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linear_unmixing.py
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linear_unmixing.py
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# SPDX-FileCopyrightText: 2021 Division of Intelligent Medical Systems, DKFZ
# SPDX-FileCopyrightText: 2021 Janek Groehl
# SPDX-License-Identifier: MIT
import os
import numpy as np
from argparse import ArgumentParser
import simpa as sp
from simpa import Tags
from simpa.visualisation.matplotlib_data_visualisation import visualise_data
from typing import Union
# FIXME temporary workaround for newest Intel architectures
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
def run_linear_unmixing(spacing: float | int = 0.25, path_manager=None, visualise: bool = True):
"""
:param spacing: The simulation spacing between voxels
:param path_manager: the path manager to be used, typically sp.PathManager
:param visualise: If VISUALIZE is set to True, the reconstruction result will be plotted
:return: a run through of the example
"""
if path_manager is None:
path_manager = sp.PathManager()
# TODO: Please make sure that a valid path_config.env file is located in your home directory, or that you
# set global params characterizing the simulated volume
VOLUME_TRANSDUCER_DIM_IN_MM = 75
VOLUME_PLANAR_DIM_IN_MM = 20
VOLUME_HEIGHT_IN_MM = 25
RANDOM_SEED = 471
VOLUME_NAME = "LinearUnmixingExample_" + str(RANDOM_SEED)
# since we want to perform linear unmixing, the simulation pipeline should be execute for at least two wavelengths
WAVELENGTHS = [750, 800, 850]
def create_example_tissue():
"""
This is a very simple example script of how to create a tissue definition.
It contains a muscular background, an epidermis layer on top of the muscles
and two blood vessels.
"""
background_dictionary = sp.Settings()
background_dictionary[Tags.MOLECULE_COMPOSITION] = sp.TISSUE_LIBRARY.constant(1e-4, 1e-4, 0.9)
background_dictionary[Tags.STRUCTURE_TYPE] = Tags.BACKGROUND
muscle_dictionary = sp.Settings()
muscle_dictionary[Tags.PRIORITY] = 1
muscle_dictionary[Tags.STRUCTURE_START_MM] = [0, 0, 0]
muscle_dictionary[Tags.STRUCTURE_END_MM] = [0, 0, 100]
muscle_dictionary[Tags.MOLECULE_COMPOSITION] = sp.TISSUE_LIBRARY.muscle()
muscle_dictionary[Tags.CONSIDER_PARTIAL_VOLUME] = True
muscle_dictionary[Tags.ADHERE_TO_DEFORMATION] = True
muscle_dictionary[Tags.STRUCTURE_TYPE] = Tags.HORIZONTAL_LAYER_STRUCTURE
vessel_1_dictionary = sp.Settings()
vessel_1_dictionary[Tags.PRIORITY] = 3
vessel_1_dictionary[Tags.STRUCTURE_START_MM] = [VOLUME_TRANSDUCER_DIM_IN_MM/2,
10,
5]
vessel_1_dictionary[Tags.STRUCTURE_END_MM] = [VOLUME_TRANSDUCER_DIM_IN_MM/2,
12,
5]
vessel_1_dictionary[Tags.STRUCTURE_RADIUS_MM] = 3
vessel_1_dictionary[Tags.MOLECULE_COMPOSITION] = sp.TISSUE_LIBRARY.blood(oxygenation=0.99)
vessel_1_dictionary[Tags.CONSIDER_PARTIAL_VOLUME] = True
vessel_1_dictionary[Tags.STRUCTURE_TYPE] = Tags.CIRCULAR_TUBULAR_STRUCTURE
vessel_2_dictionary = sp.Settings()
vessel_2_dictionary[Tags.PRIORITY] = 3
vessel_2_dictionary[Tags.STRUCTURE_START_MM] = [VOLUME_TRANSDUCER_DIM_IN_MM/3,
10,
5]
vessel_2_dictionary[Tags.STRUCTURE_END_MM] = [VOLUME_TRANSDUCER_DIM_IN_MM/3,
12,
5]
vessel_2_dictionary[Tags.STRUCTURE_RADIUS_MM] = 2
vessel_2_dictionary[Tags.MOLECULE_COMPOSITION] = sp.TISSUE_LIBRARY.blood(oxygenation=0.75)
vessel_2_dictionary[Tags.CONSIDER_PARTIAL_VOLUME] = True
vessel_2_dictionary[Tags.STRUCTURE_TYPE] = Tags.CIRCULAR_TUBULAR_STRUCTURE
epidermis_dictionary = sp.Settings()
epidermis_dictionary[Tags.PRIORITY] = 8
epidermis_dictionary[Tags.STRUCTURE_START_MM] = [0, 0, 0]
epidermis_dictionary[Tags.STRUCTURE_END_MM] = [0, 0, 0.1]
epidermis_dictionary[Tags.MOLECULE_COMPOSITION] = sp.TISSUE_LIBRARY.epidermis()
epidermis_dictionary[Tags.CONSIDER_PARTIAL_VOLUME] = True
epidermis_dictionary[Tags.ADHERE_TO_DEFORMATION] = True
epidermis_dictionary[Tags.STRUCTURE_TYPE] = Tags.HORIZONTAL_LAYER_STRUCTURE
tissue_dict = sp.Settings()
