This is an old revision of the document!
This logic can estimate ctf parameters from particle power spectra class sums, apply previously calculated ones to images and also lets the user choose the parameters manually. This correction for the phase information modulation during the imaging process is an essential part of getting high resolution structures out of input data.
If Ctf parameters are to be estimated, the user needs to provide class sums of classified power spectra due to the higher signal-to-noise ratio as compared to images. Afterwards, the images that make up the class sums can be corrected through the determined parameters, as well as images that contain the ctf information in their header.
To push one's dataset to the limits, estimating and applying ctf parameters is needed. So the user would classify the power spectra of the dataset's particles and then estimate the ctf parameters from the resulting classsums. The parameters from this (or from ctf correction of micrographs) can then either be applied directly or, as they're stored as header values, used in successive refinement.
In this mode, ctf parameters are calculated from input power spectra class sums through comparison with theoretical power spectra.
| Parameters | Description |
| Spherical Abberration | The spherical abberration of the microscope |
| Max astigmatism | The spherical abberration of the microscope |
| Max defocus | Maximum defocus to be considered for the first estimation round |
| Min defocus | Minimum defocus to be considered for the first estimation round |
| High tension in kV | Acceleration voltage of the microscope |
| Pixel size of class sum power spectra | Power spectra's pixel size |
| Input | Description |
| Input Images | Power spectra class sums. Each sum needs to have “classId” in the header |
| Output | Description |
| Classes with ctf info | The input images with ctf information in the header |
| Half-half PS | Half input image, half fitted power spectrum to show validity of the fit |
| New/Changed Header Values | Description |
| classId | The class id of the power spectrum |
| defocus | The estimated defocus |
| bFactor | The estimated b-factor |
| bFactorExp | The estimated b-factor exponent |
| astigmMag | The estimated magnitude of the astigmatism |
| astigmDir | The direction of the astigmatism |
| amplitudeContrastRatio | The ratio of amplitude to phase contrast |
| qualityOfPSFit | The cross-correlation coefficient of the power spectrum of the class and the theoretical estimated power spectrum |
| accelerationVoltage | The acceleration voltage used during image acquisition |
| pixelSize | The pixel size of the image |
| sphericalAberration | The spherical aberration of the image |
| Parameters | Description |
| Some changeable parameter | Description of this parameter |
| → and its sub-parameter | more description |
| Next main parameter | and more more more |
| → and its sub-parameter | … descriptions |
| Input | Description |
| FirstInput | Input Description 1 |
| SecondInput | Input Description 2 |
| ThirdInput | Input Description 3: Optional Input in Italic |
| Output | Description |
| FirstOutput | Output Description |
| New/Changed Header Values | Description |
| headerValue1 | what does it say? how is it changed? |
| headerValue2 | what does it say? how is it changed? |
| headerValue3 | what does it say? how is it changed? |
| headerValue4 | what does it say? how is it changed? |
In this paragraph, the “HOW a logic works under the hood” and WHY someone should use it can be elaborated with higher detail. Describes a scenario in an image processing workflow where this logic can be used to solve the resulting problem. Also, wikipages, publications or anything else describing the theory behind an algorithm should be linked here, if applicable.