, Y. Liang, A. Kerlin, J. Bierfeld, J. D. Seelig, D. E. Wilson, B. Scholl, B. Mohar, M. Tanimoto, M. Koyama, D. Fitzpatrick, M. B. Orger, and N. Ji, “Video-rate volumetric functional imaging of the brain at synaptic resolution,” Nat. Neurosci. 20(4), 620–628 (2017).
[Crossref]

Blanc-Feraud, L.

S. B. Hadj, L. Blanc-Feraud, and G. Aubert, “Space Variant Blind Image Restoration,” SIAM J. Imaging Sci. 7(4), 2196–2225 (2014).
[Crossref]

N. Dey, L. Blanc-Feraud, C. Zimmer, P. Roux, Z. Kam, J.-C. Olivo-Marin, and J. Zerubia, “Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution,” Microsc. Res. Tech. 69(4), 260–266 (2006).
[Crossref]

Booth, M. J.

R. Turcotte, C. C. Schmidt, N. J. Emptage, and M. J. Booth, “Focusing light in biological tissue through a multimode optical fiber: refractive index matching,” Opt. Lett. 44(10), 2386–2389 (2019).
[Crossref]

S. A. Vasquez-Lopez, R. Turcotte, V. Koren, M. Plöschner, Z. Padamsey, M. J. Booth, T. Čižmár, and N. J. Emptage, “Subcellular spatial resolution achieved for deep-brain imaging in vivo using a minimally invasive multimode fiber,” Light: Sci. Appl. 7(1), 110 (2018).
[Crossref]

R. Turcotte, C. C. Schmidt, M. J. Booth, and N. J. Emptage, “Two-photon fluorescence imaging of live neurons using a multimode optical fiber,” bioRxiv p. 2020.04.27.063388 (2020).

Buchs, P. A.

L. Stoppini, P. A. Buchs, and D. Muller, “A Simple Method for Organotypic Cultures of Nervous-Tissue,” J. Neurosci. Methods 37(2), 173–182 (1991).
[Crossref]

Caravaca-Aguirre, A.

Chacko, N.

N. Chacko and M. Liebling, “Fast spatially variant deconvolution for optical microscopy via iterative shrinkage thresholding,” in IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2838–2842, (2014).

Cižmár, T.

S. A. Vasquez-Lopez, R. Turcotte, V. Koren, M. Plöschner, Z. Padamsey, M. J. Booth, T. Čižmár, and N. J. Emptage, “Subcellular spatial resolution achieved for deep-brain imaging in vivo using a minimally invasive multimode fiber,” Light: Sci. Appl. 7(1), 110 (2018).
[Crossref]

S. Turtaev, I. T. Leite, T. Altwegg-Boussac, J. M. P. Pakan, N. L. Rochefort, and T. Čižmár, “High-fidelity multimode fibre-based endoscopy for deep brain in vivo imaging,” Light: Sci. Appl. 7(1), 92 (2018).
[Crossref]

M. Plöschner and T. Čižmár, “Compact multimode fiber beam-shaping system based on GPU accelerated digital holography,” Opt. Lett. 40(2), 197–200 (2015).
[Crossref]

T. Čižmár and K. Dholakia, “Exploiting multimode waveguides for pure fibre-based imaging,” Nat. Commun. 3(1), 1027 (2012).
[Crossref]

Colicchio, B.

Denis, L.

L. Denis, E. Thiébaut, F. Soulez, J.-M. Becker, and R. Mourya, “Fast approximations of shift-variant blur,” Int. J. Comput. Vis. 115(3), 253–278 (2015).
[Crossref]

Dey, N.

N. Dey, L. Blanc-Feraud, C. Zimmer, P. Roux, Z. Kam, J.-C. Olivo-Marin, and J. Zerubia, “Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution,” Microsc. Res. Tech. 69(4), 260–266 (2006).
[Crossref]

Dholakia, K.

T. Čižmár and K. Dholakia, “Exploiting multimode waveguides for pure fibre-based imaging,” Nat. Commun. 3(1), 1027 (2012).
[Crossref]

DiCarlo, J. J.

Dieterlen, A.

Donati, L.

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An open-source software for deconvolution microscopy,” Methods 115, 28–41 (2017).
[Crossref]

Emptage, N. J.

