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Kaz Wanelik (Unlicensed)

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VoCentering
Process categoryBrief description

Computational demand

for typical tomography data

(low, medium, high)

Comment(s)Reference(s)Common alternative process(es)
filter

To find an optimal value of CoR automatically.

High
  1. Since the computational cost of this auto-centring process is high, it is normally applied to a small but representative subset of data (this can most conveniently be done with the aid of the preview parameter of this process).
  2. Any reconstructor process that follows VoCentering included in a process chain after VoCentering will have its centre_of_rotation parameter automatically set to the value determined by this the auto-centring process.
  3. VoCentering should normally be included after a ring-artefact suppression process (such as, e.g. RemoveAllRings), but before any low-pass process such as PaganinFilter or FresnelFilter. 
Reliable method for calculating the center of rotation in parallel-beam tomography VoCenteringIterative

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Code Block
titleSavu Configurator command
>>> disp -avv

-------------------------------------------------------------------------------------
 1) VoCentering(savu.plugins.centering.vo_centering)                                 
  A plugin to find the center of rotation per frame                                  
    1)                preview : []
    A slice list of required frames (sinograms) to use in the calulation of the
    centre of rotation (this will not reduce the data size for subsequent plugins).
    2)            start_pixel : None
    The approximate centre. If value is None, take the value from .nxs file else set
    to image centre.
    3)            search_area : (-50, 50)
    Search area from horizontal approximate centre of the image.
    4)            in_datasets : []
    Create a list of the dataset(s) to process.
    5)          search_radius : 6
    Use for fine searching.
    6)                  ratio : 0.5
    The ratio between the size of object and FOV of the camera.
    7)           out_datasets : ['cor_raw', 'cor_fit']
    The default names.
    8)   datasets_to_populate : []
    A list of datasets which require this information.
    9)               row_drop : 20
    Drop lines around vertical center of the mask.
   10)                   step : 0.5
    Step of fine searching.
-------------------------------------------------------------------------------------

>>>

Additional notes

For basic information on this process, please use the disp -av (or disp -avv or disp-v[v] <process index>) command in Savu Configurator (see above). The table below is intended to provide some additional notes on a number of selected topics:

ItemParameter nameParameter formatExample(s)Comment(s)
Parameter valueEffect
1

preview




  1. Note that the preview parameter has a nested-cumulative behaviour, i. e. if one specifies VoCentering's preview parameter together with NxtomoLoader's preview parameter, then VoCentering will effectively be selecting a subset from NxtomoLoader's own subset (rather than the entire dataset). Note also that VoCentering's subset must always needs to be specified by indexing indices referring to NxtomoLoader's own subset (rather than the entire dataset). For instance, one must set VoCentering's preview parameter to [:, 0, :] in order to select the initial slice of any dataset previously loaded by NxtomoLoader, which may, for example, be the [:, 123:456, :] subset of (456-123=333 slices of) the entire dataset.
  2. If you use any kind of parameter tuning before applying any parameter tuning in one (or more) process(es) preceding VoCentering, then the latter will automatically receive (from the immediately preceding process) a dataset of order higher than 3. In this case, if you leave VoCentering's preview parameter at its default value (i.e. [ ]), then VoCentering will have too process the entire rank-4 dataN (N>3) dataset, which is hardly ever desired. To avoid this situationwaste of resources, you must set should specify VoCentering's preview parameter to be a desired, reasonably-sized rank-3 slice of that the incoming rank-4 N dataset. For instance, if you have generated a dataset of shape (D, img_W, img_L, T) by subjecting the original (D, img_W, img_L) data to a single parameter-tuning operation, then your new, parameter-tuned rank-4 dataset needs to be sliced in the last dimension (corresponding to T), e. g. one can use the [:, mid, :, 0] slice of that parameterthis rank-tuned dataset will give you the 4 dataset to select its rank-3 subset containing the middle sinogram that was created with the initial value of your the tuning parameter.    
2

start_pixel




An initial estimate for the pixel coordinate of an optimal CoR.
3

search_area




If the value of the search_area parameter is set to the default interval of (-50, 50), then VoCentering will attempt attempts to search for an optimal value of CoR in the (start_pixel - 50, start_pixel + 50) interval (if the user-specified value of start_pixel is None, then img_W/2 (or a value found in /entry1/tomo_entry/instrument/detector/x_rotation_axis_pixel_position) is used instead). For some datasets, this default, 100-pixel-wide range of search (100 pixel) may not be not sufficiently large to include an optimal value of CoR, leading to subsequent sub-optimal reconstruction. Therefore, if the value of CoR determined by VoCentering is found to coincide with one of the search-interval limits (i. e. either start_pixel - 50 or start_pixel + 50), then this value of CoR may not necessarily be optimal, and one should then re-run VoCentering with a larger value of the search_area parameter to confirm this result.   
4

in_datasets





5search_radius



6ratio



7out_datasets



8

datasets_to_populate





9row_drop



10step


Floating-point or integer value in pixel units.

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