pytomography.projectors.SPECT#

Submodules#

Package Contents#

Classes#

SPECTSystemMatrix

System matrix for SPECT imaging implemented using the rotate+sum technique.

MonteCarloHybridSPECTSystemMatrix

System matrix for SPECT imaging implemented using the rotate+sum technique.

StarGuideSystemMatrix

System matrix for the StarGuide SPECT imaging system form General Electric Healthcare.
class pytomography.projectors.SPECT.SPECTSystemMatrix(obj2obj_transforms, proj2proj_transforms, object_meta, proj_meta, object_initial_based_on_camera_path=False)[source]#

Bases: pytomography.projectors.system_matrix.SystemMatrix

System matrix for SPECT imaging implemented using the rotate+sum technique.

Parameters:

  • obj2obj_transforms (Sequence[Transform]) – Sequence of object mappings that occur before forward projection.

  • proj2proj_transforms (Sequence[Transform]) – Sequence of proj mappings that occur after forward projection.

  • object_meta (SPECTObjectMeta) – SPECT Object metadata.

  • proj_meta (SPECTProjMeta) – SPECT projection metadata.

  • object_initial_based_on_camera_path (bool) – Whether or not to initialize the object estimate based on the camera path; this sets voxels to zero that are outside the SPECT camera path. Defaults to False.

_get_object_initial(device=None)[source]#

Returns an initial object estimate used in reconstruction algorithms. By default, this is a tensor of ones with the same shape as the object metadata.

Returns:

Initial object used in reconstruction algorithm.

Return type:

torch.Tensor

set_n_subsets(n_subsets)[source]#

Sets the subsets for this system matrix given n_subsets total subsets.

Parameters:

n_subsets (int) – number of subsets used in OSEM

Return type:

list

get_projection_subset(projections, subset_idx)[source]#

Gets the subset of projections \(g_m\) corresponding to index \(m\).

Parameters:

  • projections (torch.tensor) – full 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 \(H_m^T 1\) 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]#

Function used to get normalization factor \(H^T_m 1\) corresponding to projection subset \(m\).

Parameters:

subset_idx (int | None, optional) – Index of subset. If none, then considers all projections. Defaults to None.

Returns:

normalization factor \(H^T_m 1\)

Return type:

torch.Tensor

forward(object, subset_idx=None)[source]#

Applies forward projection to object for a SPECT imaging system.

Parameters:

  • object (torch.tensor[Lx, Ly, Lz]) – The object to be forward projected

  • subset_idx (int, optional) – Only uses a subset of angles \(g_m\) corresponding to the provided subset index \(m\). If None, then defaults to the full projections \(g\).

Returns:

forward projection estimate \(g_m=H_mf\)

Return type:

torch.tensor

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

Applies back projection to proj for a SPECT imaging system.

Parameters:

  • proj (torch.tensor) – projections \(g\) which are to be back projected

  • subset_idx (int, optional) – Only uses a subset of angles \(g_m\) corresponding to the provided subset index \(m\). If None, then defaults to the full projections \(g\).

  • return_norm_constant (bool) – Whether or not to return \(H_m^T 1\) along with back projection. Defaults to ‘False’.

Returns:

the object \(\hat{f} = H_m^T g_m\) obtained via back projection.

Return type:

torch.tensor

class pytomography.projectors.SPECT.MonteCarloHybridSPECTSystemMatrix(object_meta, proj_meta, n_events, n_parallel, obj2obj_transforms, proj2proj_transforms, attenuation_map_140keV, energy_window_params, primary_window_idx, isotope_names, isotope_ratios, collimator_type, crystal_thickness, cover_thickness, backscatter_thickness, energy_resolution_140keV=0, advanced_energy_resolution_model=None, advanced_collimator_modeling=False)[source]#

Bases: SPECTSystemMatrix

System matrix for SPECT imaging implemented using the rotate+sum technique.

Parameters:

  • obj2obj_transforms (Sequence[Transform]) – Sequence of object mappings that occur before forward projection.

  • proj2proj_transforms (Sequence[Transform]) – Sequence of proj mappings that occur after forward projection.

  • object_meta (SPECTObjectMeta) – SPECT Object metadata.

  • proj_meta (SPECTProjMeta) – SPECT projection metadata.

  • object_initial_based_on_camera_path (bool) – Whether or not to initialize the object estimate based on the camera path; this sets voxels to zero that are outside the SPECT camera path. Defaults to False.

