Previewing#

Previewing is the way to change the dimensions of the input data by reducing them. It also can be interpreted as a data cropping or data slicing operation.

Reduction of the input data is often done to remove unnecessary/useless information, and to accelerate the processing time. It is also recommended to use when searching for optimal parameter values, see Parameter Tuning. Skip to Enabling data preview for information about how to use it in HTTomo.

Previewing in the loader#

Previewing is an important part of the loader (see HTTomo Loaders). Here, a brief explanation is given on how to use the preview parameter in the standard_tomo loader.

Note

HTTomo assumes that the input data is a three dimensional (3D) array, where the 1st axis is the angular dimension, the 2nd axis is the vertical \(Y\)-detector dimension and the 3rd axis is the horizontal \(X\)-detector dimension (see Fig. 7).

3D data

Fig. 7 3D projection data and their axes#

Structure of the preview parameter value#

The value of the preview parameter has three fields, one for each axis in the 3D input data, and also a start and stop field for each dimension:

preview:
 angles:
   start:
   stop:
 detector_y:
   start:
   stop:
 detector_x:
   start:
   stop:

Warning

Note that previewing in the angles dimension is not yet supported by loaders in HTTomo, but ignoring data along this dimension is a feature that will be coming in a future release.

Full data preview#

If the preview parameter is omitted entirely in the loader configuration, then the full data will be selected and no cropping/previewing will be applied. Ie, previewing is disabled in this case.

Enabling data preview#

In order to change the input data dimensions and accelerate the processing pipeline, one can do two of the following operations.

Note

Although this is optional, by doing this the size of the reconstructed volume is reduced without any detriment to the data, which can result in a significant speedup in post-processing analysis time.

In the figure below the projections have been cropped vertically and horizontally.

Before cropping pic1 and after pic2

  1. Reduce the size of the vertical dimension (detector- \(Y\)) by removing blank regions in your data (top and bottom cropping), see Fig. 8. The blank areas, if any, can be established by looking through the sequence of raw projections.

    preview:
  detector_y:
    start: 200
    stop: 1800

    This will crop the data starting at slice 200 and finishing at slice 1800, therefore resulting in the data with the vertical dimension equal to 1600 pixels. In Python this will be interpreted as [:,200:1800,:].

3D data, Y slicing

Fig. 8 Cropping detector- \(Y\) dimension of 3D projection data#

  1. Reduce the size of the horizontal dimension (detector- \(X\)) by removing blank regions in your data (cropping the left and right sides), see Fig. 9.

    Warning

    Please be aware that cropping this dimension can create issues with the automatic centering and potentially lead to reconstruction artefacts, especially if iterative methods are used. It is general practice to be more conservative with the cropping of the \(X\) detector dimension.

    preview:
  detector_x:
    start: 100
    stop: 2000

    In Python this will be interpreted as [:,:,100:2000].

3D data, X slicing

Fig. 9 Cropping detector- \(X\) dimension of 3D projection data#

One can combine vertical and horizontal cropping with:

preview:
  detector_y:
    start: 200
    stop: 1800
  detector_x:
    start: 100
    stop: 2000

The mid value#

The detector_y and detector_x dimension fields also support the value mid in addition to the start and/or stop fields. Specifying mid for either of these dimensions will result in the middle three slices of that dimension being selected.

Warning

The angles dimension field doesn’t support the value mid

Rules for omitting fields in the preview parameter value#

One may have noticed that, in many of the preview parameter value examples above, some fields were omitted. It’s infrequently needed to crop all three dimensions, and sometimes when cropping, only either the start or end is of interest.

With these in mind, along the general notion that anything is more readable when unnecessary information is omitted, there are several ways in which the

  • dimension fields

  • start/stop fields

in the preview parameter value can be omitted in the process list, and still achieve the desired cropping behavior.

Omitting one or more dimension fields#

If any of the three top-level dimension fields are omitted, then no cropping will be applied to the omitted dimension(s).

If a top-level dimension is provided but given no value, then no cropping will be applied to that dimension either. Ie, the following configuration will select the entire input data and apply no cropping/previewing:

preview:
  angles:
  detector_y:
  detector_x:

Omitting the start or stop fields#

For a given dimension field:

  • if the start field is omitted, then the start value is assumed to be 0

  • if the stop field is omitted, then the stop value is assumed to be the very last element in that dimension