Source code for pytomography.transforms.SPECT.atteunation

from __future__ import annotations
import torch
import torch.nn as nn
import pytomography
from pytomography.utils.helper_functions import rotate_detector_z, rev_cumsum, pad_object
from pytomography.transforms import Transform


[docs]def get_prob_of_detection_matrix(CT: torch.Tensor, dx: float) -> torch.tensor: r"""Converts an attenuation map of :math:`\text{cm}^{-1}` to a probability of photon detection matrix (scanner at +x). Note that this requires the attenuation map to be at the energy of photons being emitted. Args: CT (torch.tensor): Tensor of size [batch_size, Lx, Ly, Lz] corresponding to the attenuation coefficient in :math:`{\text{cm}^{-1}} dx (float): Axial plane pixel spacing. Returns: torch.tensor: Tensor of size [batch_size, Lx, Ly, Lz] corresponding to probability of photon being detected at detector at +x axis. """ return torch.exp(-rev_cumsum(CT * dx))
[docs]class SPECTAttenuationTransform(Transform): r"""obj2obj transform used to model the effects of attenuation in SPECT. Args: CT (torch.tensor): Tensor of size [batch_size, Lx, Ly, Lz] corresponding to the attenuation coefficient in :math:`{\text{cm}^{-1}}` at the photon energy corresponding to the particular scan """ def __init__(self, CT: torch.Tensor) -> None: super(SPECTAttenuationTransform, self).__init__() self.CT = CT.to(self.device) @torch.no_grad()
[docs] def forward( self, object_i: torch.Tensor, ang_idx: torch.Tensor, ) -> torch.Tensor: r"""Forward projection :math:`A:\mathbb{U} \to \mathbb{U}` of attenuation correction Args: 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 image space corresponding to each projection angle in ``object_i``. Returns: torch.tensor: Tensor of size [batch_size, Lx, Ly, Lz] such that projection of this tensor along the first axis corresponds to an attenuation corrected projection. """ CT = pad_object(self.CT) norm_factor = get_prob_of_detection_matrix(rotate_detector_z(CT.repeat(len(ang_idx),1,1,1), self.image_meta.angles[ang_idx]), self.object_meta.dx) object_i*=norm_factor return object_i
@torch.no_grad()
[docs] def backward( self, object_i: torch.Tensor, ang_idx: torch.Tensor, norm_constant: torch.Tensor | None = None, ) -> torch.Tensor: r"""Back projection :math:`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. Args: 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 image 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: torch.tensor: Tensor of size [batch_size, Lx, Ly, Lz] such that projection of this tensor along the first axis corresponds to an attenuation corrected projection. """ CT = pad_object(self.CT) norm_factor = get_prob_of_detection_matrix(rotate_detector_z(CT.repeat(len(ang_idx),1,1,1), self.image_meta.angles[ang_idx]), self.object_meta.dx) object_i*=norm_factor if norm_constant is not None: norm_constant*=norm_factor return object_i, norm_constant else: return object_i