R. Turcotte, C. C. Schmidt, N. J. Emptage, and M. J. Booth, “Focusing light in biological tissue through a multimode optical fiber: refractive index matching,” Opt. Lett. 44(10), 2386–2389 (2019).
[Crossref]

S. A. Vasquez-Lopez, R. Turcotte, V. Koren, M. Plöschner, Z. Padamsey, M. J. Booth, T. Čižmár, and N. J. Emptage, “Subcellular spatial resolution achieved for deep-brain imaging in vivo using a minimally invasive multimode fiber,” Light: Sci. Appl. 7(1), 110 (2018).
[Crossref]

R. Turcotte, C. C. Schmidt, M. J. Booth, and N. J. Emptage, “Two-photon fluorescence imaging of live neurons using a multimode optical fiber,” bioRxiv p. 2020.04.27.063388 (2020).

Fitzpatrick, D.

R. Lu, W. Sun, Y. Liang, A. Kerlin, J. Bierfeld, J. D. Seelig, D. E. Wilson, B. Scholl, B. Mohar, M. Tanimoto, M. Koyama, D. Fitzpatrick, M. B. Orger, and N. Ji, “Video-rate volumetric functional imaging of the brain at synaptic resolution,” Nat. Neurosci. 20(4), 620–628 (2017).
[Crossref]

Flicker, R. C.

Fortun, D.

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An open-source software for deconvolution microscopy,” Methods 115, 28–41 (2017).
[Crossref]

Gahlmann, A.

Glasl, S.

M. Anastasopoulou, D. Gorpas, M. Koch, E. Liapis, S. Glasl, U. Klemm, A. Karlas, T. Lasser, and V. Ntziachristos, “Fluorescence imaging reversion using spatially variant deconvolution,” Sci. Rep. 9(1), 18123 (2019).
[Crossref]

Gorpas, D.

M. Anastasopoulou, D. Gorpas, M. Koch, E. Liapis, S. Glasl, U. Klemm, A. Karlas, T. Lasser, and V. Ntziachristos, “Fluorescence imaging reversion using spatially variant deconvolution,” Sci. Rep. 9(1), 18123 (2019).
[Crossref]

Gu, R. Y.

Guiet, R.

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An open-source software for deconvolution microscopy,” Methods 115, 28–41 (2017).
[Crossref]

Hadj, S. B.

S. B. Hadj, L. Blanc-Feraud, and G. Aubert, “Space Variant Blind Image Restoration,” SIAM J. Imaging Sci. 7(4), 2196–2225 (2014).
[Crossref]

Harmeling, S.

M. Hirsch, S. Sra, B. Schölkopf, and S. Harmeling, “Efficient filter flow for space-variant multiframe blind deconvolution,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (2010), pp. 607–614.

Hirsch, M.

M. Hirsch, S. Sra, B. Schölkopf, and S. Harmeling, “Efficient filter flow for space-variant multiframe blind deconvolution,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (2010), pp. 607–614.

Ji, N.

R. Turcotte, Y. Liang, M. Tanimoto, Q. Zhang, Z. Li, M. Koyama, E. Betzig, and N. Ji, “Dynamic super-resolution structured illumination imaging in the living brain,” Proc. Natl. Acad. Sci. U. S. A. 116(19), 9586–9591 (2019).
[Crossref]

R. Lu, W. Sun, Y. Liang, A. Kerlin, J. Bierfeld, J. D. Seelig, D. E. Wilson, B. Scholl, B. Mohar, M. Tanimoto, M. Koyama, D. Fitzpatrick, M. B. Orger, and N. Ji, “Video-rate volumetric functional imaging of the brain at synaptic resolution,” Nat. Neurosci. 20(4), 620–628 (2017).
[Crossref]

Kahn, J. M.

Kalkman, J.

Kam, Z.

N. Dey, L. Blanc-Feraud, C. Zimmer, P. Roux, Z. Kam, J.-C. Olivo-Marin, and J. Zerubia, “Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution,” Microsc. Res. Tech. 69(4), 260–266 (2006).
[Crossref]

Karlas, A.

M. Anastasopoulou, D. Gorpas, M. Koch, E. Liapis, S. Glasl, U. Klemm, A. Karlas, T. Lasser, and V. Ntziachristos, “Fluorescence imaging reversion using spatially variant deconvolution,” Sci. Rep. 9(1), 18123 (2019).
[Crossref]

Katona, G.

J. Tønnesen, G. Katona, B. Rózsa, and U. V. Nägerl, “Spine neck plasticity regulates compartmentalization of synapses,” Nat. Neurosci. 17(5), 678–685 (2014).
[Crossref]

Kerlin, A.

R. Lu, W. Sun, Y. Liang, A. Kerlin, J. Bierfeld, J. D. Seelig, D. E. Wilson, B. Scholl, B. Mohar, M. Tanimoto, M. Koyama, D. Fitzpatrick, M. B. Orger, and N. Ji, “Video-rate volumetric functional imaging of the brain at synaptic resolution,” Nat. Neurosci. 20(4), 620–628 (2017).
[Crossref]

Kim, B.

