from __future__ import annotations
from typing import Sequence
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
import os
import pytomography
from pytomography.metadata.SPECT import SPECTObjectMeta, SPECTProjMeta, SPECTPSFMeta
from pytomography.utils import get_mu_from_spectrum_interp, compute_EW_scatter
from ..shared import get_header_value
[docs]relation_dict = {'unsignedinteger': 'int',
'shortfloat': 'float',
'int': 'int'}
[docs]def _get_projections_from_single_file(headerfile: str):
"""Gets projection data from a SIMIND header file.
Args:
headerfile (str): Path to the header file
distance (str, optional): The units of measurements in the SIMIND file (this is required as input, since SIMIND uses mm/cm but doesn't specify). Defaults to 'cm'.
Returns:
(torch.Tensor[1, Ltheta, Lr, Lz]): Simulated SPECT projection data.
"""
with open(headerfile) as f:
headerdata = f.readlines()
headerdata = np.array(headerdata)
num_proj = get_header_value(headerdata, 'total number of images', int)
proj_dim1 = get_header_value(headerdata, 'matrix size [1]', int)
proj_dim2 = get_header_value(headerdata, 'matrix size [2]', int)
number_format = get_header_value(headerdata, 'number format', str)
number_format= relation_dict[number_format]
num_bytes_per_pixel = get_header_value(headerdata, 'number of bytes per pixel', int)
imagefile = get_header_value(headerdata, 'name of data file', str)
dtype = eval(f'np.{number_format}{num_bytes_per_pixel*8}')
projections = np.fromfile(os.path.join(str(Path(headerfile).parent), imagefile), dtype=dtype)
projections = np.transpose(projections.reshape((num_proj,proj_dim2,proj_dim1))[:,::-1], (0,2,1))
projections = torch.tensor(projections.copy()).to(pytomography.device)
return projections
[docs]def get_projections(headerfiles: str | Sequence[str], weights: float = None):
"""Gets projection data from a SIMIND header file.
Args:
headerfile (str): Path to the header file
distance (str, optional): The units of measurements in the SIMIND file (this is required as input, since SIMIND uses mm/cm but doesn't specify). Defaults to 'cm'.
Returns:
(torch.Tensor[1, Ltheta, Lr, Lz]): Simulated SPECT projection data.
"""
if type(headerfiles) is str:
return _get_projections_from_single_file(headerfiles)
elif isinstance(headerfiles[0], Sequence) and not isinstance(headerfiles[0], str):
projections = []
for i, headerfiles_window_i in enumerate(headerfiles):
projections_window_i = []
for j, headerfiles_window_i_organ_j in enumerate(headerfiles_window_i):
projections_window_i_organ_j = get_projections(headerfiles_window_i_organ_j)
if weights is not None: projections_window_i_organ_j *= weights[j]
projections_window_i.append(projections_window_i_organ_j)
projections_window_i = torch.sum(torch.stack(projections_window_i), dim=0)
projections.append(projections_window_i)
projections = torch.stack(projections)
return projections.squeeze()
else:
projections = []
for i, headerfile in enumerate(headerfiles):
projections.append(get_projections(headerfile))
projections = torch.stack(projections)
return projections.squeeze()
[docs]def get_energy_window_bounds(headerfile: str) -> tuple[float, float]:
"""Computes the lower and upper bounds of the energy window from a SIMIND header file
Args:
headerfile (str): SIMIND header file
Returns:
tuple[float, float]: Lower and upper energies
"""
with open(headerfile) as f:
headerdata = f.readlines()
headerdata = np.array(headerdata)
lwr = get_header_value(headerdata, 'energy window lower level', np.float32)
upr = get_header_value(headerdata, 'energy window upper level', np.float32)
return lwr, upr
[docs]def get_energy_window_width(headerfile: str) -> float:
"""Computes the energy window width from a SIMIND header file
Args:
headerfile (str): Headerfile corresponding to SIMIND data
Returns:
float: Energy window width
"""
with open(headerfile) as f:
headerdata = f.readlines()
headerdata = np.array(headerdata)
lwr = get_header_value(headerdata, 'energy window lower level', np.float32)
upr = get_header_value(headerdata, 'energy window upper level', np.float32)
return upr - lwr
[docs]def combine_projection_data(
headerfiles: Sequence[str],
weights: Sequence[float]
):
"""Takes in a list of SIMIND headerfiles corresponding to different simulated regions and adds the projection data together based on the `weights`.
Args:
headerfiles (Sequence[str]): List of filepaths corresponding to the SIMIND header files of different simulated regions
weights (Sequence[str]): Amount by which to weight each projection relative.
Returns:
(SPECTObjectMeta, SPECTProjMeta, torch.Tensor): Returns necessary object/projections metadata along with the projection data
"""
projections = 0
for headerfile, weight in zip(headerfiles, weights):
projections_i = get_projections(headerfile)
projections += projections_i * weight
return projections
[docs]def get_attenuation_map(headerfile: str, smi_index_22: int = 3):
"""Opens attenuation data from SIMIND output
Args:
headerfile (str): Path to header file
smi_index_22 (int, optional): Value of provided in the simind simulation tag: " in:x22,<idx>x " where <idx> is 3 (mu) or 5 (mu-castor). You can check what value this is by default (if you did not provide it) by looking at simind.ini in the simind/smc_dir folder. Defaults to 3.
Returns:
torch.Tensor[batch_size, Lx, Ly, Lz]: Tensor containing attenuation map required for attenuation correction in SPECT/PET imaging.
"""
with open(headerfile) as f:
headerdata = f.readlines()
headerdata = np.array(headerdata)
matrix_size_1 = get_header_value(headerdata, 'matrix size [1]', int)
matrix_size_2 = get_header_value(headerdata, 'matrix size [2]', int)
matrix_size_3 = get_header_value(headerdata, 'matrix size [3]', int)
shape = (matrix_size_3, matrix_size_2, matrix_size_1)
imagefile = get_header_value(headerdata, 'name of data file', str)
CT = np.fromfile(os.path.join(str(Path(headerfile).parent), imagefile), dtype=np.float32)
# Flip "Z" ("X" in SIMIND) b/c "first density image located at +X" according to SIMIND manual
# Flip "Y" ("Z" in SIMIND) b/c axis convention is opposite for x22,5x (mu-castor format)
CT = np.transpose(CT.reshape(shape), (2,1,0))
if smi_index_22==5: # mu-castor format
CT = CT[:,::-1,::-1]
elif smi_index_22==3: # mu format
CT = CT[:,:,::-1]
CT = torch.tensor(CT.copy())
return CT.to(pytomography.device)
