pytomography.io.SPECT.dicom#

Note: This module is still being built and is not yet finished.

Module Contents#

Functions#

get_radii_and_angles(ds)

Gets projections with corresponding radii and angles corresponding to projection data from a DICOM file.

get_projections(file[, index_peak])

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

get_window_width(ds, index)

Computes the width of an energy window corresponding to a particular index in the DetectorInformationSequence DICOM attribute.

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_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_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.

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.

CT_to_attenuation_map(CT_HU, ds[, photopeak_window_index])

Obtains an attenuation map from a CT file. Requires dataset corresponding to projection data because energy windows are used to convert from HU to linear attenuation coefficients. See https://www.sciencedirect.com/science/article/pii/S0969804308000067 for more details.

get_affine_spect(ds)

Computes an affine matrix corresponding the coordinate system of a SPECT DICOM file.

get_affine_CT(ds, max_z)

Computes an affine matrix corresponding the coordinate system of a CT DICOM file. Note that since CT scans consist of many independent DICOM files, ds corresponds to an individual one of these files. This is why the maximum z value is also required (across all seperate independent DICOM files).

stitch_multibed(recons, files_NM[, method])

Stitches together multiple reconstructed objects corresponding to different bed positions.

pytomography.io.SPECT.dicom.get_radii_and_angles(ds)[source]#

Gets projections with corresponding radii and angles corresponding to projection data from a DICOM file.

Parameters:

ds (Dataset) – pydicom dataset object.

Returns:

Required image data for reconstruction.

Return type:

(torch.tensor[1,Ltheta, Lr, Lz], np.array, np.array)

pytomography.io.SPECT.dicom.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.dicom.get_window_width(ds, index)[source]#

Computes the width of an energy window corresponding to a particular index in the DetectorInformationSequence DICOM attribute.

Parameters:
  • ds (Dataset) – DICOM dataset.

  • index (int) – Energy window index corresponding to the DICOM dataset.

Returns:

Range of the energy window in keV

Return type:

float

pytomography.io.SPECT.dicom.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.dicom.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.dicom.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

pytomography.io.SPECT.dicom.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.dicom.CT_to_attenuation_map(CT_HU, ds, photopeak_window_index=0)[source]#

Obtains an attenuation map from a CT file. Requires dataset corresponding to projection data because energy windows are used to convert from HU to linear attenuation coefficients. See https://www.sciencedirect.com/science/article/pii/S0969804308000067 for more details.

Parameters:
  • CT_HU (np.array) – CT object in units of hounsfield units.

  • ds (Dataset) – DICOM data set of projection data

  • primary_window_index (int, optional) – The energy window corresponding to the photopeak. Defaults to 0.

  • photopeak_window_index (int) –

Returns:

Array of length 4 containins the 4 coefficients required for the bilinear transformation.

Return type:

np.array

pytomography.io.SPECT.dicom.get_affine_spect(ds)[source]#

Computes an affine matrix corresponding the coordinate system of a SPECT DICOM file.

Parameters:

ds (Dataset) – DICOM dataset of projection data

Returns:

Affine matrix.

Return type:

np.array

pytomography.io.SPECT.dicom.get_affine_CT(ds, max_z)[source]#

Computes an affine matrix corresponding the coordinate system of a CT DICOM file. Note that since CT scans consist of many independent DICOM files, ds corresponds to an individual one of these files. This is why the maximum z value is also required (across all seperate independent DICOM files).

Parameters:
  • ds (Dataset) – DICOM dataset of CT data

  • max_z (float) – Maximum value of z across all axial slices that make up the CT scan

Returns:

Affine matrix corresponding to CT scan.

Return type:

np.array

pytomography.io.SPECT.dicom.stitch_multibed(recons, files_NM, method='midslice')[source]#

Stitches together multiple reconstructed objects corresponding to different bed positions.

Parameters:
  • recons (torch.Tensor[n_beds, Lx, Ly, Lz]) – Reconstructed objects. The first index of the tensor corresponds to different bed positions

  • files_NM (list) – List of length n_beds corresponding to the DICOM file of each reconstruction

  • method (str, optional) – Method to perform stitching (see https://doi.org/10.1117/12.2254096 for all methods described). Available methods include 'midslice', 'average', 'crossfade', and 'TEM; (transition error minimization).

Returns:

Stitched together DICOM file. Note the new z-dimension size \(L_z'\).

Return type:

torch.Tensor[1, Lx, Ly, Lz’]