B. Kim and T. Naemura, “Blind depth-variant deconvolution of 3D data in wide-field fluorescence microscopy,” Sci. Rep. 5(1), 9894 (2015).
[Crossref]

Klemm, U.

M. Anastasopoulou, D. Gorpas, M. Koch, E. Liapis, S. Glasl, U. Klemm, A. Karlas, T. Lasser, and V. Ntziachristos, “Fluorescence imaging reversion using spatially variant deconvolution,” Sci. Rep. 9(1), 18123 (2019).
[Crossref]

Koch, M.

M. Anastasopoulou, D. Gorpas, M. Koch, E. Liapis, S. Glasl, U. Klemm, A. Karlas, T. Lasser, and V. Ntziachristos, “Fluorescence imaging reversion using spatially variant deconvolution,” Sci. Rep. 9(1), 18123 (2019).
[Crossref]

Koren, V.

S. A. Vasquez-Lopez, R. Turcotte, V. Koren, M. Plöschner, Z. Padamsey, M. J. Booth, T. Čižmár, and N. J. Emptage, “Subcellular spatial resolution achieved for deep-brain imaging in vivo using a minimally invasive multimode fiber,” Light: Sci. Appl. 7(1), 110 (2018).
[Crossref]

Koyama, M.

R. Turcotte, Y. Liang, M. Tanimoto, Q. Zhang, Z. Li, M. Koyama, E. Betzig, and N. Ji, “Dynamic super-resolution structured illumination imaging in the living brain,” Proc. Natl. Acad. Sci. U. S. A. 116(19), 9586–9591 (2019).
[Crossref]

R. Lu, W. Sun, Y. Liang, A. Kerlin, J. Bierfeld, J. D. Seelig, D. E. Wilson, B. Scholl, B. Mohar, M. Tanimoto, M. Koyama, D. Fitzpatrick, M. B. Orger, and N. Ji, “Video-rate volumetric functional imaging of the brain at synaptic resolution,” Nat. Neurosci. 20(4), 620–628 (2017).
[Crossref]

Lasser, T.

M. Anastasopoulou, D. Gorpas, M. Koch, E. Liapis, S. Glasl, U. Klemm, A. Karlas, T. Lasser, and V. Ntziachristos, “Fluorescence imaging reversion using spatially variant deconvolution,” Sci. Rep. 9(1), 18123 (2019).
[Crossref]

Lauer, T. R.

T. R. Lauer, “Deconvolution with a spatially-variant PSF,” in Proc. SPIE 4847, Astronomical Data Analysis II, (2002), pp. 167–173.

Leite, I. T.

S. Turtaev, I. T. Leite, T. Altwegg-Boussac, J. M. P. Pakan, N. L. Rochefort, and T. Čižmár, “High-fidelity multimode fibre-based endoscopy for deep brain in vivo imaging,” Light: Sci. Appl. 7(1), 92 (2018).
[Crossref]

Leonardo, R. D.

Li, Z.

R. Turcotte, Y. Liang, M. Tanimoto, Q. Zhang, Z. Li, M. Koyama, E. Betzig, and N. Ji, “Dynamic super-resolution structured illumination imaging in the living brain,” Proc. Natl. Acad. Sci. U. S. A. 116(19), 9586–9591 (2019).
[Crossref]

Liang, Y.

R. Turcotte, Y. Liang, M. Tanimoto, Q. Zhang, Z. Li, M. Koyama, E. Betzig, and N. Ji, “Dynamic super-resolution structured illumination imaging in the living brain,” Proc. Natl. Acad. Sci. U. S. A. 116(19), 9586–9591 (2019).
[Crossref]

R. Lu, W. Sun, Y. Liang, A. Kerlin, J. Bierfeld, J. D. Seelig, D. E. Wilson, B. Scholl, B. Mohar, M. Tanimoto, M. Koyama, D. Fitzpatrick, M. B. Orger, and N. Ji, “Video-rate volumetric functional imaging of the brain at synaptic resolution,” Nat. Neurosci. 20(4), 620–628 (2017).
[Crossref]

Liapis, E.

M. Anastasopoulou, D. Gorpas, M. Koch, E. Liapis, S. Glasl, U. Klemm, A. Karlas, T. Lasser, and V. Ntziachristos, “Fluorescence imaging reversion using spatially variant deconvolution,” Sci. Rep. 9(1), 18123 (2019).
[Crossref]

Liebling, M.

N. Chacko and M. Liebling, “Fast spatially variant deconvolution for optical microscopy via iterative shrinkage thresholding,” in IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2838–2842, (2014).

