Source code for pytomography.transforms.SPECT.psf

from __future__ import annotations
from typing import Sequence
import torch
import torch.nn as nn
import numpy as np
import pytomography
from pytomography.utils import get_distance, compute_pad_size, pad_object, pad_object_z, unpad_object_z, rotate_detector_z, unpad_object
from pytomography.transforms import Transform
from pytomography.metadata import ObjectMeta, ImageMeta, PSFMeta
import time
    
[docs]class GaussianBlurNet(nn.Module): def __init__(self, layer_r, layer_z=None): super(GaussianBlurNet, self).__init__() self.layer_r = layer_r self.layer_z = layer_z
[docs] def forward(self, input): output = self.layer_r(torch.permute(input[0],(1,0,2))) # If 2D blurring if self.layer_z: output = self.layer_z(torch.permute(output,(2,1,0))) return torch.permute(output,(1,2,0)).unsqueeze(0)
[docs]def get_1D_PSF_layer( sigmas: np.array, kernel_size: int, ) -> torch.nn.Conv1d: """Creates a 1D convolutional layer that is used for PSF modeling. Args: sigmas (array): Array of length Lx corresponding to blurring (sigma of Gaussian) as a function of distance from scanner kernel_size (int): Size of the kernel used in each layer. Needs to be large enough to cover most of Gaussian Returns: torch.nn.Conv2d: Convolutional neural network layer used to apply blurring to objects of shape [batch_size, Lx, Ly, Lz] """ N = len(sigmas) layer = nn.Conv1d(N, N, kernel_size, groups=N, padding='same', padding_mode='zeros', bias=0, device=pytomography.device) x = torch.arange(-int(kernel_size//2), int(kernel_size//2)+1).to(pytomography.device).unsqueeze(0).unsqueeze(0).repeat((N,1,1)) sigmas = torch.tensor(sigmas).to(pytomography.device).to(pytomography.dtype).reshape((N,1,1)) kernel = torch.exp(-x**2 / (2*sigmas**2 + pytomography.delta)) kernel = kernel / kernel.sum(axis=-1).unsqueeze(-1) layer.weight.data = kernel.to(pytomography.dtype) return layer
[docs]class SPECTPSFTransform(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 :math:`\sigma` dependent on the distance of the point to the detector; that information is specified in the ``PSFMeta`` parameter. Args: psf_meta (PSFMeta): Metadata corresponding to the parameters of PSF blurring """ def __init__( self, psf_meta: PSFMeta, ) -> None: """Initializer that sets corresponding psf parameters""" super(SPECTPSFTransform, self).__init__() self.psf_meta = psf_meta
[docs] def configure( self, object_meta: ObjectMeta, image_meta: ImageMeta ) -> None: """Function used to initalize the transform using corresponding object and image metadata Args: object_meta (ObjectMeta): Object metadata. image_meta (ImageMeta): Image metadata. """ super(SPECTPSFTransform, self).configure(object_meta, image_meta) self.layers = {} for radius in np.unique(image_meta.radii): kernel_size_r = self.compute_kernel_size(radius, axis=0) kernel_size_z = self.compute_kernel_size(radius, axis=2) # Compute sigmas and normalize to pixel units sigma_r = self.get_sigma(radius)/object_meta.dx sigma_z = self.get_sigma(radius)/object_meta.dz layer_r = get_1D_PSF_layer(sigma_r, kernel_size_r) layer_z = get_1D_PSF_layer(sigma_z, kernel_size_z) if self.psf_meta.kernel_dimensions=='2D': self.layers[radius] = GaussianBlurNet(layer_r, layer_z) else: # 1D blurring self.layers[radius] = GaussianBlurNet(layer_r)
[docs] def compute_kernel_size(self, radius, axis) -> int: """Function used to compute the kernel size used for PSF blurring. In particular, uses the ``min_sigmas`` attribute of ``PSFMeta`` to determine what the kernel size should be such that the kernel encompasses at least ``min_sigmas`` at all points in the object. Returns: int: The corresponding kernel size used for PSF blurring. """ sigma_max = max(self.get_sigma(radius)) sigma_max /= self.object_meta.dr[axis] return (np.ceil(sigma_max * self.psf_meta.min_sigmas)*2 + 1).astype(int)
[docs] def get_sigma( self, radius: float, ) -> np.array: """Uses PSF Meta data information to get blurring :math:`\sigma` as a function of distance from detector. Args: radius (float): The distance from the detector. Returns: array: An array of length Lx corresponding to blurring at each point along the 1st axis in object space """ dim = self.object_meta.shape[0] + 2*compute_pad_size(self.object_meta.shape[0]) distances = get_distance(dim, radius, self.object_meta.dx) sigma = self.psf_meta.sigma_fit(distances, *self.psf_meta.sigma_fit_params) return sigma
[docs] def apply_psf(self, object, ang_idx): object_return = [] for i in range(len(ang_idx)): object_temp = object[i].unsqueeze(0) object_temp = self.layers[self.image_meta.radii[ang_idx[i]]](object_temp) object_return.append(object_temp) return torch.vstack(object_return)
@torch.no_grad()
[docs] def forward( self, object_i: torch.Tensor, ang_idx: int, ) -> torch.tensor: r"""Applies the PSF transform :math:`A:\mathbb{U} \to \mathbb{U}` for the situation where an object is being detector by a detector at the :math:`+x` axis. Args: 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 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 n PSF corrected projection. """ return self.apply_psf(object_i, ang_idx)
@torch.no_grad()
[docs] def backward( self, object_i: torch.Tensor, ang_idx: int, norm_constant: torch.Tensor | None = None, ) -> torch.tensor: r"""Applies the transpose of the PSF transform :math:`A^T:\mathbb{U} \to \mathbb{U}` for the situation where an object is being detector by a detector at the :math:`+x` axis. Since the PSF transform is a symmetric matrix, its implemtation is the same as the ``forward`` method. Args: 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 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 n PSF corrected projection. """ if norm_constant is not None: object_i = self.apply_psf(object_i, ang_idx) norm_constant = self.apply_psf(norm_constant, ang_idx) return object_i, norm_constant else: return self.apply_psf(object_i, ang_idx)