Abstract

Integrating light field microscopy techniques with existing miniscope architectures has allowed for volumetric imaging of targeted brain regions in freely moving animals. However, the current design of light field miniscopes is limited by non-uniform resolution and long imaging path length. In an effort to overcome these limitations, this paper proposes an optimized Galilean-mode light field miniscope (Gali-MiniLFM), which achieves a more consistent resolution and a significantly shorter imaging path than its conventional counterparts. In addition, this paper provides a novel framework that incorporates the anticipated aberrations of the proposed Gali-MiniLFM into the point spread function (PSF) modeling. This more accurate PSF model can then be used in 3D reconstruction algorithms to further improve the resolution of the platform. Volumetric imaging in the brain necessitates the consideration of the effects of scattering. We conduct Monte Carlo simulations to demonstrate the robustness of the proposed Gali-MiniLFM for volumetric imaging in scattering tissue.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2020 (2)

T. Shuman, D. Aharoni, D. J. Cai, C. R. Lee, S. Chavlis, L. Page-Harley, L. M. Vetere, Y. Feng, C. Y. Yang, and I. Mollinedo-Gajate, “Breakdown of spatial coding and interneuron synchronization in epileptic mice,” Nat. Neurosci. 23(2), 229–238 (2020).
[Crossref]

A. de Groot, B. J. van den Boom, R. M. van Genderen, J. Coppens, J. van Veldhuijzen, J. Bos, H. Hoedemaker, M. Negrello, I. Willuhn, C. I. De Zeeuw, and T. M. Hoogland, “Ninscope: a versatile miniscope for multi-region circuit investigations,” eLife 9, 685909 (2020).
[Crossref]

2019 (7)

J. Senarathna, H. Yu, C. Deng, A. L. Zou, J. B. Issa, D. H. Hadjiabadi, S. Gil, Q. Wang, B. M. Tyler, N. V. Thakor, and A. P. Pathak, “A miniature multi-contrast microscope for functional imaging in freely behaving animals,” Nat. Commun. 10(1), 99 (2019).
[Crossref]

D. Aharoni, B. S. Khakh, A. J. Silva, and P. Golshani, “All the light that we can see: a new era in miniaturized microscopy,” Nat. Methods 16(1), 11–13 (2019).
[Crossref]

A. Glas, M. Hübener, T. Bonhoeffer, and P. M. Goltstein, “Benchmarking miniaturized microscopy against two-photon calcium imaging using single-cell orientation tuning in mouse visual cortex,” PLoS One 14(4), e0214954 (2019).
[Crossref]

J. Mertz, “Strategies for volumetric imaging with a fluorescence microscope,” Optica 6(10), 1261 (2019).
[Crossref]

G. Barbera, B. Liang, L. Zhang, Y. Li, and D.-T. Lin, “A wireless miniScope for deep brain imaging in freely moving mice,” J. Neurosci. Methods 323, 56–60 (2019).
[Crossref]

H. Li, C. Guo, D. Kim-Holzapfel, W. Li, Y. Altshuller, B. Schroeder, W. Liu, Y. Meng, J. B. French, K.-I. Takamaru, M. A. Frohman, and S. Jia, “Fast, volumetric live-cell imaging using high-resolution light-field microscopy,” Biomed. Opt. Express 10(1), 29–49 (2019).
[Crossref]

C. Guo, W. Liu, X. Hua, H. Li, and S. Jia, “Fourier light-field microscopy,” Opt. Express 27(18), 25573 (2019).
[Crossref]

2018 (5)

S. Zhu, A. Lai, K. Eaton, P. Jin, and L. Gao, “On the fundamental comparison between unfocused and focused light field cameras,” Appl. Opt. 57(1), A1 (2018).
[Crossref]

M. A. Taylor, T. Nöbauer, A. Pernia-Andrade, F. Schlumm, and A. Vaziri, “Brain-wide 3D light-field imaging of neuronal activity with speckle-enhanced resolution,” Optica 5(4), 345–353 (2018).
[Crossref]

O. Skocek, T. Nöbauer, L. Weilguny, F. Martínez Traub, C. N. Xia, M. I. Molodtsov, A. Grama, M. Yamagata, D. Aharoni, D. D. Cox, P. Golshani, and A. Vaziri, “High-speed volumetric imaging of neuronal activity in freely moving rodents,” Nat. Methods 15(6), 429–432 (2018).
[Crossref]