A. Shajkofci and M. Liebling, “Semi-blind spatially-variant deconvolution in optical microscopy with local point spread function estimation by use of convolutional neural networks,” in International Conference on Image Processing, (2018), pp. 3818–3822.

Loterie, D.

Lu, R.

R. Lu, W. Sun, Y. Liang, A. Kerlin, J. Bierfeld, J. D. Seelig, D. E. Wilson, B. Scholl, B. Mohar, M. Tanimoto, M. Koyama, D. Fitzpatrick, M. B. Orger, and N. Ji, “Video-rate volumetric functional imaging of the brain at synaptic resolution,” Nat. Neurosci. 20(4), 620–628 (2017).
[Crossref]

Maalouf, E.

Mahalati, R. N.

McGowan, J.

Mohar, B.

R. Lu, W. Sun, Y. Liang, A. Kerlin, J. Bierfeld, J. D. Seelig, D. E. Wilson, B. Scholl, B. Mohar, M. Tanimoto, M. Koyama, D. Fitzpatrick, M. B. Orger, and N. Ji, “Video-rate volumetric functional imaging of the brain at synaptic resolution,” Nat. Neurosci. 20(4), 620–628 (2017).
[Crossref]

Morales-Delgado, E. E.

Moser, C.

Mourya, R.

L. Denis, E. Thiébaut, F. Soulez, J.-M. Becker, and R. Mourya, “Fast approximations of shift-variant blur,” Int. J. Comput. Vis. 115(3), 253–278 (2015).
[Crossref]

Muller, D.

L. Stoppini, P. A. Buchs, and D. Muller, “A Simple Method for Organotypic Cultures of Nervous-Tissue,” J. Neurosci. Methods 37(2), 173–182 (1991).
[Crossref]

Naemura, T.

B. Kim and T. Naemura, “Blind depth-variant deconvolution of 3D data in wide-field fluorescence microscopy,” Sci. Rep. 5(1), 9894 (2015).
[Crossref]

Nägerl, U. V.

J. Tønnesen, G. Katona, B. Rózsa, and U. V. Nägerl, “Spine neck plasticity regulates compartmentalization of synapses,” Nat. Neurosci. 17(5), 678–685 (2014).
[Crossref]

Ntziachristos, V.

M. Anastasopoulou, D. Gorpas, M. Koch, E. Liapis, S. Glasl, U. Klemm, A. Karlas, T. Lasser, and V. Ntziachristos, “Fluorescence imaging reversion using spatially variant deconvolution,” Sci. Rep. 9(1), 18123 (2019).
[Crossref]

Ohayon, S.

Olivo-Marin, J.-C.

N. Dey, L. Blanc-Feraud, C. Zimmer, P. Roux, Z. Kam, J.-C. Olivo-Marin, and J. Zerubia, “Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution,” Microsc. Res. Tech. 69(4), 260–266 (2006).
[Crossref]

Orger, M. B.

R. Lu, W. Sun, Y. Liang, A. Kerlin, J. Bierfeld, J. D. Seelig, D. E. Wilson, B. Scholl, B. Mohar, M. Tanimoto, M. Koyama, D. Fitzpatrick, M. B. Orger, and N. Ji, “Video-rate volumetric functional imaging of the brain at synaptic resolution,” Nat. Neurosci. 20(4), 620–628 (2017).
[Crossref]

Padamsey, Z.

S. A. Vasquez-Lopez, R. Turcotte, V. Koren, M. Plöschner, Z. Padamsey, M. J. Booth, T. Čižmár, and N. J. Emptage, “Subcellular spatial resolution achieved for deep-brain imaging in vivo using a minimally invasive multimode fiber,” Light: Sci. Appl. 7(1), 110 (2018).
[Crossref]

Pakan, J. M. P.

S. Turtaev, I. T. Leite, T. Altwegg-Boussac, J. M. P. Pakan, N. L. Rochefort, and T. Čižmár, “High-fidelity multimode fibre-based endoscopy for deep brain in vivo imaging,” Light: Sci. Appl. 7(1), 92 (2018).
[Crossref]

Patwary, N.

Piestun, R.

Plöschner, M.

S. A. Vasquez-Lopez, R. Turcotte, V. Koren, M. Plöschner, Z. Padamsey, M. J. Booth, T. Čižmár, and N. J. Emptage, “Subcellular spatial resolution achieved for deep-brain imaging in vivo using a minimally invasive multimode fiber,” Light: Sci. Appl. 7(1), 110 (2018).
[Crossref]

M. Plöschner and T. Čižmár, “Compact multimode fiber beam-shaping system based on GPU accelerated digital holography,” Opt. Lett. 40(2), 197–200 (2015).
[Crossref]

Preza, C.

