pytomography.io.PET.shared#

Module Contents#

Functions#

sinogram_coordinates(info)

Obtains two tensors: the first yields the sinogram coordinates (r/theta) given two crystal IDs (shape [N_crystals_per_ring, N_crystals_per_ring, 2]), the second yields the sinogram index given two ring IDs (shape [Nrings, Nrings])

sinogram_to_spatial(info)

Returns two tensors: the first yields the detector coordinates (x1/y1/x2/y2) of each of the two crystals given the element of the sinogram (shape [N_crystals_per_ring, N_crystals_per_ring, 2, 2]), the second yields the ring coordinates (z1/z2) given two ring IDs (shape [Nrings*Nrings, 2])

listmode_to_sinogram(detector_ids, info[, weights, ...])

Converts PET listmode data to sinogram

_listmodeTOF_to_sinogramTOF(detector_ids, info, tof_meta)

Helper function to listmode_to_sinogram for TOF data

get_detector_ids_from_trans_axial_ids(...)

Obtain detector IDs from individual part IDs

get_axial_trans_ids_from_info(info[, ...])

Get axial and transaxial IDs corresponding to each crystal in the scanner

get_scanner_LUT(info)

Obtains scanner lookup table (gives x/y/z coordinates for each detector ID)

sinogram_to_listmode(detector_ids, sinogram, info)

Obtains listmode data from a sinogram at the given detector IDs

smooth_randoms_sinogram(sinogram_random, info[, ...])

Smooths a PET randoms sinogram using a Gaussian filter in the r, theta, and z direction. Rebins the sinogram into (r,theta,z1,z2) before blurring (same blurring applied to z1 and z2)

randoms_sinogram_to_sinogramTOF(sinogram_random, ...)

Converts a non-TOF randoms sinogram to a TOF randoms sinogram.
pytomography.io.PET.shared.sinogram_coordinates(info)[source]#

Obtains two tensors: the first yields the sinogram coordinates (r/theta) given two crystal IDs (shape [N_crystals_per_ring, N_crystals_per_ring, 2]), the second yields the sinogram index given two ring IDs (shape [Nrings, Nrings])

Parameters:

info (dict) – PET geometry information dictionary

Returns:

LOR coordinates and sinogram index lookup tensors

Return type:

Sequence[torch.Tensor]

pytomography.io.PET.shared.sinogram_to_spatial(info)[source]#

Returns two tensors: the first yields the detector coordinates (x1/y1/x2/y2) of each of the two crystals given the element of the sinogram (shape [N_crystals_per_ring, N_crystals_per_ring, 2, 2]), the second yields the ring coordinates (z1/z2) given two ring IDs (shape [Nrings*Nrings, 2])

Parameters:

info (dict) – PET geometry information dictionary

Returns:

Two tensors yielding spatial coordinates

Return type:

Sequence[torch.Tensor]

pytomography.io.PET.shared.listmode_to_sinogram(detector_ids, info, weights=None, normalization=False, tof_meta=None)[source]#

Converts PET listmode data to sinogram

Parameters:

  • detector_ids (torch.Tensor) – Listmode detector ID data

  • info (dict) – PET geometry information dictionary

  • weights (torch.Tensor, optional) – Binning weights for each listmode event. Defaults to None.

  • normalization (bool, optional) – Whether or not this is a normalization sinogram (need to do some extra steps). Defaults to False.

  • tof_meta (PETTOFMeta, optional) – PET TOF metadata. Defaults to None.

Returns:

PET sinogram

Return type:

torch.Tensor

pytomography.io.PET.shared._listmodeTOF_to_sinogramTOF(detector_ids, info, tof_meta, weights=None)[source]#

Helper function to listmode_to_sinogram for TOF data

Parameters:

  • detector_ids (torch.Tensor) – Listmode detector ID data

  • info (dict) – PET geometry information dictionary

  • weights (torch.Tensor, optional) – Binning weights for each listmode event. Defaults to None.

  • tof_meta (PETTOFMeta, optional) – PET TOF metadata. Defaults to None.

