pytomography.projectors.CT.ct_conebeam_flatpanel_system_matrix#

Module Contents#

Classes#

CTConeBeamFlatPanelSystemMatrix

System matrix for a cone beam CT system with a flat detector panel. Backprojection supports FBP, but only for non-helical (i.e. fixed z) geometries.

Functions#

get_discrete_ramp_FFT(n)

FBP_filter(proj[, device])

pytomography.projectors.CT.ct_conebeam_flatpanel_system_matrix.get_discrete_ramp_FFT(n)[source]#
pytomography.projectors.CT.ct_conebeam_flatpanel_system_matrix.FBP_filter(proj, device=pytomography.device)[source]#
class pytomography.projectors.CT.ct_conebeam_flatpanel_system_matrix.CTConeBeamFlatPanelSystemMatrix(object_meta, proj_meta, N_splits=1, device=pytomography.device)[source]#

Bases: pytomography.projectors.SystemMatrix

System matrix for a cone beam CT system with a flat detector panel. Backprojection supports FBP, but only for non-helical (i.e. fixed z) geometries.

Parameters:

  • object_meta (ObjectMeta) – Metadata for object space

  • proj_meta (CTConeBeamFlatPanelProjMeta) – Projection metadata for the CT system

  • N_splits (int, optional) – Splits up computation of forward/back projection to save GPU memory. Defaults to 1.

  • device (str, optional) – Device on which projections are output. Defaults to pytomography.device.

_get_FBP_scale()[source]#
_get_FBP_preweight(idx)[source]#
_get_FBP_postweight(idx)[source]#
set_n_subsets(n_subsets)[source]#

Returns a list where each element consists of an array of indices corresponding to a partitioned version of the projections.

Parameters:

n_subsets (int) – Number of subsets to partition the projections into

Returns:

List of arrays where each array corresponds to the projection indices of a particular subset.

Return type:

list

get_projection_subset(projections, subset_idx)[source]#

Obtains subsampled projections \(g_m\) corresponding to subset index \(m\). CT conebeam flat panel partitions projections based on angle.

Parameters:

  • projections (torch.Tensor) – total projections \(g\)

  • subset_idx (int) – subset index \(m\)

Returns:

subsampled projections \(g_m\).

Return type:

torch.Tensor

get_weighting_subset(subset_idx)[source]#

Computes the relative weighting of a given subset (given that the projection space is reduced). This is used for scaling parameters relative to \(\tilde{H}_m^T 1\) in reconstruction algorithms, such as prior weighting \(\beta\)

Parameters:

subset_idx (int) – Subset index

Returns:

Weighting for the subset.

Return type:

float

compute_normalization_factor(subset_idx=None)[source]#

Computes the normalization factor \(H^T 1\)

Parameters:

subset_idx (int, optional) – Subset index for ths sinogram. If None, considers all elements. Defaults to None..

Returns:

Normalization factor.

Return type:

torch.Tensor

forward(object, subset_idx=None, FBP_post_weight=None, projection_type='matched')[source]#

Computes forward projection

Parameters:

  • object (torch.Tensor) – Object to be forward projected

  • subset_idx (int | None, optional) – Subset index \(m\) of the projection. If None, then projects to entire projection space. Defaults to None.

  • FBP_post_weight (torch.Tensor, optional) – _description_. Defaults to None.

  • projection_type (str) – Type of forward projection to use; defaults to mathced. (For implementing the adjoint of FBP, we need the option of using FBP weights in the forward projection).

Returns:

Projections corresponding to \(\int \mu dx\) along all LORs.

Return type:

torch.Tensor

backward(proj, subset_idx=None, projection_type='matched')[source]#

Computes back projection.

Parameters:

  • proj (torch.Tensor) – Projections to be back projected

  • subset_idx (int | None, optional) – Subset index \(m\) of the projection. Defaults to None.

  • projection_type (str, optional) – Type of back projection to use. To use with filtered back projection, use 'FBP', which weights all LORs accordingly for this geometry. Defaults to 'matched'.

Returns:

_description_

Return type:

torch.Tensor