Yes, THUNDER Imagers always keep both the raw data and the THUNDERed data. It is however possible to only keep the THUNDERed data to reduce data size.
The THUNDER Imagers offer three methods: Instant Computational Clearing (ICC), Small Volume Computational Clearing (SVCC) and Large Volume Computational Clearing (LVCC). Fastest method is ICC which removes background in real time on both 2D and 3D images. Image quality can further be improved by combining ICC with the decision-mask based 3D deconvolution. This combination is used in the SVCC and LVCC methods. All three methods work on the fly and are displayed in the viewer already during acquisition. None of the THUNDER methods influence the acquisition speed. See LAS X THUNDER tutorial.
Computational Clearing offers two parameters: “Feature scale” and “Strength”. Feature scale defines the maximum size of features which need to be unmasked from the background. The default value of this parameter is set very conservatively for each magnification but can be finetuned by the user. “Strength” defines how much of the blur is to be removed. Its default value depends on the selected THUNDER method. “Feature scale” and “Strength” can both be adjusted separately for each of the fluorescence channels. See LAS X THUNDER tutorial.
THUNDERed images are saved in the typical Leica file formats “lif”, “lof” and “xlef” (XLEF combined with TIF, JPEG, PNG, BMP or LOF). These established formats are compatible with image analysis software packages such as Aivia, and other analysis software that use open-source library bio-formats (I.e., FIJI and Omero).
Data can also be exported to the image formats TIF, JPEG, PNG and BMP and movie formats AVI, QuickTime, MP4 and WMV.
More about Thunder and Aivia.
Yes, stitched tile Z-stacks can be THUNDERed. The only size limit is the available space on the temp-drive.
There is no clear answer to this. It strongly depends on the specimen properties. Our THUNDER TechNote includes an example of a 150µm thick brain section, but this is not the limit. In the upper layers of this brain section, fine details can be resolved and segmented. Although the resolution and segmentation may be reduced for deeper layers, imaging at a depth of 150μm reveal significant details which are not visible in the raw data.
The answer is a clear Yes. Computational Clearing effectively differentiates between signal and background. Only background is removed while the signal is preserved and can be used for quantification. This is important when dealing with varying background intensities often found in biological samples. A comparison between signal intensities requires the removal of background first. Computational Clearing does this automatically without the need for local background removal algorithms which usually need to be fine-tuned. For more information on intensity quantification please see the THUNDER TechNote.
Typically, THUNDER is applied on the fly and displayed in the viewer during the acquisition. It is also possible to apply the THUNDER methods post acquisition. See LAS X THUNDER tutorial.
Comparison to deconvolution: The core technology in all THUNDER Imagers is Computational Clearing. Computational Clearing removes blur in real time and extends the range of applications to thick samples compared to conventional Widefield techniques. THUNDER Imagers offer all the benefits of camera-based systems such as high acquisition speed, low light stress, high dynamic range, and high sensitivity, allowing imaging under physiological conditions. Image quality can be further improved by combining Computational Clearing with 3D deconvolution as offered in the methods Small Volume Computational Clearing (SVCC) and Large Volume Computational Clearing (LVCC). LVCC specifically benefits from Computational Clearing as it makes thick samples more accessible for deconvolution.
Comparison to other imaging techniques: another way to acquire optical sections with camera-based imaging systems is by using multiple-point illumination, such as a Nipkow disk or grid-projecting devices. The latter introduces artifacts whenever the grid cannot be projected sharply in the focal plane so they can struggle with thick specimens. In addition, they require multiple exposures for each image which slows down acquisition and may cause more photo damage to the sample. Disk-based systems, on the other hand, must deal with the finite distance between pinholes which introduces light contamination from out-of-focus planes at certain imaging depths.
The benchmark for optical sectioning of 3D samples are confocal point scanners which offer superior quality high-contrast fluorescence images. However, with point scanning confocal systems, imaging large samples can be time consuming.
THUNDER Imagers are integrated, well-established system solutions dedicated to specific applications. The THUNDER Imager family offers solutions for 3D tissue, 3D cell cultures, model organisms and CLEM applications.
THUNDER Imagers enable users to image thick samples that would be beyond the capabilities of conventional widefield systems by removing blur making thick samples accessible for analysis.
THUNDER imagers increase physiological relevance by using highly sensitive cameras that apply a minimal light dose to your sample. Furthermore, the statistical significance of the data can be increased as background independent quantification enables the detection and analysis of more objects in your images.
THUNDER Imagers minimize time to result by skipping the post processing step. 3D Widefield images require a deconvolution step prior to image analysis. This step is not necessary on a THUNDER Imager since the images are THUNDERed during acquisition and can immediately be used for analysis saving time.