B. N. Ozbay, G. L. Futia, M. Ma, V. M. Bright, J. T. Gopinath, E. G. Hughes, D. Restrepo, and E. A. Gibson, “Three dimensional two-photon brain imaging in freely moving mice using a miniature fiber coupled microscope with active axial-scanning,” Sci. Rep. 8(1), 8108 (2018).
[Crossref]

B. B. Scott, S. Y. Thiberge, C. Guo, D. G. R. Tervo, C. D. Brody, A. Y. Karpova, and D. W. Tank, “Imaging cortical dynamics in GCaMP transgenic rats with a head-mounted widefield macroscope,” Neuron 100(5), 1045–1058.e5 (2018).
[Crossref]

2017 (6)

X. Wang, Y. Liu, X. Li, Z. Zhang, H. Yang, Y. Zhang, P. R. Williams, N. S. A. Alwahab, K. Kapur, B. Yu, Y. Zhang, M. Chen, H. Ding, C. R. Gerfen, K. H. Wang, and Z. He, “Deconstruction of corticospinal circuits for goal-directed motor skills,” Cell 171(2), 440–455.e14 (2017).
[Crossref]

M. Murugan, H. J. Jang, M. Park, E. M. Miller, J. Cox, J. P. Taliaferro, N. F. Parker, V. Bhave, H. Hur, and Y. Liang et al., “Combined social and spatial coding in a descending projection from the prefrontal cortex,” Cell 171(7), 1663–1677.e16 (2017).
[Crossref]

W. A. Liberti III, L. N. Perkins, D. P. Leman, and T. J. Gardner, “An open source, wireless capable miniature microscope system,” J. Neural Eng. 14(4), 045001 (2017).
[Crossref]

W. Zong, R. Wu, M. Li, Y. Hu, Y. Li, J. Li, H. Rong, H. Wu, Y. Xu, Y. Lu, H. Jia, M. Fan, Z. Zhou, Y. Zhang, A. Wang, L. Chen, and H. Cheng, “Fast high-resolution miniature two-photon microscopy for brain imaging in freely behaving mice,” Nat. Methods 14(7), 713–719 (2017).
[Crossref]

L. Cong, Z. Wang, Y. Chai, W. Hang, C. Shang, W. Yang, L. Bai, J. Du, K. Wang, and Q. Wen, “Rapid whole brain imaging of neural activity in freely behaving larval zebrafish (danio rerio),” eLife 6, e28158 (2017).
[Crossref]

T. Nöbauer, O. Skocek, A. J. Pernia-Andrade, L. Weilguny, F. M. Traub, M. I. Molodtsov, and A. Vaziri, “Video rate volumetric C2+ imaging across cortex using seeded iterative demixing (SID) microscopy,” Nat. Methods 14(8), 811–818 (2017).
[Crossref]

2016 (2)

N. C. Pégard, H.-Y. Liu, N. Antipa, M. Gerlock, H. Adesnik, and L. Waller, “Compressive light-field microscopy for 3D neural activity recording,” Optica 3(5), 517–524 (2016).
[Crossref]

D. J. Cai, D. Aharoni, T. Shuman, J. Shobe, J. Biane, W. Song, B. Wei, M. Veshkini, M. La-Vu, J. Lou, S. E. Flores, I. Kim, Y. Sano, M. Zhou, K. Baumgaertel, A. Lavi, M. Kamata, M. Tuszynski, M. Mayford, P. Golshani, and A. J. Silva, “A shared neural ensemble links distinct contextual memories encoded close in time,” Nature 534(7605), 115–118 (2016).
[Crossref]

2014 (2)

N. Cohen, S. Yang, A. Andalman, M. Broxton, L. Grosenick, K. Deisseroth, M. Horowitz, and M. Levoy, “Enhancing the performance of the light field microscope using wavefront coding,” Opt. Express 22(20), 24817–24839 (2014).
[Crossref]

R. Prevedel, Y.-G. Yoon, M. Hoffmann, N. Pak, G. Wetzstein, S. Kato, T. Schrödel, R. Raskar, M. Zimmer, E. S. Boyden, and A. Vaziri, “Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy,” Nat. Methods 11(7), 727–730 (2014).
[Crossref]

2013 (2)

2011 (2)

K. K. Ghosh, L. D. Burns, E. D. Cocker, A. Nimmerjahn, Y. Ziv, A. El Gamal, and M. J. Schnitzer, “Miniaturized integration of a fluorescence microscope,” Nat. Methods 8(10), 871–878 (2011).
[Crossref]

