Abstract

Optical projection tomography (OPT) is a 3D imaging alternative to conventional microscopy which allows imaging of millimeter-sized object with isotropic micrometer resolution. The zebrafish is an established model organism and an important tool used in genetic and chemical screening. The size and optical transparency of the embryo and larva makes them well suited for imaging using OPT. Here, we present an open-source implementation of an OPT platform, built around a customized sample stage, 3D-printed parts and open source algorithms optimized for the system. We developed a versatile automated workflow including a two-step image processing approach for correcting the center of rotation and generating accurate 3D reconstructions. Our results demonstrate high-quality 3D reconstruction using synthetic data as well as real data of live and fixed zebrafish. The presented 3D-printable OPT platform represents a fully open design, low-cost and rapid loading and unloading of samples. Our system offers the opportunity for researchers with different backgrounds to setup and run OPT for large scale experiments, particularly in studies using zebrafish larvae as their key model organism.

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

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    [Crossref]
  26. J. Sharpe, “Optical Projection Tomography as a Tool for 3D Microscopy and Gene Expression Studies,” Science 296(5567), 541–545 (2002).
    [Crossref]
  27. A. Allalou, Y. Wu, M. Ghannad-Rezaie, P. M. Eimon, and M. F. Yanik, “Automated deep-phenotyping of the vertebrate brain,” eLife 6, 1–26 (2017).
    [Crossref]
  28. A. M. Petzold, V. M. Bedell, N. J. Boczek, J. J. Essner, D. Balciunas, K. J. Clark, and S. C. Ekker, “SCORE Imaging: Specimen in a Corrected Optical Rotational Enclosure,” Zebrafish 7(2), 149–154 (2010).
    [Crossref]
  29. W. van Aarle, W. J. Palenstijn, J. De Beenhouwer, T. Altantzis, S. Bals, K. J. Batenburg, and J. Sijbers, “The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography,” Ultramicroscopy 157, 35–47 (2015).
    [Crossref]
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    [Crossref]

2020 (1)

A. del Pozo, R. Manuel, A. B. Iglesias Gonzalez, H. K. Koning, J. Habicher, H. Zhang, A. Allalou, K. Kullander, and H. Boije, “Behavioral Characterization of dmrt3a Mutant Zebrafish Reveals Crucial Aspects of Vertebrate Locomotion through Phenotypes Related to Acceleration,” eNeuro 7(3), ENEURO.0047-20.2020 (2020).
[Crossref]

2019 (1)

P. P. Vallejo Ramirez, J. Zammit, O. Vanderpoorten, F. Riche, F.-X. Blé, X.-H. Zhou, B. Spiridon, C. Valentine, S. E. Spasov, P. W. Oluwasanya, G. Goodfellow, M. J. Fantham, O. Siddiqui, F. Alimagham, M. Robbins, A. Stretton, D. Simatos, O. Hadeler, E. J. Rees, F. Ströhl, R. F. Laine, and C. F. Kaminski, “OptiJ: Open-source optical projection tomography of large organ samples,” Sci. Rep. 9(1), 15693 (2019).
[Crossref]

2018 (2)

H. Yu, S. Xia, C. Wei, Y. Mao, D. Larsson, X. Xiao, P. Pianetta, Y.-S. Yu, and Y. Liu, “Automatic projection image registration for nanoscale X-ray tomographic reconstruction,” J. Synchrotron Radiat. 25(6), 1819–1826 (2018).
[Crossref]

C.-C. Cheng, Y.-T. Ching, P.-H. Ko, and Y. Hwu, “Correction of center of rotation and projection angle in synchrotron X-ray computed tomography,” Sci. Rep. 8(1), 9884 (2018).
[Crossref]

2017 (4)

A. Allalou, Y. Wu, M. Ghannad-Rezaie, P. M. Eimon, and M. F. Yanik, “Automated deep-phenotyping of the vertebrate brain,” eLife 6, 1–26 (2017).
[Crossref]