  • n_events (int) –

  • n_parallel (int) –

  • attenuation_map_140keV (torch.Tensor) –

  • energy_window_params (Sequence[str]) –

  • primary_window_idx (int) –

  • isotope_names (Sequence[str]) –

  • isotope_ratios (Sequence[float]) –

  • collimator_type (str) –

  • crystal_thickness (float) –

  • cover_thickness (float) –

  • backscatter_thickness (float) –

  • energy_resolution_140keV (float) –

  • advanced_energy_resolution_model (str | None) –

  • advanced_collimator_modeling (bool) –

_get_proj_meta_subset(subset_idx)[source]#

Creates a new SPECTProjMeta that corresponds to a subset of projections :param subset_idx: Index of the subset to use :type subset_idx: int

Returns:

New SPECTProjMeta that corresponds to the subset of projections

Return type:

SPECTProjMeta

Parameters:

subset_idx (int) –

forward(object, subset_idx=None)[source]#

Runs the Monte Carlo scatter simulation using SIMIND and returns the simulated projections. :param object: Object to simulate :type object: torch.Tensor :param subset_idx: Index of the subset to use. If None, then all projections are used. Defaults to None. :type subset_idx: int | None, optional

Returns:

Simulated projections

Return type:

torch.Tensor

Parameters:

  • object (torch.Tensor) –

  • subset_idx (int | None) –

class pytomography.projectors.SPECT.StarGuideSystemMatrix(object_meta, proj_meta, obj2obj_transforms=[], proj2proj_transforms=[])[source]#

Bases: pytomography.projectors.system_matrix.SystemMatrix

System matrix for the StarGuide SPECT imaging system form General Electric Healthcare.

Parameters:

  • obj2obj_transforms (Sequence[Transform]) – Sequence of object mappings that occur before forward projection.

  • proj2proj_transforms (Sequence[Transform]) – Sequence of proj mappings that occur after forward projection.

  • object_meta (SPECTObjectMeta) – SPECT Object metadata.

  • proj_meta (StarGuideProjMeta) – Projection metadata pertaining to the StarGuide system.

  • object_initial_based_on_camera_path (bool) – Whether or not to initialize the object estimate based on the camera path; this sets voxels to zero that are outside the SPECT camera path. Defaults to False.

forward(object, subset_idx=None)[source]#

Applies forward projection to object.

Parameters:

  • object (torch.tensor[Lx, Ly, Lz]) – The object to be forward projected

  • subset_idx (int, optional) – Only uses a subset of angles \(g_m\) corresponding to the provided subset index \(m\). If None, then defaults to the full projections \(g\).

Returns:

forward projection estimate \(g_m=H_mf\)

Return type:

torch.tensor

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

Applies back projection.

Parameters:

  • proj (torch.tensor) – projections \(g\) which are to be back projected

  • subset_idx (int, optional) – Only uses a subset of angles \(g_m\) corresponding to the provided subset index \(m\). If None, then defaults to the full projections \(g\).

  • return_norm_constant (bool) – Whether or not to return \(H_m^T 1\) along with back projection. Defaults to ‘False’.

Returns:

the object \(\hat{f} = H_m^T g_m\) obtained via back projection.

Return type:

torch.tensor

compute_normalization_factor(subset_idx=None)[source]#

Function used to get normalization factor \(H^T_m 1\) corresponding to projection subset \(m\).

Parameters:

subset_idx (int | None, optional) – Index of subset. If none, then considers all projections. Defaults to None.

Returns:

normalization factor \(H^T_m 1\)

Return type:

torch.Tensor

set_n_subsets(n_subsets)[source]#

Sets the subsets for this system matrix given n_subsets total subsets.

Parameters:

n_subsets (int) – number of subsets used in OSEM

Return type:

list

get_projection_subset(projections, subset_idx)[source]#

Gets the subset of projections \(g_m\) corresponding to index \(m\).

Parameters:

  • projections (torch.tensor) – full projections \(g\)

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

Returns:

subsampled projections \(g_m\)

Return type:

torch.tensor

_translate_object(obj, translations)[source]#

Internal function that applies translations to an object with a batch size dimension.

Parameters:

  • obj (torch.Tensor) – Object to be translated

  • translations (torch.Tensor) – Translations for each object in the batch

Returns:

Translated object

Return type:

torch.Tensor