Restoration of Light Sheet Multi-View Data

since the resolution along the optical detection axis of the microscope is typically much worse than in the imaging plane, taking multiple images that are rotated around an axis perpendicular to the detection axis of the microscope can reveal features that are not well resolved in a single stack. To optimally use the information of a multi-angle measurement, the diff erent image stacks should be aligned in three dimensions: they need to be rotated into the same orientation and shiſt ed to obtain a precise overlay. T e aligned stacks can then be transformed into a single image stack by combining the pixel intensities. T is can be done in a straightforward manner, for instance by taking the average or the maxima of the pixel intensities, or in more sophisti- cated ways that take the local quality of the image (in terms

of their information content) into account. To facilitate the alignment of multi-angle views, small objects such as beads are frequently included in the sample to act as landmarks. Generally, this is not desirable since such beads are oſt en very bright and can obscure dim object features. Furthermore in practice, the inclusion of beads in large samples such as tissue or small living species is a challenging task. T e Huygens Light Sheet Fusion and Deconvolution Wizard provides a work-fl ow to automatically align a set of image stacks without the need to add external markers to the sample. T e resulting aligned stacks can then be combined into a single image using several options for combining pixel intensities, ranging from simple averaging to the use of the local image entropy as a quality measure. An important feature of the Huygens fusion wizard is the integration of the Huygens deconvo- lution algorithms into the fusion process. By deconvolving the data, the fusion process can be signifi cantly improved because the alignment process is facilitated by the increase in resolution and signal-to-noise ratio of the source images. Furthermore, the registration and deconvolution of the multi-angle views has been optimized for execution on both the central processor and on graphical processors. T us, the Huygens Light Sheet Fusion and Deconvolution Wizard provides a convenient and fast work-fl ow to deconvolve and fuse multiple LSFM views into a single high-quality image.


Figure 7 : De-skewing and deconvolution of lattice light sheet images. A skewed image of a breast carcinoma cell (cell line SUM159), expressing an ER marker linked to OX-GFP and a cytosolic protein that should mainly localize to lipid droplets, was acquired with a dithered, multi-Bessel beam Lattice Light Sheet system. The image was de-skewed in the Huygens Object Stabilizer. MIPs of the raw and deconvolved endoplasmic reticulum signal (A and B, respectively) and their corresponding magnifi cations (A’ and B’) show a more well-defi ned reticular ER pattern in the cell periphery and close to the nucleus after deconvolution. The line in panel A’ indicates the location in the raw and deconvolved image at which an intensity profi le was produced (C; raw, undashed; deconvolved, dashed line). Panel D shows a MIP of the marker that localizes to rings around lipid droplets. These rings are diffi cult to discern in single slices of the raw image (D’), whereas deconvolution makes their appearance much clearer (E). The intensity profi les of a line in D’ show an increase of signal (more than 2 times) and resolution (more than 1.5 times) after deconvolution (F; raw, undashed line; deconvolved, dashed line). Scale bars are in micrometers.


Drosophila brain . T e image of the sample was acquired with a Zeiss Light Sheet Z.1 (Carl Zeiss Microscopy, Jena, Germany) and shows a complete adult brain from Drosophila Melanogaster immunostained against GFP (green, Alexa488- coupled secondary antibody) and the protein Bruchpilot (red, Alexa633-coupled secondary antibody). Bruchpilot is a synaptic protein labeling all the neuropiles in the brain. T e GFP expression was controlled by Notch signaling (NRE-GFP transgene). T e sample was completely rotated around the y -axis to collect eight diff erent stack images (stepsize: 45 degrees), which were saved as raw data in a single Zeiss czi fi le ( Figure 4A ). T e Huygens Light Sheet Fusion and Deconvolution Wizard, which starts as a separate window in Huygens (version 18.04.0p4), was used to select the images from the .czi fi le to fuse, to set the deconvolution and fusion parameters, and fi nally to perform the fusion operation ( Figure 4A ). Each of the eight input images was fi rst deconvolved with the Classic Maximum Likelihood Estimation (CMLE) algorithm [ 7 ], and the regularization parameter for deconvolution (the signal-to-noise ratio; SNR) was set to 20 and 40 iterations. T e Wizard transformed the images, by rotating and shiſt ing and then fused them into the fi nal result. T e same fusion procedure was repeated without deconvolution to generate • 2018 September

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