J. H. Park, J. Platisa, J. V. Verhagen, S. H. Gautam, A. Osman, D. Kim, V. A. Pieribone, and E. Culurciello, “Head-mountable high speed camera for optical neural recording,” J. Neurosci. Methods 201(2), 290–295 (2011).
[Crossref]

2006 (2)

M. Levoy, R. Ng, A. Adams, M. Footer, and M. Horowitz, “Light field microscopy,” ACM Trans. Graph. 25(3), 924–934 (2006).
[Crossref]

P. Theer and W. Denk, “On the fundamental imaging-depth limit in two-photon microscopy,” J. Opt. Soc. Am. A 23(12), 3139–3149 (2006).
[Crossref]

2005 (1)

F. Helmchen and W. Denk, “Deep tissue two-photon microscopy,” Nat. Methods 2(12), 932–940 (2005).
[Crossref]

1994 (1)

1880 (1)

L. Rayleigh, “Investigations in optics, with special reference to the spectroscope,” Mon. Not. R. Astron. Soc. 9(53), 40–55 (1880).
[Crossref]

Adams, A.

M. Levoy, R. Ng, A. Adams, M. Footer, and M. Horowitz, “Light field microscopy,” ACM Trans. Graph. 25(3), 924–934 (2006).
[Crossref]

Adesnik, H.

Aharoni, D.

T. Shuman, D. Aharoni, D. J. Cai, C. R. Lee, S. Chavlis, L. Page-Harley, L. M. Vetere, Y. Feng, C. Y. Yang, and I. Mollinedo-Gajate, “Breakdown of spatial coding and interneuron synchronization in epileptic mice,” Nat. Neurosci. 23(2), 229–238 (2020).
[Crossref]

D. Aharoni, B. S. Khakh, A. J. Silva, and P. Golshani, “All the light that we can see: a new era in miniaturized microscopy,” Nat. Methods 16(1), 11–13 (2019).
[Crossref]

O. Skocek, T. Nöbauer, L. Weilguny, F. Martínez Traub, C. N. Xia, M. I. Molodtsov, A. Grama, M. Yamagata, D. Aharoni, D. D. Cox, P. Golshani, and A. Vaziri, “High-speed volumetric imaging of neuronal activity in freely moving rodents,” Nat. Methods 15(6), 429–432 (2018).
[Crossref]

D. J. Cai, D. Aharoni, T. Shuman, J. Shobe, J. Biane, W. Song, B. Wei, M. Veshkini, M. La-Vu, J. Lou, S. E. Flores, I. Kim, Y. Sano, M. Zhou, K. Baumgaertel, A. Lavi, M. Kamata, M. Tuszynski, M. Mayford, P. Golshani, and A. J. Silva, “A shared neural ensemble links distinct contextual memories encoded close in time,” Nature 534(7605), 115–118 (2016).
[Crossref]

T. Shuman, D. Aharoni, D. J. Cai, C. R. Lee, S. Chavlis, J. Taxidis, S. E. Flores, K. Cheng, M. Javaherian, C. C. Kaba, M. Shtrahman, K. I. Bakhurin, S. Masmanidis, B. S. Khakh, P. Poirazi, A. J. Silva, and P. Golshani, “Breakdown of spatial coding and neural synchronization in epilepsy,” bioRxiv p. 358580 (2018).

Altshuller, Y.

Alwahab, N. S. A.

X. Wang, Y. Liu, X. Li, Z. Zhang, H. Yang, Y. Zhang, P. R. Williams, N. S. A. Alwahab, K. Kapur, B. Yu, Y. Zhang, M. Chen, H. Ding, C. R. Gerfen, K. H. Wang, and Z. He, “Deconstruction of corticospinal circuits for goal-directed motor skills,” Cell 171(2), 440–455.e14 (2017).
[Crossref]

Andalman, A.

Andreev, A.

T. V. Truong, D. B. Holland, S. Madaan, A. Andreev, J. V. Troll, D. E. S. Koo, K. Keomanee-Dizon, M. J. McFall-Ngai, and S. E. Fraser, “Selective volume illumination microscopy offers synchronous volumetric imaging with high contrast,” bioRxiv (2018).

Antipa, N.

Bai, L.

L. Cong, Z. Wang, Y. Chai, W. Hang, C. Shang, W. Yang, L. Bai, J. Du, K. Wang, and Q. Wen, “Rapid whole brain imaging of neural activity in freely behaving larval zebrafish (danio rerio),” eLife 6, e28158 (2017).
[Crossref]

Bakhurin, K. I.