Returns:

PET TOF sinogram

Return type:

torch.Tensor

pytomography.io.PET.shared.get_detector_ids_from_trans_axial_ids(ids_trans_crystal, ids_trans_submodule, ids_trans_module, ids_trans_rsector, ids_axial_crystal, ids_axial_submodule, ids_axial_module, ids_axial_rsector, info)[source]#

Obtain detector IDs from individual part IDs

Parameters:

  • ids_trans_crystal (torch.Tensor) – Transaxial crystal IDs

  • ids_trans_submodule (torch.Tensor) – Transaxial submodule IDs

  • ids_trans_module (torch.Tensor) – Transaxial module IDs

  • ids_trans_rsector (torch.Tensor) – Transaxial rsector IDs

  • ids_axial_crystal (torch.Tensor) – Axial crystal IDs

  • ids_axial_submodule (torch.Tensor) – Axial submodule IDs

  • ids_axial_module (torch.Tensor) – Axial module IDs

  • ids_axial_rsector (torch.Tensor) – Axial rsector IDs

  • info (dict) – PET geometry information dictionary

Returns:

Tensor containing (spatial) detector IDs

Return type:

torch.Tensor

pytomography.io.PET.shared.get_axial_trans_ids_from_info(info, return_combinations=False, sort_by_detector_ids=False)[source]#

Get axial and transaxial IDs corresponding to each crystal in the scanner

Parameters:

  • info (dict) – PET geometry information dictionary

  • return_combinations (bool, optional) – Whether or not to return all possible combinations (crystal pairs). Defaults to False.

  • sort_by_detector_ids (bool, optional) – Whether or not to sort by increasing detector IDs. Defaults to False.

Returns:

IDs corresponding to axial/transaxial components of each part

Return type:

Sequence[torch.Tensor]

pytomography.io.PET.shared.get_scanner_LUT(info)[source]#

Obtains scanner lookup table (gives x/y/z coordinates for each detector ID)

Parameters:

info (dict) – PET geometry information dictionary

Returns:

Lookup table

Return type:

torch.Tensor[N_detectors, 3]

pytomography.io.PET.shared.sinogram_to_listmode(detector_ids, sinogram, info)[source]#

Obtains listmode data from a sinogram at the given detector IDs

Parameters:

  • detector_ids (torch.Tensor) – Detector IDs at which to obtain listmode data

  • sinogram (torch.Tensor) – PET sinogram

  • info (dict) – PET geometry information dictionary

Returns:

Listmode data

Return type:

torch.Tensor

pytomography.io.PET.shared.smooth_randoms_sinogram(sinogram_random, info, sigma_r=4, sigma_theta=4, sigma_z=4, kernel_size_r=21, kernel_size_theta=21, kernel_size_z=21)[source]#

Smooths a PET randoms sinogram using a Gaussian filter in the r, theta, and z direction. Rebins the sinogram into (r,theta,z1,z2) before blurring (same blurring applied to z1 and z2)

Parameters:

  • sinogram_random (torch.Tensor) – PET sinogram of randoms

  • info (dict) – PET geometry information dictionary

  • sigma_r (float, optional) – Blurring (in pixel size) in r direction. Defaults to 4.

  • sigma_theta (float, optional) – Blurring (in pixel size) in r direction. Defaults to 4.

  • sigma_z (float, optional) – Blurring (in pixel size) in z direction. Defaults to 4.

  • kernel_size_r (int, optional) – Kernel size in r direction. Defaults to 21.

  • kernel_size_theta (int, optional) – Kernel size in theta direction. Defaults to 21.

  • kernel_size_z (int, optional) – Kernel size in z1/z2 diretions. Defaults to 21.

Returns:

Smoothed randoms sinogram

Return type:

torch.Tensor

pytomography.io.PET.shared.randoms_sinogram_to_sinogramTOF(sinogram_random, tof_meta, coincidence_timing_width)[source]#

Converts a non-TOF randoms sinogram to a TOF randoms sinogram.

Parameters:

  • sinogram_random (torch.Tenor) – Randoms sinogram (non-TOF)

  • tof_meta (PETTOFMeta) – PET TOF metadata

  • coincidence_timing_width (float) – Coincidence timing width used for the acceptance of coincidence events

Returns:

Randoms sinogram (TOF)

Return type:

torch.Tensor