Source code for pytomography.io.SPECT.simind

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 import SPECTObjectMeta, SPECTProjMeta, SPECTPSFMeta
from pytomography.utils import get_mu_from_spectrum_interp, compute_TEW
from ..shared import get_header_value

[docs]relation_dict = {'unsignedinteger': 'int', 'shortfloat': 'float', 'int': 'int'}
[docs]def get_metadata(headerfile: str, distance: str = 'cm', corrfile: str | None = None): """Obtains required metadata 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'. corrfile (str, optional): .cor file used in SIMIND to specify radial positions for non-circular orbits. This needs to be provided for non-standard orbits. Returns: (SPECTObjectMeta, SPECTProjMeta, torch.Tensor[1, Ltheta, Lr, Lz]): Required information for reconstruction in PyTomography. """ if distance=='mm': scale_factor = 1/10 elif distance=='cm': scale_factor = 1 with open(headerfile) as f: headerdata = f.readlines() headerdata = np.array(headerdata) proj_dim1 = get_header_value(headerdata, 'matrix size [1]', int) proj_dim2 = get_header_value(headerdata, 'matrix size [2]', int) dx = get_header_value(headerdata, 'scaling factor (mm/pixel) [1]', np.float32) / 10 # to mm dz = get_header_value(headerdata, 'scaling factor (mm/pixel) [2]', np.float32) / 10 # to mm dr = (dx, dx, dz) extent_of_rotation = get_header_value(headerdata, 'extent of rotation', np.float32) number_of_projections = get_header_value(headerdata, 'number of projections', int) start_angle = get_header_value(headerdata, 'start angle', np.float32) direction = get_header_value(headerdata, 'direction of rotation', str) angles = np.linspace(start_angle, extent_of_rotation, number_of_projections, endpoint=False) if direction=='CW': angles = -angles % 360 # Get radial positions if corrfile is not None: radii = np.loadtxt(corrfile) * scale_factor else: radius = get_header_value(headerdata, 'Radius', np.float32) * scale_factor radii = np.ones(len(angles))*radius shape_obj = (proj_dim1, proj_dim1, proj_dim2) object_meta = SPECTObjectMeta(dr,shape_obj) proj_meta = SPECTProjMeta((proj_dim1, proj_dim2), angles, radii) return object_meta, proj_meta
[docs]def get_projections(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()).unsqueeze(dim=0).to(pytomography.device) return projections
[docs]def get_scatter_from_TEW( headerfile_peak: str, headerfile_lower: str, headerfile_upper: str, ): """Obtains a triple energy window scatter estimate from corresponding photopeak, lower, and upper energy windows. Args: headerfile_peak: Headerfile corresponding to the photopeak headerfile_lower: Headerfile corresponding to the lower energy window headerfile_upper: Headerfile corresponding to the upper energy window Returns: torch.Tensor[1, Ltheta, Lr, Lz]: Estimated scatter from the triple energy window. """ # assumes all three energy windows have same metadata projectionss = [] window_widths = [] for headerfile in [headerfile_peak, headerfile_lower, headerfile_upper]: projections = get_projections(headerfile) with open(headerfile) as f: headerdata = f.readlines() headerdata = np.array(headerdata) lwr_window = get_header_value(headerdata, 'energy window lower level', np.float32) upr_window = get_header_value(headerdata, 'energy window upper level', np.float32) window_widths.append(upr_window - lwr_window) projectionss.append(projections) projections_scatter = compute_TEW(projectionss[1], projectionss[2], window_widths[1], window_widths[2], window_widths[0]) return projections_scatter
[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 combine_scatter_data_TEW( headerfiles_peak: Sequence[str], headerfiles_lower: Sequence[str], headerfiles_upper: Sequence[str], weights: Sequence[float] ): """Computes the triple energy window scatter estimate of the sequence of projection data weighted by `weights`. See `combine_projection_data` for more details. Args: headerfiles_peak (Sequence[str]): List of headerfiles corresponding to the photopeak headerfiles_lower (Sequence[str]): List of headerfiles corresponding to the lower scatter window headerfiles_upper (Sequence[str]): List of headerfiles corresponding to the upper scatter window weights (Sequence[float]): Amount by which to weight each set of projection data by. Returns: _type_: _description_ """ scatter = 0 for headerfile_peak, headerfile_lower, headerfile_upper, weight in zip(headerfiles_peak, headerfiles_lower, headerfiles_upper, weights): scatter_i = get_scatter_from_TEW(headerfile_peak, headerfile_lower, headerfile_upper) scatter+= weight * scatter_i return scatter
[docs]def get_attenuation_map(headerfile: str): """Opens attenuation data from SIMIND output Args: headerfile (str): Path to header file 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))[:,::-1,::-1] CT = torch.tensor(CT.copy()).unsqueeze(dim=0) return CT.to(pytomography.device)
[docs]def get_psfmeta_from_header(headerfile: str): """Obtains the SPECTPSFMeta data corresponding to a SIMIND simulation scan from the headerfile Args: headerfile (str): SIMIND headerfile. Returns: SPECTPSFMeta: SPECT PSF metadata required for PSF modeling in reconstruction. """ module_path = os.path.dirname(os.path.abspath(__file__)) with open(headerfile) as f: headerdata = f.readlines() headerdata = np.array(headerdata) hole_diameter = get_header_value(headerdata, 'Collimator hole diameter', np.float32) hole_length = get_header_value(headerdata, 'Collimator thickness', np.float32) energy_keV = get_header_value(headerdata, 'Photon Energy', np.float32) lead_attenuation = get_mu_from_spectrum_interp(os.path.join(module_path, '../../data/NIST_attenuation_data/lead.csv'), energy_keV) collimator_slope = hole_diameter/(hole_length - (2/lead_attenuation)) * 1/(2*np.sqrt(2*np.log(2))) collimator_intercept = hole_diameter * 1/(2*np.sqrt(2*np.log(2))) return SPECTPSFMeta((collimator_slope, collimator_intercept))