Psaltis, D.

Richardson, C. J.

Rigaut, F. J.

Rochefort, N. L.

S. Turtaev, I. T. Leite, T. Altwegg-Boussac, J. M. P. Pakan, N. L. Rochefort, and T. Čižmár, “High-fidelity multimode fibre-based endoscopy for deep brain in vivo imaging,” Light: Sci. Appl. 7(1), 92 (2018).
[Crossref]

Roux, P.

N. Dey, L. Blanc-Feraud, C. Zimmer, P. Roux, Z. Kam, J.-C. Olivo-Marin, and J. Zerubia, “Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution,” Microsc. Res. Tech. 69(4), 260–266 (2006).
[Crossref]

Rózsa, B.

J. Tønnesen, G. Katona, B. Rózsa, and U. V. Nägerl, “Spine neck plasticity regulates compartmentalization of synapses,” Nat. Neurosci. 17(5), 678–685 (2014).
[Crossref]

Sage, D.

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An open-source software for deconvolution microscopy,” Methods 115, 28–41 (2017).
[Crossref]

Schmidt, C. C.

R. Turcotte, C. C. Schmidt, N. J. Emptage, and M. J. Booth, “Focusing light in biological tissue through a multimode optical fiber: refractive index matching,” Opt. Lett. 44(10), 2386–2389 (2019).
[Crossref]

R. Turcotte, C. C. Schmidt, M. J. Booth, and N. J. Emptage, “Two-photon fluorescence imaging of live neurons using a multimode optical fiber,” bioRxiv p. 2020.04.27.063388 (2020).

Schmit, G.

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An open-source software for deconvolution microscopy,” Methods 115, 28–41 (2017).
[Crossref]

Schölkopf, B.

M. Hirsch, S. Sra, B. Schölkopf, and S. Harmeling, “Efficient filter flow for space-variant multiframe blind deconvolution,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (2010), pp. 607–614.

Scholl, B.

R. Lu, W. Sun, Y. Liang, A. Kerlin, J. Bierfeld, J. D. Seelig, D. E. Wilson, B. Scholl, B. Mohar, M. Tanimoto, M. Koyama, D. Fitzpatrick, M. B. Orger, and N. Ji, “Video-rate volumetric functional imaging of the brain at synaptic resolution,” Nat. Neurosci. 20(4), 620–628 (2017).
[Crossref]

Sedat, J. W.

Seelig, J. D.

R. Lu, W. Sun, Y. Liang, A. Kerlin, J. Bierfeld, J. D. Seelig, D. E. Wilson, B. Scholl, B. Mohar, M. Tanimoto, M. Koyama, D. Fitzpatrick, M. B. Orger, and N. Ji, “Video-rate volumetric functional imaging of the brain at synaptic resolution,” Nat. Neurosci. 20(4), 620–628 (2017).
[Crossref]

Seitz, A.

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An open-source software for deconvolution microscopy,” Methods 115, 28–41 (2017).
[Crossref]

Shaevitz, J.

Shajkofci, A.

A. Shajkofci and M. Liebling, “Semi-blind spatially-variant deconvolution in optical microscopy with local point spread function estimation by use of convolutional neural networks,” in International Conference on Image Processing, (2018), pp. 3818–3822.

Soulez, F.

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An open-source software for deconvolution microscopy,” Methods 115, 28–41 (2017).
[Crossref]

L. Denis, E. Thiébaut, F. Soulez, J.-M. Becker, and R. Mourya, “Fast approximations of shift-variant blur,” Int. J. Comput. Vis. 115(3), 253–278 (2015).
[Crossref]

Sra, S.

M. Hirsch, S. Sra, B. Schölkopf, and S. Harmeling, “Efficient filter flow for space-variant multiframe blind deconvolution,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (2010), pp. 607–614.

Stoppini, L.

L. Stoppini, P. A. Buchs, and D. Muller, “A Simple Method for Organotypic Cultures of Nervous-Tissue,” J. Neurosci. Methods 37(2), 173–182 (1991).
[Crossref]

Sun, W.

R. Lu, W. Sun, Y. Liang, A. Kerlin, J. Bierfeld, J. D. Seelig, D. E. Wilson, B. Scholl, B. Mohar, M. Tanimoto, M. Koyama, D. Fitzpatrick, M. B. Orger, and N. Ji, “Video-rate volumetric functional imaging of the brain at synaptic resolution,” Nat. Neurosci. 20(4), 620–628 (2017).
[Crossref]

Tanimoto, M.

