pytomography.transforms.SPECT.attenuation
#
Module Contents#
Classes#
obj2obj transform used to model the effects of attenuation in SPECT. This transform accepts either an |
Functions#
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Converts an attenuation map of \(\text{cm}^{-1}\) to a probability of photon detection matrix (scanner at +x). Note that this requires the attenuation map to be at the energy of photons being emitted. |
- pytomography.transforms.SPECT.attenuation.get_prob_of_detection_matrix(attenuation_map, dx)[source]#
Converts an attenuation map of \(\text{cm}^{-1}\) to a probability of photon detection matrix (scanner at +x). Note that this requires the attenuation map to be at the energy of photons being emitted.
- Parameters:
attenuation_map (torch.tensor) – Tensor of size [batch_size, Lx, Ly, Lz] corresponding to the attenuation coefficient in :math:`{text{cm}^{-1}}
dx (float) – Axial plane pixel spacing.
- Returns:
Tensor of size [batch_size, Lx, Ly, Lz] corresponding to probability of photon being detected at detector at +x axis.
- Return type:
torch.tensor
- class pytomography.transforms.SPECT.attenuation.SPECTAttenuationTransform(attenuation_map=None, filepath=None)[source]#
Bases:
pytomography.transforms.Transform
obj2obj transform used to model the effects of attenuation in SPECT. This transform accepts either an
attenuation_map
(which must be aligned with the SPECT projection data) or afilepath
consisting of folder containing CT DICOM files all pertaining to the same scan- Parameters:
attenuation_map (torch.tensor) – Tensor of size [batch_size, Lx, Ly, Lz] corresponding to the attenuation coefficient in \({\text{cm}^{-1}}\) at the photon energy corresponding to the particular scan
filepath (Sequence[str]) – Folder location of CT scan; all .dcm files must correspond to different slices of the same scan.
- configure(object_meta, image_meta)[source]#
Function used to initalize the transform using corresponding object and image metadata
- Parameters:
object_meta (SPECTObjectMeta) – Object metadata.
image_meta (SPECTImageMeta) – Image metadata.
- Return type:
None
- forward(object_i, ang_idx)[source]#
Forward projection \(A:\mathbb{U} \to \mathbb{U}\) of attenuation correction.
- Parameters:
object_i (torch.tensor) – Tensor of size [batch_size, Lx, Ly, Lz] being projected along
axis=1
.ang_idx (torch.Tensor) – The projection indices: used to find the corresponding angle in image space corresponding to each projection angle in
object_i
.
- Returns:
Tensor of size [batch_size, Lx, Ly, Lz] such that projection of this tensor along the first axis corresponds to an attenuation corrected projection.
- Return type:
torch.tensor
- backward(object_i, ang_idx, norm_constant=None)[source]#
Back projection \(A^T:\mathbb{U} \to \mathbb{U}\) of attenuation correction. Since the matrix is diagonal, the implementation is the same as forward projection. The only difference is the optional normalization parameter.
- Parameters:
object_i (torch.tensor) – Tensor of size [batch_size, Lx, Ly, Lz] being projected along
axis=1
.ang_idx (torch.Tensor) – The projection indices: used to find the corresponding angle in image space corresponding to each projection angle in
object_i
.norm_constant (torch.tensor, optional) – A tensor used to normalize the output during back projection. Defaults to None.
- Returns:
Tensor of size [batch_size, Lx, Ly, Lz] such that projection of this tensor along the first axis corresponds to an attenuation corrected projection.
- Return type:
torch.tensor