X. Tang, D. M. van der Zwaan, A. Zammit, K. F. D. Rietveld, and F. J. Verbeek, “Fast Post-Processing Pipeline for Optical Projection Tomography,” IEEE Trans. Nanobioscience 16(5), 367–374 (2017).
[Crossref]

T. Watson, N. Andrews, S. Davis, L. Bugeon, M. D. Dallman, and J. McGinty, “OPTiM: Optical projection tomography integrated microscope using open-source hardware and software,” PLoS One 12(7), e0180309 (2017).
[Crossref]

D. Nguyen, P. J. Marchand, A. L. Planchette, J. Nilsson, M. Sison, J. Extermann, A. Lopez, M. Sylwestrzak, J. Sordet-Dessimoz, A. Schmidt-Christensen, D. Holmberg, D. Van De Ville, and T. Lasser, “Optical projection tomography for rapid whole mouse brain imaging,” Biomed. Opt. Express 8(12), 5637 (2017).
[Crossref]

2016 (3)

S. Avagyan and L. I. Zon, “Fish to Learn: Insights into Blood Development and Blood Disorders from Zebrafish Hematopoiesis,” Hum. Gene Ther. 27(4), 287–294 (2016).
[Crossref]

G. K. Varshney, B. Carrington, W. Pei, K. Bishop, Z. Chen, C. Fan, L. Xu, M. Jones, M. C. LaFave, J. Ledin, R. Sood, and S. M. Burgess, “A high-throughput functional genomics workflow based on CRISPR/Cas9-mediated targeted mutagenesis in zebrafish,” Nat. Protoc. 11(12), 2357–2375 (2016).
[Crossref]

J. van der Horst and J. Kalkman, “Image resolution and deconvolution in optical tomography,” Opt. Express 24(21), 24460 (2016).
[Crossref]

2015 (2)

T. Correia, N. Lockwood, S. Kumar, J. Yin, M.-C. Ramel, N. Andrews, M. Katan, L. Bugeon, M. J. Dallman, J. McGinty, P. Frankel, P. M. W. French, and S. Arridge, “Accelerated Optical Projection Tomography Applied to In Vivo Imaging of Zebrafish,” PLoS One 10(8), e0136213 (2015).
[Crossref]

W. van Aarle, W. J. Palenstijn, J. De Beenhouwer, T. Altantzis, S. Bals, K. J. Batenburg, and J. Sijbers, “The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography,” Ultramicroscopy 157, 35–47 (2015).
[Crossref]

2014 (2)

2013 (4)

S. L. Simpson, R. G. Lyday, S. Hayasaka, A. P. Marsh, and P. J. Laurienti, “A permutation testing framework to compare groups of brain networks,” Front. Comput. Neurosci. 7171(2013).
[Crossref]

C. Pardo-Martin, A. Allalou, J. Medina, P. M. Eimon, C. Wählby, and M. Fatih Yanik, “High-throughput hyperdimensional vertebrate phenotyping,” Nat. Commun. 4(1), 1467 (2013).
[Crossref]

M. D. Wong, J. Dazai, J. R. Walls, N. W. Gale, and R. M. Henkelman, “Design and Implementation of a Custom Built Optical Projection Tomography System,” PLoS One 8(9), e73491 (2013).
[Crossref]

Di Dong, Shouping Zhu, V. Kumar, J. V. Stein, S. Oehler, C. Savakis, Jie Tian, and J. Ripoll, “Automated Recovery of the Center of Rotation in Optical Projection Tomography in the Presence of Scattering,” IEEE J. Biomed. Heal. Informatics 17(1), 198–204 (2013).
[Crossref]

2012 (4)

D. D. Shouping Zhu, U. J. Birk, M. Rieckher, N. Tavernarakis, X. Qu, J. Liang, J. Tian, and J. Ripoll, “Automated Motion Correction for In Vivo Optical Projection Tomography,” IEEE Trans. Med. Imaging 31(7), 1358–1371 (2012).
[Crossref]

