pytomography.utils.misc#

Module Contents#

Functions#

rev_cumsum(x)

Reverse cumulative sum along the first axis of a tensor of shape [Lx, Ly, Lz].

get_distance(Lx, r, dx)

Given the radial distance to center of object space from the scanner, computes the distance between each parallel plane (i.e. (y-z plane)) and a detector located at +x. This function is used for SPECT PSF modeling where the amount of blurring depends on thedistance from the detector.

get_object_nearest_neighbour(object, shifts[, mode])

Given an object tensor, finds the nearest neighbour (corresponding to shifts) for each voxel (done by shifting object by i,j,k)

print_collimator_parameters()

Prints all the available SPECT collimator parameters

check_if_class_contains_method(instance, method_name)

Checks if class corresponding to instance implements the method method_name

get_1d_gaussian_kernel(sigma, kernel_size[, padding_mode])

Returns a 1D gaussian blurring kernel
pytomography.utils.misc.rev_cumsum(x)[source]#

Reverse cumulative sum along the first axis of a tensor of shape [Lx, Ly, Lz]. since this is used with SPECT attenuation correction, the initial voxel only contributes 1/2.

Parameters:

x (torch.tensor[Lx,Ly,Lz]) – Tensor to be summed

Returns:

The cumulatively summed tensor.

Return type:

torch.tensor[Lx, Ly, Lz]

pytomography.utils.misc.get_distance(Lx, r, dx)[source]#

Given the radial distance to center of object space from the scanner, computes the distance between each parallel plane (i.e. (y-z plane)) and a detector located at +x. This function is used for SPECT PSF modeling where the amount of blurring depends on thedistance from the detector.

Parameters:

  • Lx (int) – The number of y-z planes to compute the distance of

  • r (float) – The radial distance between the central y-z plane and the detector at +x.

  • dx (float) – The spacing between y-z planes in Euclidean distance.

Returns:

An array of distances for each y-z plane to the detector.

Return type:

np.array[Lx]

pytomography.utils.misc.get_object_nearest_neighbour(object, shifts, mode='replicate')[source]#

Given an object tensor, finds the nearest neighbour (corresponding to shifts) for each voxel (done by shifting object by i,j,k)

Parameters:

  • object (torch.Tensor) – Original object

  • shifts (list[int]) – List of three integers [i,j,k] corresponding to neighbour location

Returns:

Shifted object whereby each voxel corresponding to neighbour [i,j,k] of the object.

Return type:

torch.Tensor

pytomography.utils.misc.print_collimator_parameters()[source]#

Prints all the available SPECT collimator parameters

pytomography.utils.misc.check_if_class_contains_method(instance, method_name)[source]#

Checks if class corresponding to instance implements the method method_name

Parameters:

  • instance (Object) – A python object

  • method_name (str) – Name of the method of the object being checked

pytomography.utils.misc.get_1d_gaussian_kernel(sigma, kernel_size, padding_mode='zeros')[source]#

Returns a 1D gaussian blurring kernel

Parameters:

  • sigma (float) – Sigma (in pixels) of blurring pixels

  • kernel_size (int) – Size of kernel used

  • padding_mode (str, optional) – Type of padding. Defaults to ‘zeros’.

Returns:

Torch Conv1d layer corresponding to the gaussian kernel

Return type:

Conv1d