Recon#

This module allows the user to run pre-configured reconstruction algorithms on their data.

Analytical Reconstruction#

The CIL analytical reconstructions use CIL to filter and prepare the data using highly optimised routines. The filtered data is then backprojected using projectors from TIGRE or ASTRA-TOOLBOX.

Standard FBP (filtered-backprojection) should be used for parallel-beam data. FDK (Feldkamp, Davis, and Kress) is a filtered-backprojection algorithm for reconstruction of cone-beam data measured with a standard circular orbit.

The filter can be set to a predefined function, or a custom filter can be set. The predefined filters take the following forms:

FBP Filters

FBP - Reconstructor for parallel-beam geometry#

class cil.recon.FBP(input, image_geometry=None, filter='ram-lak', backend='tigre')[source]#

Creates an FBP reconstructor based on your parallel-beam acquisition data.

Parameters:

  • input (AcquisitionData) – The input data to reconstruct. The reconstructor is set-up based on the geometry of the data.

  • image_geometry (ImageGeometry, default used if None) – A description of the area/volume to reconstruct

  • filter (string, numpy.ndarray, default='ram-lak') – The filter to be applied. Can be a string from: {’ram-lak’, ‘shepp-logan’, ‘cosine’, ‘hamming’, ‘hann’}, or a numpy array.

  • backend (string) – The backend to use, can be ‘astra’ or ‘tigre’. Data must be in the correct order for requested backend.

Example

>>> from cil.utilities.dataexample import SIMULATED_PARALLEL_BEAM_DATA
>>> from cil.recon import FBP
>>> data = SIMULATED_PARALLEL_BEAM_DATA.get()
>>> fbp = FBP(data)
>>> out = fbp.run()

Notes

The reconstructor can be further customised using additional ‘set’ methods provided.

set_split_processing(slices_per_chunk=0)[source]#

Splits the processing in to chunks. Default, 0 will process the data in a single call.

Parameters:

out (slices_per_chunk, optional) – Process the data in chunks of n slices. It is recommended to use value of power-of-two.

Notes

This will reduce memory use but may increase computation time. It is recommended to tune it too your hardware requirements using 8, 16 or 32 slices.

This can only be used on simple and offset data-geometries.

run(out=None, verbose=1)[source]#

Runs the configured FBP recon and returns the reconstruction

Parameters:

  • out (ImageData, optional) – Fills the referenced ImageData with the reconstructed volume and suppresses the return

  • verbose (int, default=1) – Controls the verbosity of the reconstructor. 0: No output is logged, 1: Full configuration is logged

Returns:

The reconstructed volume. Suppressed if out is passed

Return type:

ImageData

reset()[source]#

Resets all optional configuration parameters to their default values

get_filter_array()#

Returns the filter array in the frequency domain.

Returns:

An array containing the filter values

Return type:

numpy.ndarray

Notes

The filter length N is 2^self.fft_order.

The indices of the array are interpreted as:

  • [0] The DC frequency component

  • [1:N/2] positive frequencies

  • [N/2:N-1] negative frequencies

The array can be modified and passed back using set_filter()

Notes

Filter reference in frequency domain: Eq. 1.12 - 1.15 T. M. Buzug. Computed Tomography: From Photon Statistics to Modern Cone-Beam CT. Berlin: Springer, 2008.

Plantagie, L. Algebraic filters for filtered backprojection, 2017 https://hdl.handle.net/1887/48289

plot_filter()#

Returns a plot of the filter array.

Returns:

A plot of the filter

Return type:

matplotlib.pyplot

set_fft_order(order=None)#

The width of the fourier transform N=2^order.

Parameters:

order (int, optional) – The width of the fft N=2^order

Notes

If None the default used is the power-of-2 greater than 2 * detector width, or 8, whichever is greater Higher orders will yield more accurate results but increase computation time.

set_filter(filter='ram-lak', cutoff=1.0)#

Set the filter used by the reconstruction.

Pre-set filters are constructed in the frequency domain. Pre-set filters are: ‘ram-lak’, ‘shepp-logan’, ‘cosine’, ‘hamming’, ‘hann’

Parameters:

  • filter (string, numpy.ndarray, default='ram-lak') – Pass a string selecting from the list of pre-set filters, or pass a numpy.ndarray with a custom filter.

  • cutoff (float, default = 1) – The cut-off frequency of the filter between 0 - 1 pi rads/pixel. The filter will be 0 outside the range rect(-frequency_cutoff, frequency_cutoff)

Notes

If passed a numpy array the filter must have length N = 2^self.fft_order

The indices of the array are interpreted as:

  • [0] The DC frequency component

  • [1:N/2] positive frequencies

  • [N/2:N-1] negative frequencies

set_filter_inplace(inplace=False)#

False (default) will allocate temporary memory for filtered projections. True will filter projections in-place.

