Source code for pytomography.io.SPECT.helpers

"""This module contains helper function for inputting/outputting/interpretting SPECT/CT data in the SIMIND and DICOM file formats. A considerable amount of these functions have to do with creating attenuation maps from CT data files for attenuation correction in SPECT imaging.
"""
from __future__ import annotations
from typing import Sequence
import warnings
import numpy as np
import re
import os
from scipy.optimize import curve_fit, minimize
from scipy.signal import find_peaks
import pydicom
from functools import partial

# Set filepaths of the module
[docs]module_path = os.path.dirname(os.path.abspath(__file__))
[docs]DATADIR = os.path.join(module_path, '../../data/NIST_attenuation_data')
[docs]FILE_WATER = os.path.join(DATADIR, 'water.csv')
[docs]FILE_AIR = os.path.join(DATADIR, 'air.csv')
[docs]FILE_CBONE = os.path.join(DATADIR, 'bonecortical.csv')
[docs]def compute_TEW(projection_lower, projection_upper, width_lower, width_upper, width_peak): return (projection_lower/width_lower + projection_upper/width_upper)*width_peak / 2
[docs]def dual_sqrt_exponential( energy: float, c1: float, c2: float, d1: float, d2: float ) -> float: """Function used for curve fitting of linear attenuation coefficient vs. photon energy curves from NIST. It's given by the functional form :math:`f(x) = c_1e^{-d_1\sqrt{x}} + c_2e^{-d_2\sqrt{x}}`. It was chosen purely because it gave good fit results. Args: energy (float): Energy of photon c1 (float): Fit parameter 1 c2 (float): Fit parameter 2 d1 (float): Fit parameter 3 d2 (float): Fit parameter 4 Returns: float: _description_ """ return c1*np.exp(-d1*np.sqrt(energy)) + c2*np.exp(-d2*np.sqrt(energy))
[docs]def bilinear_transform( HU: float, a1: float, a2: float, b1: float, b2: float ) -> float: """Function used to convert between Hounsfield Units at an effective CT energy and linear attenuation coefficient at a given SPECT radionuclide energy. It consists of two distinct linear curves in regions :math:`HU<0` and :math:`HU \geq 0`. Args: HU (float): Hounsfield units at CT energy a1 (float): Fit parameter 1 a2 (float): Fit parameter 2 b1 (float): Fit parameter 3 b2 (float): Fit parameter 4 Returns: float: Linear attenuation coefficient at SPECT energy """ output = np.piecewise( HU, [HU < 0, HU >= 0], [lambda x: a1*x + b1, lambda x: a2*x + b2]) output[output<0] = 0 return output
[docs]def open_CT_file( files_CT: Sequence[str], return_max_slice_loc: bool = False ) -> np.array: """Given a list of seperate DICOM files, opens them up and stacks them together into a single CT image. Args: files_CT (Sequence[str]): List of DICOM files corresponding to a particular scan return_max_slice_loc (bool, optional): Whether or not to return the max slice location of all the CT files. Needed when applying affine transformation to align CT and SPECT images. Defaults to False. Returns: np.array: CT scan in units of Hounsfield Units at the effective CT energy. """ CT = [] slice_locs = [] for file in files_CT: ds = pydicom.read_file(file) CT.append(ds.pixel_array) slice_locs.append(float(ds.SliceLocation)) CT = np.transpose(np.array(CT)[np.argsort(slice_locs)], (2,1,0)).astype(np.float32) CT = ds.RescaleSlope * CT + ds.RescaleIntercept if return_max_slice_loc: return CT, np.max(np.abs(slice_locs)) else: return CT
[docs]def get_E_mu_data_from_datasheet(file: str) -> tuple[np.array, np.array]: """Return energy and linear attenuation data from NIST datafiles of mass attenuation coefficients between 50keV and 511keV. Args: file (str): Location of NIST data file. Corresponds to a particular element/material. Returns: tuple[np.array, np.array]: Energy and linear attenuation values. """ with open(file) as f: lines = f.readlines() rho = float(lines[0]) lines = lines[1:] for i in range(len(lines)): lines[i] = re.split(r'\s+', lines[i])[-4:-1] E, mu_rho, _ = np.array(lines).astype(float).T E*=1000 mu = mu_rho * rho idx = (E>50)*(E<550) return E[idx], mu[idx]
[docs]def get_mu_from_spectrum_interp( file: str, energy: float ) -> np.array: """Gets linear attenuation corresponding to a given energy in in the data from ``file``. Args: file (str): Filepath of the mu-energy data energy (float): Energy at which mu is computed Returns: np.array: Linear attenuation coefficient (in 1/cm) at the desired energies. """ Edata, mudata = get_E_mu_data_from_datasheet(file) p_f2_opt = curve_fit(dual_sqrt_exponential, Edata, mudata)[0] return dual_sqrt_exponential(energy, *p_f2_opt)