O. Ronneberger, K. Liu, M. Rath, D. Rueß, T. Mueller, H. Skibbe, B. Drayer, T. Schmidt, A. Filippi, R. Nitschke, T. Brox, H. Burkhardt, and W. Driever, “ViBE-Z: a framework for 3D virtual colocalization analysis in zebrafish larval brains,” Nat. Methods 9(7), 735–742 (2012).
[Crossref]

R. Tomer, K. Khairy, F. Amat, and P. J. Keller, “Quantitative high-speed imaging of entire developing embryos with simultaneous multiview light-sheet microscopy,” Nat. Methods 9(7), 755–763 (2012).
[Crossref]

Y. Min, G. Haidong, L. Xingdong, M. Fanyong, and W. Dongbo, “A new method to determine the center of rotation shift in 2D-CT scanning system using image cross correlation,” NDT&E Int. 46(1), 48–54 (2012).
[Crossref]

2011 (1)

2010 (2)

U. J. Birk, M. Rieckher, N. Konstantinides, A. Darrell, A. Sarasa-Renedo, H. Meyer, N. Tavernarakis, and J. Ripoll, “Correction for specimen movement and rotation errors for in-vivo Optical Projection Tomography,” Biomed. Opt. Express 1(1), 87 (2010).
[Crossref]

A. M. Petzold, V. M. Bedell, N. J. Boczek, J. J. Essner, D. Balciunas, K. J. Clark, and S. C. Ekker, “SCORE Imaging: Specimen in a Corrected Optical Rotational Enclosure,” Zebrafish 7(2), 149–154 (2010).
[Crossref]

2009 (1)

W. S. Noble, “How does multiple testing correction work?” Nat. Biotechnol. 27(12), 1135–1137 (2009).
[Crossref]

2008 (1)

C. Thisse and B. Thisse, “High-resolution in situ hybridization to whole-mount zebrafish embryos,” Nat. Protoc. 3(1), 59–69 (2008).
[Crossref]

2007 (1)

M. B. Walker and C. B. Kimmel, “A two-color acid-free cartilage and bone stain for zebrafish larvae,” Biotech. Histochem. 82(1), 23–28 (2007).
[Crossref]

2005 (1)

J. R. Walls, J. G. Sled, J. Sharpe, and R. M. Henkelman, “Correction of artefacts in optical projection tomography,” Phys. Med. Biol. 50(19), 4645–4665 (2005).
[Crossref]

2003 (1)

C. A. MacRae and R. T. Peterson, “Zebrafish-Based Small Molecule Discovery,” Chem. Biol. 10(10), 901–908 (2003).
[Crossref]

2002 (1)

J. Sharpe, “Optical Projection Tomography as a Tool for 3D Microscopy and Gene Expression Studies,” Science 296(5567), 541–545 (2002).
[Crossref]

Alimagham, F.

P. P. Vallejo Ramirez, J. Zammit, O. Vanderpoorten, F. Riche, F.-X. Blé, X.-H. Zhou, B. Spiridon, C. Valentine, S. E. Spasov, P. W. Oluwasanya, G. Goodfellow, M. J. Fantham, O. Siddiqui, F. Alimagham, M. Robbins, A. Stretton, D. Simatos, O. Hadeler, E. J. Rees, F. Ströhl, R. F. Laine, and C. F. Kaminski, “OptiJ: Open-source optical projection tomography of large organ samples,” Sci. Rep. 9(1), 15693 (2019).
[Crossref]

Allalou, A.

A. del Pozo, R. Manuel, A. B. Iglesias Gonzalez, H. K. Koning, J. Habicher, H. Zhang, A. Allalou, K. Kullander, and H. Boije, “Behavioral Characterization of dmrt3a Mutant Zebrafish Reveals Crucial Aspects of Vertebrate Locomotion through Phenotypes Related to Acceleration,” eNeuro 7(3), ENEURO.0047-20.2020 (2020).
[Crossref]

A. Allalou, Y. Wu, M. Ghannad-Rezaie, P. M. Eimon, and M. F. Yanik, “Automated deep-phenotyping of the vertebrate brain,” eLife 6, 1–26 (2017).
[Crossref]

C. Pardo-Martin, A. Allalou, J. Medina, P. M. Eimon, C. Wählby, and M. Fatih Yanik, “High-throughput hyperdimensional vertebrate phenotyping,” Nat. Commun. 4(1), 1467 (2013).
[Crossref]

Altantzis, T.