R. Turcotte, Y. Liang, M. Tanimoto, Q. Zhang, Z. Li, M. Koyama, E. Betzig, and N. Ji, “Dynamic super-resolution structured illumination imaging in the living brain,” Proc. Natl. Acad. Sci. U. S. A. 116(19), 9586–9591 (2019).
[Crossref]

R. Lu, W. Sun, Y. Liang, A. Kerlin, J. Bierfeld, J. D. Seelig, D. E. Wilson, B. Scholl, B. Mohar, M. Tanimoto, M. Koyama, D. Fitzpatrick, M. B. Orger, and N. Ji, “Video-rate volumetric functional imaging of the brain at synaptic resolution,” Nat. Neurosci. 20(4), 620–628 (2017).
[Crossref]

Thiébaut, E.

L. Denis, E. Thiébaut, F. Soulez, J.-M. Becker, and R. Mourya, “Fast approximations of shift-variant blur,” Int. J. Comput. Vis. 115(3), 253–278 (2015).
[Crossref]

Tønnesen, J.

J. Tønnesen, G. Katona, B. Rózsa, and U. V. Nägerl, “Spine neck plasticity regulates compartmentalization of synapses,” Nat. Neurosci. 17(5), 678–685 (2014).
[Crossref]

Turcotte, R.

R. Turcotte, Y. Liang, M. Tanimoto, Q. Zhang, Z. Li, M. Koyama, E. Betzig, and N. Ji, “Dynamic super-resolution structured illumination imaging in the living brain,” Proc. Natl. Acad. Sci. U. S. A. 116(19), 9586–9591 (2019).
[Crossref]

R. Turcotte, C. C. Schmidt, N. J. Emptage, and M. J. Booth, “Focusing light in biological tissue through a multimode optical fiber: refractive index matching,” Opt. Lett. 44(10), 2386–2389 (2019).
[Crossref]

S. A. Vasquez-Lopez, R. Turcotte, V. Koren, M. Plöschner, Z. Padamsey, M. J. Booth, T. Čižmár, and N. J. Emptage, “Subcellular spatial resolution achieved for deep-brain imaging in vivo using a minimally invasive multimode fiber,” Light: Sci. Appl. 7(1), 110 (2018).
[Crossref]

R. Turcotte, C. C. Schmidt, M. J. Booth, and N. J. Emptage, “Two-photon fluorescence imaging of live neurons using a multimode optical fiber,” bioRxiv p. 2020.04.27.063388 (2020).

Turtaev, S.

S. Turtaev, I. T. Leite, T. Altwegg-Boussac, J. M. P. Pakan, N. L. Rochefort, and T. Čižmár, “High-fidelity multimode fibre-based endoscopy for deep brain in vivo imaging,” Light: Sci. Appl. 7(1), 92 (2018).
[Crossref]

Unser, M.

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An open-source software for deconvolution microscopy,” Methods 115, 28–41 (2017).
[Crossref]

van der Horst, J.

Vasquez-Lopez, S. A.

S. A. Vasquez-Lopez, R. Turcotte, V. Koren, M. Plöschner, Z. Padamsey, M. J. Booth, T. Čižmár, and N. J. Emptage, “Subcellular spatial resolution achieved for deep-brain imaging in vivo using a minimally invasive multimode fiber,” Light: Sci. Appl. 7(1), 110 (2018).
[Crossref]

Vonesch, C.

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An open-source software for deconvolution microscopy,” Methods 115, 28–41 (2017).
[Crossref]

Wilson, D. E.

R. Lu, W. Sun, Y. Liang, A. Kerlin, J. Bierfeld, J. D. Seelig, D. E. Wilson, B. Scholl, B. Mohar, M. Tanimoto, M. Koyama, D. Fitzpatrick, M. B. Orger, and N. Ji, “Video-rate volumetric functional imaging of the brain at synaptic resolution,” Nat. Neurosci. 20(4), 620–628 (2017).
[Crossref]

Yan, T.

Zerubia, J.

N. Dey, L. Blanc-Feraud, C. Zimmer, P. Roux, Z. Kam, J.-C. Olivo-Marin, and J. Zerubia, “Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution,” Microsc. Res. Tech. 69(4), 260–266 (2006).
[Crossref]

Zhang, M.

Zhang, Q.

R. Turcotte, Y. Liang, M. Tanimoto, Q. Zhang, Z. Li, M. Koyama, E. Betzig, and N. Ji, “Dynamic super-resolution structured illumination imaging in the living brain,” Proc. Natl. Acad. Sci. U. S. A. 116(19), 9586–9591 (2019).
[Crossref]

Zimmer, C.

