SenseAI Accelerate uses compressed sensing to generate high-quality images from only a fraction of the original pixels – typically 10% but down to as little as 1% in some cases.
Because it’s requiring less data, it’s significantly faster – 10x up to 100x.
And becasue Accelerate uses 90% less beam dose, samples retain their integrity.
Faster imaging speeds – image in real time & make adjustments as you work
Lower beam dose for sensitive samples – integrity is preserved
Reduced data volumes without loss of critical information
Overcome instability (drift and vibration-effects), beam damage and slow frame rates as well as vast data sizes which makes post-processing problematic.
With SenseAI Accelerate, experiments can now be done live so you can make adjustments as you work. It uses less than 10% of the data.
It also reduces beam damage to preserve the integrity of sensitive samples.
SenseAI SEM supercharges all and any SEM. The subsampling reduces beam damage, improves stability, and speed by using 10% or less of the data.
This transforms your workflows and enables you to do more, better and quicker for less cost and resource.
SenseAI Accelerate can work with FIB-SEM and SBF (Serial Block Face)s for volumetric analyses across all sample types. The subsampling reduces charging effects and beam damage for regular block material samples, and both resin embedded and cryo preserved samples. It also dramatically speeds up workflows such that a sample that might take days to mill and image, would take a couple of hours. The data sizes are dramatically reduced too, 10% or less of the data required making the experiment and post-processing significantly quicker, easier and cheaper.
EDS and EELS can be typically slow to acquire datasets in both SEM and STEM and low-dose data can be noisy. Accelerate supercharges these spectroscopy analyses by 10x and more.
Accelerate can speed up light microscopy acquisition particularly in scanning techniques such as confocal. This reduces photodamage to samples and enables faster acquisition and less data.