W. van Aarle, W. J. Palenstijn, J. De Beenhouwer, T. Altantzis, S. Bals, K. J. Batenburg, and J. Sijbers, “The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography,” Ultramicroscopy 157, 35–47 (2015).
[Crossref]

Amat, F.

R. Tomer, K. Khairy, F. Amat, and P. J. Keller, “Quantitative high-speed imaging of entire developing embryos with simultaneous multiview light-sheet microscopy,” Nat. Methods 9(7), 755–763 (2012).
[Crossref]

Andrews, N.

T. Watson, N. Andrews, S. Davis, L. Bugeon, M. D. Dallman, and J. McGinty, “OPTiM: Optical projection tomography integrated microscope using open-source hardware and software,” PLoS One 12(7), e0180309 (2017).
[Crossref]

T. Correia, N. Lockwood, S. Kumar, J. Yin, M.-C. Ramel, N. Andrews, M. Katan, L. Bugeon, M. J. Dallman, J. McGinty, P. Frankel, P. M. W. French, and S. Arridge, “Accelerated Optical Projection Tomography Applied to In Vivo Imaging of Zebrafish,” PLoS One 10(8), e0136213 (2015).
[Crossref]

Arridge, S.

T. Correia, N. Lockwood, S. Kumar, J. Yin, M.-C. Ramel, N. Andrews, M. Katan, L. Bugeon, M. J. Dallman, J. McGinty, P. Frankel, P. M. W. French, and S. Arridge, “Accelerated Optical Projection Tomography Applied to In Vivo Imaging of Zebrafish,” PLoS One 10(8), e0136213 (2015).
[Crossref]

Atwood, R. C.

Avagyan, S.

S. Avagyan and L. I. Zon, “Fish to Learn: Insights into Blood Development and Blood Disorders from Zebrafish Hematopoiesis,” Hum. Gene Ther. 27(4), 287–294 (2016).
[Crossref]

Balciunas, D.

A. M. Petzold, V. M. Bedell, N. J. Boczek, J. J. Essner, D. Balciunas, K. J. Clark, and S. C. Ekker, “SCORE Imaging: Specimen in a Corrected Optical Rotational Enclosure,” Zebrafish 7(2), 149–154 (2010).
[Crossref]

Bals, S.

W. van Aarle, W. J. Palenstijn, J. De Beenhouwer, T. Altantzis, S. Bals, K. J. Batenburg, and J. Sijbers, “The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography,” Ultramicroscopy 157, 35–47 (2015).
[Crossref]

Batenburg, K. J.

W. van Aarle, W. J. Palenstijn, J. De Beenhouwer, T. Altantzis, S. Bals, K. J. Batenburg, and J. Sijbers, “The ASTRA Toolbox: A platform for advanced algorithm development in electron tomography,” Ultramicroscopy 157, 35–47 (2015).
[Crossref]

Bedell, V. M.

A. M. Petzold, V. M. Bedell, N. J. Boczek, J. J. Essner, D. Balciunas, K. J. Clark, and S. C. Ekker, “SCORE Imaging: Specimen in a Corrected Optical Rotational Enclosure,” Zebrafish 7(2), 149–154 (2010).
[Crossref]

Birk, U. J.

D. D. Shouping Zhu, U. J. Birk, M. Rieckher, N. Tavernarakis, X. Qu, J. Liang, J. Tian, and J. Ripoll, “Automated Motion Correction for In Vivo Optical Projection Tomography,” IEEE Trans. Med. Imaging 31(7), 1358–1371 (2012).
[Crossref]

U. J. Birk, M. Rieckher, N. Konstantinides, A. Darrell, A. Sarasa-Renedo, H. Meyer, N. Tavernarakis, and J. Ripoll, “Correction for specimen movement and rotation errors for in-vivo Optical Projection Tomography,” Biomed. Opt. Express 1(1), 87 (2010).
[Crossref]

Bishop, K.