N. Dey, L. Blanc-Feraud, C. Zimmer, P. Roux, Z. Kam, J.-C. Olivo-Marin, and J. Zerubia, “Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution,” Microsc. Res. Tech. 69(4), 260–266 (2006).
[Crossref]

Biomed. Opt. Express (2)

Int. J. Comput. Vis. (1)

L. Denis, E. Thiébaut, F. Soulez, J.-M. Becker, and R. Mourya, “Fast approximations of shift-variant blur,” Int. J. Comput. Vis. 115(3), 253–278 (2015).
[Crossref]

J. Neurosci. Methods (1)

L. Stoppini, P. A. Buchs, and D. Muller, “A Simple Method for Organotypic Cultures of Nervous-Tissue,” J. Neurosci. Methods 37(2), 173–182 (1991).
[Crossref]

J. Opt. Soc. Am. A (2)

Light: Sci. Appl. (2)

S. Turtaev, I. T. Leite, T. Altwegg-Boussac, J. M. P. Pakan, N. L. Rochefort, and T. Čižmár, “High-fidelity multimode fibre-based endoscopy for deep brain in vivo imaging,” Light: Sci. Appl. 7(1), 92 (2018).
[Crossref]

S. A. Vasquez-Lopez, R. Turcotte, V. Koren, M. Plöschner, Z. Padamsey, M. J. Booth, T. Čižmár, and N. J. Emptage, “Subcellular spatial resolution achieved for deep-brain imaging in vivo using a minimally invasive multimode fiber,” Light: Sci. Appl. 7(1), 110 (2018).
[Crossref]

Methods (1)

D. Sage, L. Donati, F. Soulez, D. Fortun, G. Schmit, A. Seitz, R. Guiet, C. Vonesch, and M. Unser, “DeconvolutionLab2: An open-source software for deconvolution microscopy,” Methods 115, 28–41 (2017).
[Crossref]

Microsc. Res. Tech. (1)

N. Dey, L. Blanc-Feraud, C. Zimmer, P. Roux, Z. Kam, J.-C. Olivo-Marin, and J. Zerubia, “Richardson-Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution,” Microsc. Res. Tech. 69(4), 260–266 (2006).
[Crossref]

Nat. Commun. (1)

T. Čižmár and K. Dholakia, “Exploiting multimode waveguides for pure fibre-based imaging,” Nat. Commun. 3(1), 1027 (2012).
[Crossref]

Nat. Neurosci. (2)

J. Tønnesen, G. Katona, B. Rózsa, and U. V. Nägerl, “Spine neck plasticity regulates compartmentalization of synapses,” Nat. Neurosci. 17(5), 678–685 (2014).
[Crossref]

R. Lu, W. Sun, Y. Liang, A. Kerlin, J. Bierfeld, J. D. Seelig, D. E. Wilson, B. Scholl, B. Mohar, M. Tanimoto, M. Koyama, D. Fitzpatrick, M. B. Orger, and N. Ji, “Video-rate volumetric functional imaging of the brain at synaptic resolution,” Nat. Neurosci. 20(4), 620–628 (2017).
[Crossref]

Opt. Express (7)

Opt. Lett. (2)

Proc. Natl. Acad. Sci. U. S. A. (1)

R. Turcotte, Y. Liang, M. Tanimoto, Q. Zhang, Z. Li, M. Koyama, E. Betzig, and N. Ji, “Dynamic super-resolution structured illumination imaging in the living brain,” Proc. Natl. Acad. Sci. U. S. A. 116(19), 9586–9591 (2019).
[Crossref]

Sci. Rep. (2)

M. Anastasopoulou, D. Gorpas, M. Koch, E. Liapis, S. Glasl, U. Klemm, A. Karlas, T. Lasser, and V. Ntziachristos, “Fluorescence imaging reversion using spatially variant deconvolution,” Sci. Rep. 9(1), 18123 (2019).
[Crossref]

B. Kim and T. Naemura, “Blind depth-variant deconvolution of 3D data in wide-field fluorescence microscopy,” Sci. Rep. 5(1), 9894 (2015).
[Crossref]

SIAM J. Imaging Sci. (1)

S. B. Hadj, L. Blanc-Feraud, and G. Aubert, “Space Variant Blind Image Restoration,” SIAM J. Imaging Sci. 7(4), 2196–2225 (2014).
[Crossref]

Other (5)

A. Shajkofci and M. Liebling, “Semi-blind spatially-variant deconvolution in optical microscopy with local point spread function estimation by use of convolutional neural networks,” in International Conference on Image Processing, (2018), pp. 3818–3822.