T. Shuman, D. Aharoni, D. J. Cai, C. R. Lee, S. Chavlis, J. Taxidis, S. E. Flores, K. Cheng, M. Javaherian, C. C. Kaba, M. Shtrahman, K. I. Bakhurin, S. Masmanidis, B. S. Khakh, P. Poirazi, A. J. Silva, and P. Golshani, “Breakdown of spatial coding and neural synchronization in epilepsy,” bioRxiv p. 358580 (2018).

Barbera, G.

G. Barbera, B. Liang, L. Zhang, Y. Li, and D.-T. Lin, “A wireless miniScope for deep brain imaging in freely moving mice,” J. Neurosci. Methods 323, 56–60 (2019).
[Crossref]

Baumgaertel, K.

D. J. Cai, D. Aharoni, T. Shuman, J. Shobe, J. Biane, W. Song, B. Wei, M. Veshkini, M. La-Vu, J. Lou, S. E. Flores, I. Kim, Y. Sano, M. Zhou, K. Baumgaertel, A. Lavi, M. Kamata, M. Tuszynski, M. Mayford, P. Golshani, and A. J. Silva, “A shared neural ensemble links distinct contextual memories encoded close in time,” Nature 534(7605), 115–118 (2016).
[Crossref]

Bhave, V.

M. Murugan, H. J. Jang, M. Park, E. M. Miller, J. Cox, J. P. Taliaferro, N. F. Parker, V. Bhave, H. Hur, and Y. Liang et al., “Combined social and spatial coding in a descending projection from the prefrontal cortex,” Cell 171(7), 1663–1677.e16 (2017).
[Crossref]

Biane, J.

D. J. Cai, D. Aharoni, T. Shuman, J. Shobe, J. Biane, W. Song, B. Wei, M. Veshkini, M. La-Vu, J. Lou, S. E. Flores, I. Kim, Y. Sano, M. Zhou, K. Baumgaertel, A. Lavi, M. Kamata, M. Tuszynski, M. Mayford, P. Golshani, and A. J. Silva, “A shared neural ensemble links distinct contextual memories encoded close in time,” Nature 534(7605), 115–118 (2016).
[Crossref]

Bociort, F.

Bonhoeffer, T.

A. Glas, M. Hübener, T. Bonhoeffer, and P. M. Goltstein, “Benchmarking miniaturized microscopy against two-photon calcium imaging using single-cell orientation tuning in mouse visual cortex,” PLoS One 14(4), e0214954 (2019).
[Crossref]

Bos, J.

A. de Groot, B. J. van den Boom, R. M. van Genderen, J. Coppens, J. van Veldhuijzen, J. Bos, H. Hoedemaker, M. Negrello, I. Willuhn, C. I. De Zeeuw, and T. M. Hoogland, “Ninscope: a versatile miniscope for multi-region circuit investigations,” eLife 9, 685909 (2020).
[Crossref]

Boyden, E. S.

R. Prevedel, Y.-G. Yoon, M. Hoffmann, N. Pak, G. Wetzstein, S. Kato, T. Schrödel, R. Raskar, M. Zimmer, E. S. Boyden, and A. Vaziri, “Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy,” Nat. Methods 11(7), 727–730 (2014).
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Bright, V. M.

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Figures (12)