Parameters:

inplace (boolean) – Sets the inplace filtering of projections

set_image_geometry(image_geometry=None)#

Sets a custom image geometry to be used by the reconstructor

Parameters:

image_geometry (ImageGeometry, default used if None) – A description of the area/volume to reconstruct

set_input(input)#

Update the input data to run the reconstructor on. The geometry of the dataset must be compatible with the reconstructor.

Parameters:

input (AcquisitionData) – A dataset with a compatible geometry

FDK - Reconstructor for cone-beam geometry#

class cil.recon.FDK(input, image_geometry=None, filter='ram-lak')[source]#

Creates an FDK reconstructor based on your cone-beam acquisition data using TIGRE as a backend.

Parameters:

  • input (AcquisitionData) – The input data to reconstruct. The reconstructor is set-up based on the geometry of the data.

  • image_geometry (ImageGeometry, default used if None) – A description of the area/volume to reconstruct

  • filter (string, numpy.ndarray, default='ram-lak') – The filter to be applied. Can be a string from: {’ram-lak’, ‘shepp-logan’, ‘cosine’, ‘hamming’, ‘hann’}, or a numpy array.

Example

>>> from cil.utilities.dataexample import SIMULATED_CONE_BEAM_DATA
>>> from cil.recon import FDK
>>> data = SIMULATED_CONE_BEAM_DATA.get()
>>> fdk = FDK(data)
>>> out = fdk.run()

Notes

The reconstructor can be futher customised using additional ‘set’ methods provided.

run(out=None, verbose=1)[source]#

Runs the configured FDK recon and returns the reconstruction.

Parameters:

  • out (ImageData, optional) – Fills the referenced ImageData with the reconstructed volume and suppresses the return

  • verbose (int, default=1) – Controls the verbosity of the reconstructor. 0: No output is logged, 1: Full configuration is logged

Returns:

The reconstructed volume. Suppressed if out is passed

Return type:

ImageData

get_filter_array()#

Returns the filter array in the frequency domain.

Returns:

An array containing the filter values

Return type:

numpy.ndarray

Notes

The filter length N is 2^self.fft_order.

The indices of the array are interpreted as:

  • [0] The DC frequency component

  • [1:N/2] positive frequencies

  • [N/2:N-1] negative frequencies

The array can be modified and passed back using set_filter()

Notes

Filter reference in frequency domain: Eq. 1.12 - 1.15 T. M. Buzug. Computed Tomography: From Photon Statistics to Modern Cone-Beam CT. Berlin: Springer, 2008.

Plantagie, L. Algebraic filters for filtered backprojection, 2017 https://hdl.handle.net/1887/48289

plot_filter()#

Returns a plot of the filter array.

Returns:

A plot of the filter

Return type:

matplotlib.pyplot

reset()#

Resets all optional configuration parameters to their default values

set_fft_order(order=None)#

The width of the fourier transform N=2^order.

Parameters:

order (int, optional) – The width of the fft N=2^order

Notes

If None the default used is the power-of-2 greater than 2 * detector width, or 8, whichever is greater Higher orders will yield more accurate results but increase computation time.

set_filter(filter='ram-lak', cutoff=1.0)#

Set the filter used by the reconstruction.

Pre-set filters are constructed in the frequency domain. Pre-set filters are: ‘ram-lak’, ‘shepp-logan’, ‘cosine’, ‘hamming’, ‘hann’

Parameters:

  • filter (string, numpy.ndarray, default='ram-lak') – Pass a string selecting from the list of pre-set filters, or pass a numpy.ndarray with a custom filter.

  • cutoff (float, default = 1) – The cut-off frequency of the filter between 0 - 1 pi rads/pixel. The filter will be 0 outside the range rect(-frequency_cutoff, frequency_cutoff)

Notes

If passed a numpy array the filter must have length N = 2^self.fft_order

The indices of the array are interpreted as:

  • [0] The DC frequency component

  • [1:N/2] positive frequencies

  • [N/2:N-1] negative frequencies

set_filter_inplace(inplace=False)#

False (default) will allocate temporary memory for filtered projections. True will filter projections in-place.

Parameters:

inplace (boolean) – Sets the inplace filtering of projections

set_image_geometry(image_geometry=None)#

Sets a custom image geometry to be used by the reconstructor

Parameters:

image_geometry (ImageGeometry, default used if None) – A description of the area/volume to reconstruct

set_input(input)#

Update the input data to run the reconstructor on. The geometry of the dataset must be compatible with the reconstructor.

Parameters:

input (AcquisitionData) – A dataset with a compatible geometry

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