T. R. Lauer, “Deconvolution with a spatially-variant PSF,” in Proc. SPIE 4847, Astronomical Data Analysis II, (2002), pp. 167–173.

R. Turcotte, C. C. Schmidt, M. J. Booth, and N. J. Emptage, “Two-photon fluorescence imaging of live neurons using a multimode optical fiber,” bioRxiv p. 2020.04.27.063388 (2020).

M. Hirsch, S. Sra, B. Schölkopf, and S. Harmeling, “Efficient filter flow for space-variant multiframe blind deconvolution,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (2010), pp. 607–614.

N. Chacko and M. Liebling, “Fast spatially variant deconvolution for optical microscopy via iterative shrinkage thresholding,” in IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2838–2842, (2014).

Supplementary Material (1)

NameDescription
» Visualization 1       Images of individual modes composing the eigen-PSF model for the data in Fig. 2(a) (mode number: 1-10, 30, and 100 modes; individual image width: 6.5 \textmu m; color-bar units: [1]).

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Figures (6)

Fig. 1.
Fig. 1. (a) Schematic of the experimental system. (b) Simplified flowchart for modeling a spatially-variant point response (black), including the additional implementation of deconvolution (yellow).
Fig. 2.
Fig. 2. spatially variant point responses can be modeled continuously over the FOV. (a) Maximal intensity projection of experimentally measured foci through a MMF used to generate the eigen-PSF model (Scale bar: 15 µm). (b) Images of individual modes composing the eigen-PSF model for the data in (a) (mode number: 1-10, 30, and 100 modes; image width: 6.5 µm; color-bar units: [1]). A high-resolution version is available as Visualization 1.
Fig. 3.
Fig. 3. Experimental foci were reconstructed accurately using the eigen-PSF model. (a,b) Comparison between (a) the experimentally measured focus at the center (0 µm; top) and edge (20 µm; bottom) of the multimode fiber core and (b) the focus reconstructed from the eigen-PSF model with a varying number of modes (number of modes: 1-10, 30, and 100 modes; image width: 6.5 µm). (c,d) Difference, as the normalized RMS error, between the experimentally measured focus and the focus reconstructed from the eigen-PSF model (c) as a function of the number of modes used in the reconstruction for different distances from the center of the fiber and (d) as a function of the distance from the center of the fiber for different number of modes used in the reconstruction.
Fig. 4.
Fig. 4. Spatial regularization of the point response is achieved through deconvolution using the svmPSF model. (a,b) Deconvolution of the image shown in Fig. 2(a) using a spatially variant version of the Richardson-Lucy algorithm after 500 iterations with (a) a single mode and (b) the first 30 modes (Scale bar: 15 µm; inset width: 3.3 µm). (c,d) Effect of the number of iterations and modes used in the deconvolution on (c) the $1/$e$^{2}$ radius of an object located at the center (3.3 µm) and edge (20 µm) of the MMF core and (d) the edge-to-center ratio of the radii in (c).
Fig. 5.
Fig. 5. Spatially uniform deconvolution of fluorescent bead images having a spatially variant focus is achieved using the eigen-PSF model. (a) Image of 1-µm fluorescent beads acquired through a MMF (NA 0.66, 35 µm). (b,c) Deconvolved version of the image shown in (a) using a spatially variant version of the Richardson-Lucy algorithm after 500 iterations with (b) a single mode and (c) the first 30 modes (Scale bar: 15 µm). (d) Insets from (a-c) (width: 4.2 µm). All images were normalized to maximum intensity of the 30-modes inset. (e) Normalized intensity profile for the line shown in (c).
Fig. 6.
Fig. 6. Deconvolution of neuronal images reveals fine subcellular details, such as spines (arrow). (a) Live neurons were imaged with a MMF (NA 0.22, 50 µm) and (b,c) deconvolved using the modified Richardson-Lucy algorithm (500 iterations and (b) 1 or (c) 50 modes). Two examples are shown (Scale bar: 15 µm). Insets were intensity normalized independently (left column inset width: 6 µm; right column inset width: 4.7 µm).

Equations (8)

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

I ( x , y ) = S ( u , v ) P ( u , v , x u , y v ) d u d v ,
P ( u , v , x , y ) = i = 1 N a i ( u , v ) p i ( x , y ) ,
I ( x , y ) = i = 1 N S ( u , v ) a i ( u , v ) p i ( x u , y v ) d u d v .
T V k = 1 / ( 1 λ T V d i v [ S k | S k | ] ) ,
I k = i = 1 N F { p i } F { a i S k } ,
R k = I / F 1 { I k } ,
E k = i = 1 N F { p i } F { a i R k } ,
S k + 1 = T V k F 1 { E k } S k ,

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