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Restoration of Light Sheet Multi-View Data


Figure 4 : Fusion of multiview LSFM images. ( A) Shown is a series of eight images acquired with a Zeiss Z1 Light Sheet microscope at 45° intervals. These different views of a fl uorescent Drosophila brain can be loaded, deconvolved, and fused with the Huygens Light Sheet Fusion and Deconvolution Wizard resulting in a restored image (bottom right). (B) Maximum intensity projections (MIPs) of two single raw views (0 and 180 degrees) show high and low expression of NRE-GFP (Notch signaling) in areas in opposite parts of the sample. Arrowheads mark regions of degradation, which are restored by fusing multiple views of the sample. The right MIP shows the fused result of all eight raw views corresponding to the green channel in (A).


likelihood algorithm to account for both blurring and noise [ 6 , 7 ]. The basic idea of iterative image restoration (deconvo- lution) is to determine an estimate of the true object that, after convolution with the PSF, produces a synthetic image that closely matches the measured image. This estimate is computed taking into account the Poisson character of the noise and the optical model of the microscope. The most likely estimate is found in an iterative fashion: starting from


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a reasonable first estimate, it is adjusted each iteration, improving its likelihood until the gain drops below a preset threshold [ 6 ]. For eff ective deconvolution, the PSF must be numerically modeled or experimentally measured. An experi- mental PSF can be measured directly by imaging a point source such as a sub-resolution bead. In practice somewhat larger, brighter beads are used, and the PSF is determined by a process similar to deconvolution while exploiting the knowledge of the size of the beads, an approach also applied in the Huygens PSF Distiller. Although an experimental PSF potentially yields the “true” PSF of the given microscope setup, the measuring process can be cumbersome, and some distortions are not easily captured. For instance, aberrations that are induced by a mismatch between the refractive indices of the sample and the objective media lead to changes in the PSF as a function of the location of the image focus within the sample ( Figure 1B ). In such a case, numerical calculation of a spatially varying PSF using an accurate model that includes the aberration process may lead to improved deconvolution results [ 8 ]. Deconvolution of LSFM data proceeds, in broad lines, in the same way as other fluorescence imaging techniques such as widefield, confocal, or STED microscopy; however, it requires subtle but important adjustments to the image formation process as reflected in the PSF. The Huygens software allows the use of an experimental PSF, but it is also equipped with sophisticated optical models to calculate the PSF according to the specific type of LSFM used. The image parameters required are usually read from the image metadata and can also be adjusted, if needed ( Figure 2B ). Two


essentially different categories of LSFM are currently in use: either a cylindrical lens used to form a thin light sheet directly, or alternatively a beam of light is scanned through the sample to form a light sheet, illuminating the sample line by line. The Huygens software is able to model both approaches and can numerically calculate various light-sheet shapes: simple Gaussian light sheets generated by low-NA cylindrical lenses, more complicated diffraction patterns


www.microscopy-today.com • 2018 September


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