[docs]def get_HU_from_spectrum_interp( file: str, energy: float ) -> np.array: """Obtains the Hounsfield Units of some material at a given energy Args: file (str): Filepath of material energy (float): Energy at which HU is desired Returns: np.array: HU at the desired energies. """ mu_water = get_mu_from_spectrum_interp(FILE_WATER, energy) mu_air = get_mu_from_spectrum_interp(FILE_AIR, energy) mu_material = get_mu_from_spectrum_interp(file, energy) return (mu_material - mu_water)/(mu_water-mu_air) * 1000
[docs]def get_HU_mu_curve( files_CT: Sequence[str], CT_kvp: float, E_SPECT: float ) ->tuple[np.array, np.array]: """Gets Housnfield Unit vs. linear attenuation coefficient for air, water, and cortical bone data points Args: files_CT (Sequence[str]): Filepaths of all CT slices CT_kvp (float): Value of kVp for the CT scan E_SPECT (float): Photopeak energy of the SPECT scan Returns: tuple[np.array, np.array]: """ # try to get HU corresponding to cortical bone HU_cortical_bone = get_HU_corticalbone(files_CT) if HU_cortical_bone is not None: # compute effective CT energy from CBone HU E_CT = get_ECT_from_corticalbone_HU(HU_cortical_bone) print(f'Cortical Bone Peak: {HU_cortical_bone} HU') print(f'Effective CT Energy Determined: {E_CT} keV') else: # If can't get cortical bone peak, default to 75% KVP value warnings.warn("Could not find cortical bone peak: defaulting to 75% kVp value", category=Warning) E_CT = 0.75 * CT_kvp HU_CT = [] mu_SPECT = [] for file in [FILE_AIR, FILE_WATER, FILE_CBONE]: HU_CT.append(get_HU_from_spectrum_interp(file, E_CT)) mu_SPECT.append(get_mu_from_spectrum_interp(file, E_SPECT)) return np.array(HU_CT), np.array(mu_SPECT)
[docs]def HU_to_mu( HU: float, E: float, p_water_opt: Sequence[float], p_air_opt: Sequence[float] ): """Converts hounsfield units to linear attenuation coefficient Args: HU (float): Hounsfield Unit value E (float): Effective CT energy p_water_opt (Sequence[float]): Optimal fit parameters for mu vs. E data for water p_air_opt (Sequence[float]): Optimal fit parameters for mu vs. E data for air Returns: _type_: _description_ """ mu_water = dual_sqrt_exponential(E, *p_water_opt) mu_air = dual_sqrt_exponential(E, *p_air_opt) return 1/1000 * HU * (mu_water - mu_air) + mu_water
[docs]def get_HU_corticalbone( files_CT: Sequence[str] ) -> float | None: """Obtains the Hounsfield Unit corresponding to cortical bone from a CT scan. Args: files_CT (Sequence[str]): CT data files Returns: float | None: Hounsfield unit of bone. If not found, then returns ``None``. """ HU_from_CT_slices = open_CT_file(files_CT) x = HU_from_CT_slices.ravel() N, bin_edges = np.histogram(x[(x>1200)*(x<1600)], bins=10, density=True) bins = bin_edges[:-1] + np.diff(bin_edges)[0]/2 # Compute laplacian of histogram N_laplace = np.gradient(np.gradient(N)) peaks, _ = find_peaks(N_laplace, prominence=8e-5) if len(peaks)>0: return bins[peaks[-1]] else: return None
[docs]def get_ECT_from_corticalbone_HU(HU: float) -> float: """Finds the effective CT energy that gives a cortical bone Hounsfield Unit value corresponding to ``HU``. Args: HU (float): Hounsfield Unit of Cortical bone at effective CT energy Returns: float: Effective CT energy """ Edata, mudata_CB = get_E_mu_data_from_datasheet(FILE_CBONE) Edata, mudata_water = get_E_mu_data_from_datasheet(FILE_WATER) Edata, mudata_air = get_E_mu_data_from_datasheet(FILE_AIR) p_CB_opt = curve_fit(dual_sqrt_exponential, Edata, mudata_CB)[0] p_water_opt = curve_fit(dual_sqrt_exponential, Edata, mudata_water)[0] p_air_opt = curve_fit(dual_sqrt_exponential, Edata, mudata_air)[0] f = lambda E: 100*(dual_sqrt_exponential(E, *p_CB_opt) - HU_to_mu(HU, E, p_water_opt, p_air_opt))**2 return minimize(f, x0=(115), method='SLSQP').x[0]
[docs]def get_HU2mu_conversion( files_CT: Sequence[str], CT_kvp: float, E_SPECT: float ) -> function: """Obtains the HU to mu conversion function that converts CT data to the required linear attenuation value in units of 1/cm required for attenuation correction in SPECT/PET imaging. Args: files_CT (Sequence[str]): CT data files CT_kvp (float): kVp value for CT scan E_SPECT (float): Energy of photopeak in SPECT scan Returns: function: Conversion function from HU to mu. """ HU_CT, mu_SPECT = get_HU_mu_curve(files_CT, CT_kvp, E_SPECT) b1opt = b2opt = mu_SPECT[1] #water atten value a1opt = (mu_SPECT[1] - mu_SPECT[0]) / (HU_CT[1] - HU_CT[0]) a2opt = (mu_SPECT[2] - mu_SPECT[1]) / (HU_CT[2] - HU_CT[1]) return partial(bilinear_transform, a1=a1opt, a2=a2opt, b1=b1opt, b2=b2opt)