We maximize the scan speed, minimize the intensities of the excitation lasers, and set the pixel sizes and Z-step intervals at the traditionally defined Nyquist limit to achieve a tolerable light exposure while ensuring accurate representation of the imaged structures ( Such issues are prominent in our 4D confocal microscopy studies of secretory compartments in yeast cells (Įt al., 2015). Moreover, the number of captured photons is severely constrained by the need to avoid photodamage to the cells and fluorophores ( Intracellular structures are dynamic, so the images need to be taken rapidly. Those conditions are hard to meet with live cell imaging if Z-stacks are being collected at regular intervals to create a 3D time-lapse (4D) data set (Įt al., 2008). Because the benefits of deconvolution are fully realized when the signals are strong, the creators of deconvolution software recommend capturing a large number of photons while keeping the pixel sizes and Z-step intervals relatively small. ![]() The usual input to a deconvolution algorithm is a Z-stack of optical sections generated by widefield or confocal microscopy. In addition, this paper and the associated raw data may prove to be useful for “stress testing” other deconvolution algorithms.ĭeconvolution is an established method for sharpening fluorescence images and removing background noise (Įt al., 2017). For researchers running older versions of Huygens, the Gaussian blur prefilter is still an effective method for processing very weak confocal data. An “Update” section at the end summarizes the recent developments. We therefore limited the changes to a few textual modifications that improve clarity and accuracy based on feedback from reviewers, readers, and scientists at SVI. Given these developments, extensive revision of the paper would be hard to justify. With newer versions of Huygens, the Gaussian blur prefilter is no longer needed. This outcome is ideal because it ensures that we and others will obtain excellent results with Huygens even under suboptimal imaging conditions. We find that this change completely solves the problems reported here for both real and simulated confocal data. After seeing the original version of the paper, scientists at SVI analyzed our image data and determined that deconvolution of very weak confocal signals could be improved by refining the background estimation procedure in Huygens. This revision has been prepared under somewhat unusual circumstances. Supplementary Figure 3 and Zip1_0.25%.tif and Zip1_100%.tif are the widefield image stacks used for Supplementary Figure 2 simulation_80x80x250_plus_noise.tif is the simulated confocal image stack used forįigure 3B simulation_40x40x120.tif is the simulated confocal image stack used for ![]() Video S2 Hmg1_1x.tif and Hmg1_8x.tif are the confocal image stacks used forįigure 2 simulation_80x80x250.tif is the simulated confocal image stack used for The following files are included: 4D_movie_1x.tif and 4D_movie_8x.tif are the 4D confocal data sets used for The data referenced by this article are under copyright with the following copyright statement: Copyright: © 2017 Day KJ et al.ĭata associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).ĭataset 1: TIFF files for the experimental and simulated image data are provided in the compressed folder Original Image Files.zip.
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