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| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Cryogenic electron microscopy | 3/3 | https://en.wikipedia.org/wiki/Cryogenic_electron_microscopy | reference | science, encyclopedia | 2026-05-05T10:04:06.678811+00:00 | kb-cron |
== Image processing in cryo-TEM == Even though in the majority of approaches in electron microscopy one tries to get the best resolution image of the material, it is not always the case in cryo-TEM. Besides all the benefits of high resolution images, the signal to noise ratio remains the main hurdle that prevents assigning orientation to each particle. For example, in macromolecule complexes, there are several different structures that are being projected from 3D to 2D during imaging and if they are not distinguished the result of image processing will be a blur. That is why the probabilistic approaches become more powerful in this type of investigation. There are two popular approaches that are widely used nowadays in cryo-EM image processing, the maximum likelihood approach that was discovered in 1998 and relatively recently adapted Bayesian approach. The maximum likelihood estimation approach comes to this field from the statistics. Here, all the possible orientations of particles are summed up to get the resulting probability distribution. We can compare this to a typical least square estimation where particles get exact orientations per image. This way, the particles in the sample get "fuzzy" orientations after calculations, weighted by corresponding probabilities. The whole process is iterative and with each next iteration the model gets better. The good conditions for making the model that closely represent the real structure is when the data does not have too much noise and the particles do not have any preferential direction. The main downside of maximum likelihood approach is that the result depends on the initial guess and model optimization can sometimes get stuck at local minimum. The Bayesian approach that is now being used in cryo-TEM is empirical by nature. This means that the distribution of particles is based on the original dataset. Similarly, in the usual Bayesian method there is a fixed prior probability that is changed after the data is observed. The main difference from the maximum likelihood estimation lies in special reconstruction term that helps smoothing the resulting maps while also decreasing the noise during reconstruction. The smoothing of the maps occurs through assuming prior probability to be a Gaussian distribution and analyzing the data in the Fourier space. Since the connection between the prior knowledge and the dataset is established, there is less chance for human factor errors which potentially increases the objectivity of image reconstruction. With emerging new methods of cryo-TEM imaging and image reconstruction the new software solutions appear that help to automate the process. After the empirical Bayesian approach have been implemented in the open source computer program RELION (REgularized LIkelihood OptimizatioN) for 3D reconstruction, the program became widespread in the cryo-TEM field. It offers a range of corrections that improve the resolution of reconstructed images, allows implementing versatile scripts using python language and executes the usual tasks of 2D/3D model classifications or creating de novo models.
== Gallery ==
== See also ==
Cryogenic scanning electron microscopy EM Data Bank Resolution (electron density) Single particle analysis Cryofixation Cryo bio-crystallography Electron tomography (ET) Virus crystallisation
== References ==