ImggTomographicReconstruction dialog.
Table of Contents
| Name | Direction | Type | Default | Description |
|---|---|---|---|---|
| InputWorkspace | Input | Workspace | Mandatory | Group of workspace holding images (with one spectrum per pixel row). |
| Method | Input | string | FBP (tomopy) | Reconstruction method. Allowed values: [‘FBP (tomopy)’] |
| OutputWorkspace | Output | WorkspaceGroup | Mandatory | Output reconstructed volume, as a group of workspaces where each workspace holds one slice of the volume. |
| CenterOfRotation | Input | number | Mandatory | Center of rotation for the reconstruction (in pixels). |
| RelaxationParameter | Input | number | 0.5 | Relaxation parameter for the reconstruction method. |
| MaximumCores | Input | number | 0 | Maximum number of cores to use for parallel runs. Leave empty to use all available cores. |
| MinProjectionAngle | Input | number | 0 | Minimum projection angle. |
| MaxProjectionAngle | Input | number | 180 | Maximum projection angle (assuming a uniform angle increase from first to last projection. |
Warning
This is an early, experimental version of the algorithm.
The input and output workspaces are workspace groups where every element is an image workspace. In the input workspace every image is a 2D projection from a different angle whereas in the output workspace every image is a slice of a reconstructed 3D volume. The input workspace must have one image workspace per projection from a tomography imaging experiment. The output workspace will have one image workspace for every slice of the output reconstructed volume.
The following method is supported: FBP (following the TomoPy implementation [TomoPy2014]).
The implementation of TomoPy methods are based on the TomoPy source code available from https://github.com/tomopy/tomopy/, which is:
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References:
| [TomoPy2014] | Gursoy D, De Carlo F, Xiao X, Jacobsen C. (2014). TomoPy: a framework for the analysis of synchrotron tomographic data. J. Synchrotron Rad. 21. 1188-1193 doi:10.1107/S1600577514013939 |
Example - ReconstructProjections
# Note: you would load FITS images like this:
# wsg = LoadFITS(Filename='FITS_small_01.fits', LoadAsRectImg=1, OutputWorkspace='projections')
wsg_name = 'projections'
# Produce 16 projections with 32x32 pixels
projections = []
unit_label=UnitFactory.create('Label')
unit_label.setLabel('width','cm')
for proj in range(0, 8):
wks_name = 'wks_proj_' + str(proj)
wks = CreateSampleWorkspace(NumBanks=32, BankPixelWidth=1, XMin=0, XMax=32, BinWidth=1, OutputWorkspace=wks_name)
wks.getAxis(0).setUnit('Label')
projections.append(wks)
wsg_proj = GroupWorkspaces(projections, OutputWorkspace=wsg_name)
wsg_reconstructed = ImggTomographicReconstruction(InputWorkspace=wsg_proj, CenterOfRotation=15)
rows = wsg_reconstructed.getItem(0).getNumberHistograms()
columns = wsg_reconstructed.getItem(0).blocksize()
print ("The output reconstruction has {0} slices of {1} by {2} pixels".
format(wsg_reconstructed.size(), rows, columns))
slice_idx = 2
coord_x = 8
coord_y = 15
print ("Value of pixel at coordinate ({0},{1}) in slice {2}: {3:.1f}".
format(coord_x, coord_y, slice_idx,
wsg_reconstructed.getItem(2).readY(coord_y)[coord_x]))
Output:
The output reconstruction has 32 slices of 32 by 32 pixels
Value of pixel at coordinate (8,15) in slice 2: 2.4
Categories: Algorithms | Diffraction\Imaging | Diffraction\Tomography