pytomography.projectors.SPECT.dualhead_system_matrix

Module 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.

class pytomography.projectors.SPECT.dualhead_system_matrix.SPECTSystemMatrix(obj2obj_transforms, proj2proj_transforms, object_meta, proj_meta, object_initial_based_on_camera_path=False)

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)

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)

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)

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)

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)

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)

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)

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.dualhead_system_matrix.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)

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)

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)

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) –