G. K. Varshney, B. Carrington, W. Pei, K. Bishop, Z. Chen, C. Fan, L. Xu, M. Jones, M. C. LaFave, J. Ledin, R. Sood, and S. M. Burgess, “A high-throughput functional genomics workflow based on CRISPR/Cas9-mediated targeted mutagenesis in zebrafish,” Nat. Protoc. 11(12), 2357–2375 (2016).
[Crossref]

Blé, F.-X.

P. P. Vallejo Ramirez, J. Zammit, O. Vanderpoorten, F. Riche, F.-X. Blé, X.-H. Zhou, B. Spiridon, C. Valentine, S. E. Spasov, P. W. Oluwasanya, G. Goodfellow, M. J. Fantham, O. Siddiqui, F. Alimagham, M. Robbins, A. Stretton, D. Simatos, O. Hadeler, E. J. Rees, F. Ströhl, R. F. Laine, and C. F. Kaminski, “OptiJ: Open-source optical projection tomography of large organ samples,” Sci. Rep. 9(1), 15693 (2019).
[Crossref]

Boczek, N. J.

A. M. Petzold, V. M. Bedell, N. J. Boczek, J. J. Essner, D. Balciunas, K. J. Clark, and S. C. Ekker, “SCORE Imaging: Specimen in a Corrected Optical Rotational Enclosure,” Zebrafish 7(2), 149–154 (2010).
[Crossref]

Boije, H.

A. del Pozo, R. Manuel, A. B. Iglesias Gonzalez, H. K. Koning, J. Habicher, H. Zhang, A. Allalou, K. Kullander, and H. Boije, “Behavioral Characterization of dmrt3a Mutant Zebrafish Reveals Crucial Aspects of Vertebrate Locomotion through Phenotypes Related to Acceleration,” eNeuro 7(3), ENEURO.0047-20.2020 (2020).
[Crossref]

Brandt, R.

T. Rohlfing, R. Brandt, C. R. Maurer, and R. Menzel, “Bee brains, B-splines and computational democracy: generating an average shape atlas,” in Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001) (IEEE Comput. Soc2001), pp. 187–194.

Brox, T.

O. Ronneberger, K. Liu, M. Rath, D. Rueß, T. Mueller, H. Skibbe, B. Drayer, T. Schmidt, A. Filippi, R. Nitschke, T. Brox, H. Burkhardt, and W. Driever, “ViBE-Z: a framework for 3D virtual colocalization analysis in zebrafish larval brains,” Nat. Methods 9(7), 735–742 (2012).
[Crossref]

Bugeon, L.

T. Watson, N. Andrews, S. Davis, L. Bugeon, M. D. Dallman, and J. McGinty, “OPTiM: Optical projection tomography integrated microscope using open-source hardware and software,” PLoS One 12(7), e0180309 (2017).
[Crossref]

T. Correia, N. Lockwood, S. Kumar, J. Yin, M.-C. Ramel, N. Andrews, M. Katan, L. Bugeon, M. J. Dallman, J. McGinty, P. Frankel, P. M. W. French, and S. Arridge, “Accelerated Optical Projection Tomography Applied to In Vivo Imaging of Zebrafish,” PLoS One 10(8), e0136213 (2015).
[Crossref]

Burgess, S. M.

G. K. Varshney, B. Carrington, W. Pei, K. Bishop, Z. Chen, C. Fan, L. Xu, M. Jones, M. C. LaFave, J. Ledin, R. Sood, and S. M. Burgess, “A high-throughput functional genomics workflow based on CRISPR/Cas9-mediated targeted mutagenesis in zebrafish,” Nat. Protoc. 11(12), 2357–2375 (2016).
[Crossref]

Burkhardt, H.

