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Restoration of Light Sheet Multi-View Data


a fused result of the raw multi-view data. Maximum intensity projections (MIPs) of each image were generated to obtain 3D volume information. MIPs of the “0 degrees” and “180 degrees” views show distinct high and low signal in opposite regions in the Drosophila brain ( Figure 4B ). T e low-signal regions, marked by arrowheads, clearly suff er from signal degradation due to light scattering and absorption. By contrast, the fused result of the raw multi-view data shows signal in all regions of the Drosophila brain supporting the advantage of acquiring and fusing multiple LSFM views ( Figure 4 B and 5 ).


If deconvolution and fusion of the raw multi-view data are combined, we notice an improvement of contrast in MIPs of the Drosophila brain. Moreover, additional object detail can be observed in both channels ( Figure 5 ) and in zoomed regions of MIPs of Bruchpilot and single slices of expressed NRE-GFP ( Figure 6 ).


Breast carcinoma . T e second example image was acquired with a dithered, multi Bessel beam Lattice Light Sheet microscope from 3i (Intelligent Imaging Innovations Inc. Denver, CO). T e two-channel image shows a single cell of a SUM159 breast carcinoma cell line transiently transfected with an endoplasmic reticulum (ER) marker attached to OX-GFP (Addgene, Cambridge, MA). T is probe should display perinuclear sheets of the ER as well as the reticular ER pattern generally observed at the periphery of the cell ( Figure 7 A–B, and 7 A’–B’). T e other probe represents a protein that is cytosolic under steady-state conditions but localizes to rings around lipid droplets upon fatty acid loading ( Figure 7D–E ). T e acquired image was skewed during acquisition, and thus it needed to be de-skewed (de-sheared) fi rst before deconvolution could be applied. T e de-skewing was performed with the Huygens Object Stabilizer. Deconvolution of both channels was performed with the Good’s Roughness Maximum Likelihood Estimation (GMLE) algorithm [ 7 ], which was developed for low-signal data but also has been proven to work effi ciently on widefi eld and LSFM data. Fiſt y iterations were applied, and the SNR parameter was set to 30. MIPs of the ER marker before and aſt er deconvolution show that deconvolution reveals a more well-defi ned reticular ER pattern. T is observation is supported by intensity profi le measurements performed on both single Z slices and MIPs where the signal aſt er deconvolution is at least two times higher than in the non-deconvolved image. Moreover, the full-width at half maximum of the peaks suggests an improvement in object detail about 2 times better (Figure 7C). Figure 7D is the same cell as in Figure 7A but corresponds to the second probe used, which is the cytosolic marker that should mostly localize to rings around lipid droplets. Although some areas within the raw image already hint toward these ring structures, deconvolution gives a much clearer picture and allows the identifi cation of even more ring structures ( Figure 7D’and E ). Again, line intensity profi les demonstrate an improvement in signal and resolution of more than two times ( Figure 7F ).


Discussion


Like all fluorescent-based imaging techniques, light sheet microscopy introduces distortions in the image that conceal the true details of the imaged object. The


2018 September • www.microscopy-today.com


main distortion factors are blurring, noise, scattering, and shading. Blurring and noise can be effectively corrected with Huygens iterative deconvolution using advanced maximum likelihood algorithms [ 6 ], and scattering and shading artifacts are restored by fusing and aligning multiple light- sheet images acquired from different directions. In this article we presented two different LSFM images that clearly benefited from deconvolution and fusion using the Huygens Light Sheet Fusion and Deconvolution Wizard. This Wizard offers, together with the de-skewing option in the Huygens Object Stabilizer, a unique, fast, and easy-to-use workflow for the restoration of images from a variety of LSFM systems. The processing of LSFM images by Huygens software is efficiently executed on the central processor and graphical processors. Processing poses no limit to file size and dimensions but may demand specific computer hardware. The results show improvements in signal and resolution of about 1.5 times and higher.


Conclusion


This article discusses image restoration from light sheet microscopes. Detailed descriptions are given of the fusion of multi-views of the specimen and of image deconvo- lution. Examples show improvements in image signal and resolution.


Acknowledgements T e authors from Scientifi c Volume Imaging b.v. (SVI) would like to thank everyone who helped us in developing the LSFM restoration tools by sharing their valuable LSFM image data. We also would like to thank Daniel Sevilla Sanchez (SVI) for critically reading the manuscript and Dr. Laurent Seugnet (WAKING Team, Centre de Recherche en Neurosciences de Lyon (INSERM U1028, CNRS UMR 5292)) for providing the fl uorescent Drosophila brain sample.


References [1] J Huisken and DY Stainier , Development 136 ( 12 ) ( 2009 ) 1963 – 75 .


[2] RM Power and J Huisken , Nat Methods 14 ( 4 ) ( 2017 ) 360 – 73 .


[3] F Strobl et al ., Nat Protoc 12 ( 6 ) ( 2017 ) 1103 – 09 . [4] J Huisken et al ., Science 305 ( 5685 ) 2004 ) 1007 – 09 . [5] PJ Verveer et al ., Nat Methods 4 ( 4 ) ( 2007 ) 311 – 13 . [6] HTM van der Voort , Super-Resolution Imaging in Biomedicine , eds. A Diaspro and MA van Zandvoort, CRC Press , Boca Raton, FL , 2016 , 99 – 119 .


[7] For more information, see Scientifi c Volume Imaging, “Deconvolutoin algorithms: optimize the Huygens deconvolution results,” https://svi.nl/Deconvolution- algorithms (accessed July 15, 2018).


[8] For more information, see Scientifi c Volume Imaging, “Distorted PSF due to refractive index mismatch,” https:// svi.nl/MismatchDistortsPsf (accessed July 15, 2018).


[9] BC Chen et. al ., Science 346 ( 6208 ) ( 2014 ) 1257998/1 – 12 . [10] A North , J Cell Biol 172 ( 1 ) ( 2006 ) 9 – 18 . [11] See also Scientifi c Volume Imaging, “Data acquisition pitfalls,” https://svi.nl/AcquisitionPitfalls (accessed July 15, 2018).


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