Fig. 1.
Fig. 1. Comparison of the optical imaging path of (a) Miniscope [5], (b) LFM [19] / MiniLFM [20], (c) HR-LFM [21], and (d) our Gali-MiniLFM. NIP: native image plane; NOP: native object plane; OL: objective lens; TL: tube lens; MLA: microlens array.
Fig. 2.
Fig. 2. Overview of the design and evaluation procedure of the proposed high-resolution light field miniscope. (a) Optical design of Gali-MiniLFM; (b) We model light field PSF in 3D and incorporate aberrations of the miniscope optics. (c) We evaluate the robustness of Gali-MiniLFM by modeling the measurements under different scattering conditions and performing volumetric reconstruction via deconvolution.
Fig. 3.
Fig. 3. Lateral resolution analysis for the proposed Gali-MiniLFM. (a) By placing the MLA before the NIP, its conjugate object plane, marked as the virtual NOP, is far from the actual NOP of the miniscope. (b) The PSF images simulated from the proposed Gali-MiniLFM. (c) The deconvolved PSF images using the light field deconvolution algorithm. (d) The MTF varies across different depths. The optimal imaging depth range is determined by that having the largest bandwidth in the MTFs. The optimized MTF indicates that our design can provide better than 5$\mu$m resolution across an approximately 100$\mu$m depth range.
Fig. 4.
Fig. 4. Axial resolution analysis for the proposed Gali-MiniLFM where the dashed green rectangle marks the optimal imaging depth range in Fig. 3(d).
Fig. 5.
Fig. 5. Lateral resolution study for the proposed Gali-MiniLFM. (a) Comparison for the resolved size obtained from a single bead and two closely spaced beads. (b) Examples of deconvolved two beads. (c) Corresponding line profiles along colored dashed lines in (b).
Fig. 6.
Fig. 6. The proposed 3D PSF modeling framework incorporating both aberration and diffraction effects in Gali-MiniLFM. (a) Aberration extraction in Zemax. (a1) The position of the FP is optimized in Zemax for different point source. (a2) The Seidel diagrams for the aberrations included in the wavefront at the FP from a point source placed off-axis by 0.15mm in the $x$-direction. (a3) The Seidel coefficients for off-axis point sources in $x$ direction at two extreme depths, i.e. -140$\mu$m and 60$\mu$m. (b) Diffraction modeling in Matlab. Each aberrated wave field is propagated through the MLA to calculate the light field PSF. (c) The computational framework assume periodicity across the MLA (c1-c2) and shift-variant PSF within each microlens (c3) in order to model the off-axis light field PSFs.
Fig. 7.
Fig. 7. Validation of our light field PSF simulation method. (a) The ray tracing layout used in Zemax. (b) The comparison between PSFs simulated using (b1) direct ray tracing, (b2) the wave model without considering aberration (adapted from the code shared in [24]), and (b3) our method. The PSFs simulated from our method closely matches with the ray tracing results with diffraction effects included.
Fig. 8.
Fig. 8. Light field measurement for imaging volumetric objects without scattering and 3D reconstruction results. (a) Ground truth objects used in the simulation. The colorbar indicates the axial location of each sphere. (b) The simulated light field measurement from Zemax. The 3D reconstruction results using (c) the wave-optic model without considering aberration, and (d) our methods. Comparison of the projection images show that the results without considering aberration suffer from worse lateral resolution, incorrect depth information, and axial elongation.
Fig. 9.
Fig. 9. Results of volumetric imaging and 3D reconstruction under tissue scattering. (a) The simulation considers anisotropic volumetric scattering by embedding both the imaging targets and the background fluorescent beads inside the tissue volume. (b) Volumetric reconstruction results under increasing imaging depth. The depths of the top surface of the tissue are (b1) 180$\mu$m, (b2) 90$\mu$m, and (b3) 0$\mu$m.
Fig. 10.
Fig. 10. SBR for quantifying the scattering effect. SBRs of (a) raw light field measurements, (b) 3D reconstruction. Notice that smaller $D$ will cause more severe scattering as well as lower SBR.
Fig. 11.
Fig. 11. Performance limit analysis for the proposed Gali-MiniLFM under scattering. (a) Distributions of the fluorescent particles used in the simulation, (b) The raw light field measurements, and (c) 3D reconstruction results. The left-most column corresponds to 55 particles and $D = 0\mu$m. The second and the third columns correspond to 65 particles and $D=0\mu$m and $D=90\mu$m, respectively. The fourth and the right-most columns correspond to 75 particles and $D=90\mu$m and $D=180\mu$m, respectively.
Fig. 12.
Fig. 12. Influence analysis of background fluorescence on the performance limit of the proposed Gali-MiniLFM under scattering. (a) Distributions of the fluorescent particles used in the simulation, (b) The raw light field measurements, and (c) 3D reconstruction results. The left-most column corresponds to 50 particles and $D = 0\mu$m. The second and the third columns correspond to increasing the density of background fluorescence and imaging targets up to 1.3 times (65 particles) when $D = 0\mu$m, respectively. The fourth and the right-most columns correspond to 65 particles with reduced scattering, i.e. $D=90\mu$m but with increased density of background fluorescence up to two and three times, respectively.

Tables (1)

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Table 1. The optimized parameters of the proposed Gali-MiniLFM.

Equations (9)

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2|a|NA0M=Ndmla,
1/a+1/b>1/fmla,
12fmla<b<fmla,
Mmla2bdmlaNA0,
Ui^(r2)=ejk(cdi)jλ(cdi)Ui(r1)exp{jk2(cdi)|r1r2|2}dr12,
hi(r3)=|ejkbjλbUi^(r2)P(r2)exp{jk2b|r3r2|2}dr22|2,
P(r2)=s{S1,S2}p(r2sdmla)exp{jk2fmla|r2sdmla|2},
dq=qdmlaNn,
SBR=I0/IsI0,

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