pytomography.io.SPECT#

Submodules#

Package Contents#

Functions#

get_atteuation_map(headerfile)

Opens attenuation data from SIMIND output

get_SPECT_recon_algorithm_simind(projections_header[, ...])

get_projections(file[, index_peak])

Gets ObjectMeta, ImageMeta, and projections from a .dcm file.

get_attenuation_map_from_file(file_AM)

Gets an attenuation map from a DICOM file. This data is usually provided by the manufacturer of the SPECT scanner.

get_attenuation_map_from_CT_slices(files_CT, file_NM)

Converts a sequence of DICOM CT files (corresponding to a single scan) into a torch.Tensor object usable as an attenuation map in PyTomography. Note that it is recommended by https://jnm.snmjournals.org/content/57/1/151.long to use the vendors attenuation map as opposed to creating your own. As such, the get_attenuation_map_from_file should be used preferentially over this function, if you have access to an attenuation map from the vendor.

get_scatter_from_TEW(file, index_peak, index_lower, ...)

Gets an estimate of scatter projection data from a DICOM file using the triple energy window method.

get_blank_below_above(file_NM)

Obtains the number of blank z-slices at the sup (blank_above) and inf (blank_below) of the projection data. This method is entirely empircal, and looks for z slices where there are zero detected counts.

get_psfmeta_from_scanner_params(camera_model, ...)

Gets PSF metadata from SPECT camera/collimator parameters. Performs linear interpolation to find linear attenuation coefficient for lead collimators for energy values within the range 100keV - 600keV.

pytomography.io.SPECT.get_atteuation_map(headerfile)[source]#

Opens attenuation data from SIMIND output

Parameters:

headerfile (str) – Path to header file

Returns:

Tensor containing CT data.

Return type:

torch.tensor[Lx,Ly,Lz]

pytomography.io.SPECT.get_SPECT_recon_algorithm_simind(projections_header, scatter_headers=None, CT_header=None, psf_meta=None, prior=None, object_initial=None, recon_algorithm_class=OSEMOSL)[source]#
Parameters:
Return type:

torch.nn.Module

pytomography.io.SPECT.get_projections(file, index_peak=None)[source]#

Gets ObjectMeta, ImageMeta, and projections from a .dcm file.

Parameters:
  • file (str) – Path to the .dcm file

  • index_peak (int) – If not none, then the returned projections correspond to the index of this energy window. Otherwise returns all energy windows. Defaults to None.

Returns:

Required information for reconstruction in PyTomography.

Return type:

(ObjectMeta, ImageMeta, torch.Tensor[1, Ltheta, Lr, Lz])

pytomography.io.SPECT.get_attenuation_map_from_file(file_AM)[source]#

Gets an attenuation map from a DICOM file. This data is usually provided by the manufacturer of the SPECT scanner.

Parameters:

file_AM (str) – File name of attenuation map

Returns:

Tensor of shape [batch_size, Lx, Ly, Lz] corresponding to the atteunation map in units of cm:math:^{-1}

Return type:

torch.Tensor

pytomography.io.SPECT.get_attenuation_map_from_CT_slices(files_CT, file_NM, index_peak=0)[source]#

Converts a sequence of DICOM CT files (corresponding to a single scan) into a torch.Tensor object usable as an attenuation map in PyTomography. Note that it is recommended by https://jnm.snmjournals.org/content/57/1/151.long to use the vendors attenuation map as opposed to creating your own. As such, the get_attenuation_map_from_file should be used preferentially over this function, if you have access to an attenuation map from the vendor.

Parameters:
  • files_CT (Sequence[str]) – List of all files corresponding to an individual CT scan

  • file_NM (str) – File corresponding to raw PET/SPECT data (required to align CT with projections)

  • index_peak (int, optional) – Index corresponding to photopeak in projection data. Defaults to 0.

Returns:

Tensor of shape [Lx, Ly, Lz] corresponding to attenuation map.

Return type:

torch.Tensor

pytomography.io.SPECT.get_scatter_from_TEW(file, index_peak, index_lower, index_upper)[source]#

Gets an estimate of scatter projection data from a DICOM file using the triple energy window method.

Parameters:
  • file (str) – Filepath of the DICOM file

  • index_peak (int) – Index of the EnergyWindowInformationSequence DICOM attribute corresponding to the photopeak.

  • index_lower (int) – Index of the EnergyWindowInformationSequence DICOM attribute corresponding to lower scatter window.

  • index_upper (int) – Index of the EnergyWindowInformationSequence DICOM attribute corresponding to upper scatter window.

Returns:

Tensor corresponding to the scatter estimate.

Return type:

torch.Tensor[1,Ltheta,Lr,Lz]

pytomography.io.SPECT.get_blank_below_above(file_NM)#

Obtains the number of blank z-slices at the sup (blank_above) and inf (blank_below) of the projection data. This method is entirely empircal, and looks for z slices where there are zero detected counts.

Parameters:

file_NM (str) – Filepath to DICOM file.

Returns:

A tuple of two elements corresponding to the number of blank slices at the inf, and the number of blank slices at the sup.

Return type:

Sequence[int]

pytomography.io.SPECT.get_psfmeta_from_scanner_params(camera_model, collimator_name, energy_keV)[source]#

Gets PSF metadata from SPECT camera/collimator parameters. Performs linear interpolation to find linear attenuation coefficient for lead collimators for energy values within the range 100keV - 600keV.

Parameters:
  • camera_model (str) – Name of SPECT camera.

  • collimator_name (str) – Name of collimator used.

  • energy_keV (float) – Energy of the photopeak

Returns:

PSF metadata.

Return type:

PSFMeta