pytomography.transforms.SPECT.attenuation#

Module Contents#

Classes#

SPECTAttenuationTransform

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 a filepath consisting of folder containing CT DICOM files all pertaining to the same scan

Functions#

get_prob_of_detection_matrix(attenuation_map, dx)

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 [Lx, Ly, Lz] corresponding to the attenuation coefficient in :math:`{text{cm}^{-1}}

  • dx (float) – Axial plane pixel spacing.

Returns:

Tensor of 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, mode='constant', assume_padded=True, HU2mu_technique='from_table')[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 a filepath consisting of folder containing CT DICOM files all pertaining to the same scan

Parameters:

  • attenuation_map (torch.tensor) – Tensor of 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.

  • mode (str) – Mode used for extrapolation of CT beyond edges when aligning DICOM SPECT/CT data. Defaults to ‘constant’, which means the image is padded with zeros.

  • assume_padded (bool) – Assumes objects and projections fed into forward and backward methods are padded, as they will be in reconstruction algorithms

  • HU2mu_technique (str) – Technique to convert HU to attenuation coefficients. The default, ‘from_table’, uses a table of coefficients for bilinear curves obtained for a variety of common radionuclides. The technique ‘from_cortical_bone_fit’ looks for a cortical bone peak in the scan and uses that to obtain the bilinear coefficients. For phantom scans where the attenuation coefficient is always significantly less than bone, the corticol bone technique will still work, since the first part of the bilinear curve (in the air to water range) does not depend on the cortical bone fit. Alternatively, one can provide an arbitrary function here which takes in a 3D scan with units of HU and converts to mu.

configure(object_meta, proj_meta)[source]#

Function used to initalize the transform using corresponding object and projection metadata

Parameters:
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 [Lx, Ly, Lz] being projected along axis=0.

  • ang_idx (torch.Tensor) – The projection indices: used to find the corresponding angle in projection space corresponding to each projection angle in object_i.

Returns:

Tensor of 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)[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 [Lx, Ly, Lz] being projected along axis=0.

  • ang_idx (torch.Tensor) – The projection indices: used to find the corresponding angle in projection 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 [Lx, Ly, Lz] such that projection of this tensor along the first axis corresponds to an attenuation corrected projection.

Return type:

torch.tensor

compute_average_prob_matrix()[source]#