tissue_dict[Tags.BACKGROUND] = background_dictionary
tissue_dict["muscle"] = muscle_dictionary
tissue_dict["epidermis"] = epidermis_dictionary
tissue_dict["vessel_1"] = vessel_1_dictionary
tissue_dict["vessel_2"] = vessel_2_dictionary
return tissue_dict
# Seed the numpy random configuration prior to creating the global_settings file in
# order to ensure that the same volume is generated with the same random seed every time.
np.random.seed(RANDOM_SEED)
# Initialize global settings and prepare for simulation pipeline including
# volume creation and optical forward simulation.
general_settings = {
# These parameters set the general properties of the simulated volume
Tags.RANDOM_SEED: RANDOM_SEED,
Tags.VOLUME_NAME: VOLUME_NAME,
Tags.SIMULATION_PATH: path_manager.get_hdf5_file_save_path(),
Tags.SPACING_MM: spacing,
Tags.DIM_VOLUME_Z_MM: VOLUME_HEIGHT_IN_MM,
Tags.DIM_VOLUME_X_MM: VOLUME_TRANSDUCER_DIM_IN_MM,
Tags.DIM_VOLUME_Y_MM: VOLUME_PLANAR_DIM_IN_MM,
Tags.WAVELENGTHS: WAVELENGTHS,
Tags.GPU: True,
Tags.DO_FILE_COMPRESSION: True
}
settings = sp.Settings(general_settings)
settings.set_volume_creation_settings({
Tags.SIMULATE_DEFORMED_LAYERS: True,
Tags.STRUCTURES: create_example_tissue()
})
settings.set_optical_settings({
Tags.OPTICAL_MODEL_NUMBER_PHOTONS: 1e7,
Tags.OPTICAL_MODEL_BINARY_PATH: path_manager.get_mcx_binary_path(),
Tags.OPTICAL_MODEL: Tags.OPTICAL_MODEL_MCX,
Tags.LASER_PULSE_ENERGY_IN_MILLIJOULE: 50
})
# Set component settings for linear unmixing.
# In this example we are only interested in the chromophore concentration of oxy- and deoxyhemoglobin and the
# resulting blood oxygen saturation. We want to perform the algorithm using all three wavelengths defined above.
# Please take a look at the component for more information.
settings["linear_unmixing"] = {
Tags.DATA_FIELD: Tags.DATA_FIELD_INITIAL_PRESSURE,
Tags.WAVELENGTHS: WAVELENGTHS,
Tags.LINEAR_UNMIXING_SPECTRA: sp.get_simpa_internal_absorption_spectra_by_names(
[Tags.SIMPA_NAMED_ABSORPTION_SPECTRUM_OXYHEMOGLOBIN, Tags.SIMPA_NAMED_ABSORPTION_SPECTRUM_DEOXYHEMOGLOBIN]
),
Tags.LINEAR_UNMIXING_COMPUTE_SO2: True,
Tags.LINEAR_UNMIXING_NON_NEGATIVE: True
}
# Get device for simulation
device = sp.MSOTAcuityEcho(device_position_mm=np.array([VOLUME_TRANSDUCER_DIM_IN_MM/2,
VOLUME_PLANAR_DIM_IN_MM/2,
0]))
device.update_settings_for_use_of_model_based_volume_creator(settings)
# Run simulation pipeline for all wavelengths in Tag.WAVELENGTHS
pipeline = [
sp.ModelBasedAdapter(settings),
sp.MCXAdapter(settings),
sp.FieldOfViewCropping(settings),
]
sp.simulate(pipeline, settings, device)
# Run linear unmixing component with above specified settings.
sp.LinearUnmixing(settings, "linear_unmixing").run()
# Load linear unmixing result (blood oxygen saturation) and reference absorption for first wavelength.
file_path = path_manager.get_hdf5_file_save_path() + "/" + VOLUME_NAME + ".hdf5"
lu_results = sp.load_data_field(file_path, Tags.LINEAR_UNMIXING_RESULT)
sO2 = lu_results["sO2"]
mua = sp.load_data_field(file_path, Tags.DATA_FIELD_ABSORPTION_PER_CM, wavelength=WAVELENGTHS[0])
p0 = sp.load_data_field(file_path, Tags.DATA_FIELD_INITIAL_PRESSURE, wavelength=WAVELENGTHS[0])
gt_oxy = sp.load_data_field(file_path, Tags.DATA_FIELD_OXYGENATION, wavelength=WAVELENGTHS[0])
# Visualize linear unmixing result
if visualise:
visualise_data(path_to_hdf5_file=path_manager.get_hdf5_file_save_path() + "/" + VOLUME_NAME + ".hdf5",
wavelength=WAVELENGTHS[0],
show_initial_pressure=True,
show_oxygenation=True,
show_linear_unmixing_sO2=True)
if __name__ == "__main__":
parser = ArgumentParser(description='Run the linear unmixing example')
parser.add_argument("--spacing", default=0.2, type=Union[float, int], help='the voxel spacing in mm')
parser.add_argument("--path_manager", default=None, help='the path manager, None uses sp.PathManager')
parser.add_argument("--visualise", default=True, type=bool, help='whether to visualise the result')
config = parser.parse_args()
run_linear_unmixing(spacing=config.spacing, path_manager=config.path_manager, visualise=config.visualise)