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Supplementary Material (1)

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» Supplement 1       Detailed information of experimental setup

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Figures (8)

Fig. 1.
Fig. 1. A) Schematics of brightfield OPT setup. The dotted green lines represent the light path, and solid lines represent signals transmitted by wires. Bold arrows represent sample loading and unloading respectively from the same port of the capillary. The capillary is rotated with a stepper motor and the CMOS camera acquires projections of the sample during rotation. B) An RGB projection from the OPT system using 3 dpf in situ stained zebrafish. The dashed lines represent the inner walls of the glass capillary. C) 3D reconstruction results. Left: 3D volume of three channels rendering using Volview software.(Kitware Inc.) with pseudo color. Right: 3D volume of green channel. A slice of the sample in z direction marked in red (solid line) is demonstrated. Maximum intensity projection views of the region marked in red (dashed lines) in the volume rendering. Maximum projections are made from ventral, frontal and sagittal views in the x-y-z coordinates.
Fig. 2.
Fig. 2. A) Schematics of the OPT system for simulation. The solid line with single arrow represents the rotation of the capillary; the dotted lines with arrow represent the light path; the bold arrow represents the direction of simulated motion errors. B) Processes for generating simulation data using a phantom image in MATLAB. C) The effect of COR errors on the final reconstruction.
Fig. 3.
Fig. 3. Workflow of OPT reconstruction is presented with examples using Alcian blue stained fish. The pre-processing steps are for brightfield OPT data. Solid arrows represent the data flow. The dashed line in the images of step 5 represents the image center line and the solid line represents the detected symmetry axis. B) Detailed workflow for COR correction using gCOR and iRRpw method.
Fig. 4.
Fig. 4. A) Reconstructed image (left) and overlay with ground truth for each method (right). In the comparison image the reconstructed image is shown in green and the ground truth is shown in magenta. B) SAD of image intensities between reconstructed data and ground truth for each step in the algorithm. Mean and standard deviation of SADs are calculated based on 5 trails and the image intensity values are ranging from 0 to 1. Two sizes of image were tested using 256×256 and 512×512. The 0 in the horizontal axis represents the FBP reconstruction results without COR corrections. The gCOR is applied only once in the process and iRR methods are applied with 10 iterations with their SAD results labelled at iteration 1, 3 and 10.
Fig. 5.
Fig. 5. A) Comparison of three different combination of methods for COR correction and reconstruction in terms of SAD. We used 256×256 template size with the same noise level for all experiments. The processing step at 0 represents the reconstruction result without any correction. Dashed lines represent the single step process of gCOR and solid lines represent the process of iRR based methods with 10 iterations. B) Total variance of the reconstructed image in each iteration of iRRpw process.
Fig. 6.
Fig. 6. A) Three examples of brightfield OPT images of zebrafish larvae and B) their corresponding ventral, frontal and sagittal view of reconstructed data using green channel and with volume rendering by Volview.
Fig. 7.
Fig. 7. A) Single slice is selected for reconstruction with their capillary removed and we compare reconstruction results in each step and with two approaches for correcting type II error. The bold arrows represent the workflow and the thin arrows point to edges that used to compare reconstruction differences. B) Intermediate reconstruction results from the iRRpw method at iteration 1 through 3.
Fig. 8.
Fig. 8. A) OPT of wild-type (left) and wildtype (right) zebrafish stained with Alcian blue at 5 dpf. B) Maximum projection with color-coded regions showing significant difference between wild-type (n=10) and mutant (n=12) groups. Cyan shows voxels with statistical higher intensity in wild-type group and magenta shows voxels with statistical higher intensity in mutant group. The thin arrows point to the circular areas contain expected differences.

Equations (3)

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P ( x , y ) = i = 1 N f ( I ( x , y , i ) ) ,
arg min a R , b R , r R [ 1 x y ( M ( x , y ) M ¯ ) ( M ( x , y ) M ¯ ) x y ( M ( x , y ) M ¯ ) 2 x y ( M ( x , y ) M ¯ ) 2 ] ,
M = R r ( T a b ( P ( x , y ) ) ) , M = F l i p Y ( M ) ,