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Huygens Light Sheet Deconvolution Software

Supports many types of light sheets



Raw Light Sheet
Huygens Deconvolved

Huygens Light Sheet Fluorescence Microscopy (LSFM) Deconvolution option supports a range of light sheet types, such as a Gaussian-profile (static, or flat when moved along the sheet direction), scanning beam, and scanning Bessel beam and lattice. A spatially variant PSF corrects for PSF differences due to changes in light sheet thickness and depth-dependent spherical aberration. If needed, deskewing of image data can be performed with a specialized feature in Huygens Object Stabilizer. Deconvolution can be easily combined with multi-view fusion, with Huygens Fuser.

Large LSFM data sets benefit greatly from Huygens' efficient RAM use, robust deconvolution algorithms, and GPU acceleration.

Image description
Image of Drosophila brain taken with Zeiss Z1 Light Sheet microscope. This multiview dataset consisting of eight views (one of the raw views is shown) was deconvolved and fused with the Huygens Software. Image kindly provided by Dr. Denis Ressnikoff, University Claude Bernard, Lyon, France.


All microscope brands

All light sheet microscopes are supported.


Correct for variable sheet width

A spatially variant PSF is constructed based on the chosen light sheet type.


Most advanced algorithms





Testimonial

diSPIM Light Sheet images of DiI-labelled human brain tissue cleared with our newly published hFRUIT method, were considerably improved with Huygens de-skewing and deconvolution. Huygens 3D rendering allowed us to create awesome figures and videos, which really helped to let our data shine!

Sven Hildebrand, PhD, Maastricht Brain Imaging Centre (MBIC), Maastricht University, The Netherlands.



Spatially variant PSF to correct for variable sheet thickness


The quality of a raw Light Sheet image typically varies across the image. This is due to the spatially variant thickness of the excitation sheet, as the thickness of the sheet at a point in the sample determines how strongly this point is blurred in the image. If, for example, a static Gaussian sheet is used, the resolution at the center of the image will be higher than that at the edges. To correct for this, Huygens Light Sheet Deconvolution uses a variable Point Spread Function (PSF) (see image). This PSF is caculated using the specifications of your light sheet, entered in the Microscopy Parameters, so that the deconvolution is be optimized to your set-up. For a Gaussian light sheets which focus is moved in the sheet direction (a.k.a. axial sweep), a flat sheet setting is available.

Image description
Based on the excitation mode selected by the user, Huygens uses a variable PSF to deconvolve Light Sheet images.


LSFM PSF NoText


Large data sets? No problem!

LSFM data sets are often very large, especially when multiple views are recorded. To allow for high-speed deconvolution of such data sets without compromising the deconvolution quality, we offer a GPU acceleration option. This GPU acceleration, together with Huygens' efficient RAM usage and smart brick splitting, ensures that there is no size limit for input data.


Use in research

J. Bürgers, I. Pavlova, J.E. Rodriguez-Gatica et al., Light-sheet fluorescence expansion microscopy: fast mapping of neural circuits at super resolution.
Huygens was used to measure PSFs and to deconvolve Light Sheet images.
Neurophoton. 6 (2019)

K. Hötte, M. Koch, L. Hof et al., Ultra-thin fluorocarbon foils optimise multiscale imaging of three-dimensional native and optically cleared specimens.
Huygens was used for Light Sheet fusion and deconvolution.
Sci. Rep. 9 (2019)

For more, see Scientific Publications

Images acquired at an oblique angle will be sheared in the z-direction. This can be corrected with the Huygens Object Stabilizer. Multiview Light Sheet images can be fused interactively without the need for fiducial markers in the Huygens Fuser.

Object Stabilizer Fuser

More information

Introduction to deconvolution
Huygens Deconvolution software
Deconvolution images


SPIM GIF
Maximum Intensity Projection of a raw (left) and deconvolved (right) 3D image from mouse blastocysts acquired with a Digital Light Sheet microscope. Deconvolution was performed with the CMLE algorithm and the Huygens module for calculating the theoretical Light Sheet point-spread-function. Image was kindly provided by Dr. Marc Duque Ramirez and Dr. Ritsuya Niwayama (Hiiragi group) and Dr. Stefan Terjung (ALMF) from the EMBL Heidelberg, Germany.

See more: Images in the field of Genetics & Developmental Biology
Huygens Fused + Deconvolved
Raw SPIM
The image shows tubulin in the Arabidopsis plant leaf. You can see the somata clearly. Two separate images were acquired with illumination from opposing sides using the Zeiss Z1 Light Sheet. The raw czi images were deconvolved and fused with Huygens. Image kindly provided by Dr. Shingo Nagawa, Cell Biology Core Facility, Shanghai Center for Plant Stress Biology, CAS, China.

See more: Images in the field of Plant Biology

Fuser Exaples
Two examples of Zeiss Z1 Light Sheet datasets deconvolved and fused with the Huygens FUSER. The first dataset is from a Drosophila brain acquired at 360 degrees rotation (45 degrees steps), and the second set is from a chicken embryo imaged from two opposing sides. Image was kindly provided by Prof. Christophe Marcelle, Mrs. Marie Julie Dejardin (INMG) & Dr. Denis Ressnikoff (CIQLE), Université Lyon 1, France.

See more: Images in the field of Genetics & Developmental Biology
Results Fusion+cmle(left)
The image shows tubulin in the Arabidopsis plant leaf. You can see the somata clearly. Two separate images were acquired with illumination from opposing sides using the Zeiss Z1 Light Sheet. The raw czi images were deconvolved and fused with Huygens. Image kindly provided by Dr. Shingo Nagawa, Cell Biology Core Facility, Shanghai Center for Plant Stress Biology, CAS, China.

See more: Images in the field of Plant Biology

Comparison
Huygens deconvolution of high resolution LIght Sheet data. GFP labeled yolk granules in a C. elegans one-cell stage embryo before (left) and after deconvolution with the CMLE algorithm using a theoretical Light Sheet point-spread-function. Image kindly provided by Dr. Uros Krzic, Dr. Lars Hufnagel, and Dr. Yury Belyaev, European Molecular Biology Laboratory, Heidelberg, Germany.

See more: Images in the field of Genetics & Developmental Biology