pytomography.transforms.SPECT
#
Submodules#
Package Contents#
Classes#
obj2obj transform used to model the effects of attenuation in SPECT. This transform accepts either an |
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obj2obj transform used to model the effects of PSF blurring in SPECT. The smoothing kernel used to apply PSF modeling uses a Gaussian kernel with width \(\sigma\) dependent on the distance of the point to the detector; that information is specified in the |
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proj2proj transformation used to set pixel values equal to zero at the first and last few z slices. This is often required when reconstructing DICOM data due to the finite field of view of the projection data, where additional axial slices are included on the top and bottom, with zero measured detection events. This transform is included in the system matrix, to model the sharp cutoff at the finite FOV. |
- class pytomography.transforms.SPECT.SPECTAttenuationTransform(attenuation_map=None, filepath=None, mode='constant')[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.
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.
- configure(object_meta, proj_meta)[source]#
Function used to initalize the transform using corresponding object and projection metadata
- Parameters:
object_meta (SPECTObjectMeta) – Object metadata.
proj_meta (SPECTProjMeta) – Projection 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 projection 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 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 [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
- class pytomography.transforms.SPECT.SPECTPSFTransform(psf_meta=None, kernel_f=None, psf_net=None)[source]#
Bases:
pytomography.transforms.Transform
obj2obj transform used to model the effects of PSF blurring in SPECT. The smoothing kernel used to apply PSF modeling uses a Gaussian kernel with width \(\sigma\) dependent on the distance of the point to the detector; that information is specified in the
SPECTPSFMeta
parameter. There are a few potential arguments to initialize this transform (i) psf_meta, which contains relevant collimator information to obtain a Gaussian PSF model that works for low/medium energy SPECT (ii) kernel_f, an callable function that gives the kernel at any source-detector distance \(d\), or (iii) psf_net, a network configured to automatically apply full PSF modeling to a given object \(f\) at all source-detector distances. Only one of the arguments should be given.- Parameters:
psf_meta (SPECTPSFMeta) – Metadata corresponding to the parameters of PSF blurring. In most cases (low/medium energy SPECT), this should be the only given argument.
kernel_f (Callable) – Function \(PSF(x,y,d)\) that gives PSF at every source-detector distance \(d\). It should be able to take in 1D numpy arrays as its first two arguments, and a single argument for the final argument \(d\). The function should return a corresponding 2D PSF kernel.
psf_net (Callable) – Network that takes in an object \(f\) and applies all necessary PSF correction to return a new object \(\tilde{f}\) that is PSF corrected, such that subsequent summation along the x-axis accurately models the collimator detector response.
- _configure_gaussian_model()[source]#
Internal function to configure Gaussian modeling. This is called when psf_meta is given in initialization
- _configure_kernel_model()[source]#
Internal function to configure arbitrary kernel modeling. This is called when kernel_f is given in initialization
- _configure_manual_net()[source]#
Internal function to configure the PSF net. This is called when psf_net is given in initialization
- configure(object_meta, proj_meta)[source]#
Function used to initalize the transform using corresponding object and projection metadata
- Parameters:
object_meta (SPECTObjectMeta) – Object metadata.
proj_meta (SPECTProjMeta) – Projections metadata.
- Return type:
None
- _compute_kernel_size(radius, axis)[source]#
Function used to compute the kernel size used for PSF blurring. In particular, uses the
min_sigmas
attribute ofSPECTPSFMeta
to determine what the kernel size should be such that the kernel encompasses at leastmin_sigmas
at all points in the object.- Returns:
The corresponding kernel size used for PSF blurring.
- Return type:
int
- _get_sigma(radius)[source]#
Uses PSF Meta data information to get blurring \(\sigma\) as a function of distance from detector.
- Parameters:
radius (float) – The distance from the detector.
- Returns:
An array of length Lx corresponding to blurring at each point along the 1st axis in object space
- Return type:
array
- _apply_psf(object, ang_idx)[source]#
Applies PSF modeling to an object with corresponding angle indices
- Parameters:
object (torch.tensor) – Tensor of shape
[batch_size, Lx, Ly, Lz]
corresponding to object rotated at different anglesang_idx (Sequence[int]) – List of length
batch_size
corresponding to angle of each object in the batch
- Returns:
Object with PSF modeling applied
- Return type:
torch.tensor
- forward(object_i, ang_idx)[source]#
Applies the PSF transform \(A:\mathbb{U} \to \mathbb{U}\) for the situation where an object is being detector by a detector at the \(+x\) axis.
- Parameters:
object_i (torch.tensor) – Tensor of size [batch_size, Lx, Ly, Lz] being projected along its first axis
ang_idx (int) – The projection indices: used to find the corresponding angle in projection 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 n PSF corrected projection.
- Return type:
torch.tensor
- backward(object_i, ang_idx, norm_constant=None)[source]#
Applies the transpose of the PSF transform \(A^T:\mathbb{U} \to \mathbb{U}\) for the situation where an object is being detector by a detector at the \(+x\) axis. Since the PSF transform is a symmetric matrix, its implemtation is the same as the
forward
method.- Parameters:
object_i (torch.tensor) – Tensor of size [batch_size, Lx, Ly, Lz] being projected along its first axis
ang_idx (int) – 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 [batch_size, Lx, Ly, Lz] such that projection of this tensor along the first axis corresponds to n PSF corrected projection.
- Return type:
torch.tensor
- class pytomography.transforms.SPECT.CutOffTransform(proj=None, file_NM=None)[source]#
Bases:
pytomography.transforms.Transform
proj2proj transformation used to set pixel values equal to zero at the first and last few z slices. This is often required when reconstructing DICOM data due to the finite field of view of the projection data, where additional axial slices are included on the top and bottom, with zero measured detection events. This transform is included in the system matrix, to model the sharp cutoff at the finite FOV.
- Parameters:
proj (torch.tensor) – Measured projection data.
file_NM (str | None) –
- forward(proj)[source]#
Forward projection \(B:\mathbb{V} \to \mathbb{V}\) of the cutoff transform.
- Parameters:
proj (torch.Tensor) – Tensor of size [batch_size, Ltheta, Lr, Lz] which transform is appplied to
- Returns:
Original projections, but with certain z-slices equal to zero.
- Return type:
torch.tensor
- backward(proj, norm_constant=None)[source]#
Back projection \(B^T:\mathbb{V} \to \mathbb{V}\) of the cutoff transform. Since this is a diagonal matrix, the implementation is the same as forward projection, but with the optional norm_constant argument.
- Parameters:
proj (torch.Tensor) – Tensor of size [batch_size, Ltheta, Lr, Lz] which transform is appplied to
norm_constant (torch.Tensor | None, optional) – A tensor used to normalize the output during back projection. Defaults to None.
- Returns:
Original projections, but with certain z-slices equal to zero.
- Return type:
torch.tensor