PyGRPM

The PyGRPM Medical Physics Library contains many utility features used within the Medical Physics Research Group at Université Laval.

This library also contains a multitude of subprojects, which all contain practical functionalities, such as creating DICOM SR, manipulating DICOM CT or even performing calculations with the TG43 formalism.

Note that the code is provided "as is" and should not be used in a clinical environment. In no event shall the authors be liable for any claims, damages or other liabilities.

License

PyGRPM is distributed as free software according to the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option), any later version (http://www.gnu.org/licenses/).

Citation

If you publish using PyGRPM, we kindly ask that you credit us. PyGRPM can be found on Zenodo : http://dx.doi.org/10.5281/zenodo.8250063.

Installation

pip install pygrpm

DICOM

Making SR (Structured Report)

The creation of DICOM SR (Structured Report) has been simplified thanks to the pygrpm.make_sr() and pygrpm.make_sr_from_text() functions. Note that the SRBuilder class could allow a more refined/controlled creation.

To create an SR from an arbitrary string, use:

from pygrpm.dicom.sr import make_sr_from_text

sr = make_sr_from_text('my text', ['ref_series/ct-1.dcm', 'ref_series/ct-2.dcm'])

sr  # Is a pydicom.FileDataset
sr.ContentSequence[0].TextValue  # Contains the text
sr.ReferencedInstanceSequence  # Contains reference values

You can also provide a list of strings, this will generate multiple TEXT values within the Content Sequence:

from pygrpm.dicom.sr import make_sr_from_text

sr = make_sr_from_text(['my first text','my second text'], ['ref_series/ct-1.dcm', 'ref_series/ct-2.dcm'])

sr  # Is a pydicom.FileDataset
sr.ContentSequence[0].TextValue  # Contains the first text
sr.ContentSequence[1].TextValue # Contains the second text
sr.ReferencedInstanceSequence  # Contains reference values

To use a custom content sequence (i.e: a specific structure):

from pygrpm.dicom.sr import make_sr

# The content sequence can be basically anything if it respects the DICOM standard.
# The user that want a specific structure is invited to read on SR in the DICOM standard.
content_sequence = {
    'ValueType': 'CONTAINER',
    'ConceptNameCodeSequence': {'CodeValue': 'DOC', 'CodeMeaning': 'Document', 'CodingSchemeDesignator': 'DCM'},
    'ContinuityOfContent': 'SEPARATE',
    'Value': [
        {
            'RelationshipType': 'HAS PROPERTIES',
            'ValueType': 'TEXT',
            'ConceptNameCodeSequence': {'CodeValue': '113012',
                                        'CodeMeaning': 'Key Object Description',
                                        'CodingSchemeDesignator': 'DCM'},
            'Value': 'Some text',
        },
        # You can specify more than one value in the list
        {
            'RelationshipType': 'HAS PROPERTIES',
            'ValueType': 'TEXT',
            'ConceptNameCodeSequence': {'CodeValue': '113012',
                                        'CodeMeaning': 'Key Object Description',
                                        'CodingSchemeDesignator': 'DCM'},
            'Value': 'Some more text',
        },
    ],
},

sr = make_sr(content_sequence, ['ref_series/ct-1.dcm', 'ref_series/ct-2.dcm'])

sr  # Is a pydicom.FileDataset
sr.ContentSequence  # Correspond to the given content sequence
sr.ReferencedInstanceSequence  # Contains reference values

Users who wish to have more information on the creation of SR are invited to read documentation concerning SR builder, SR dose content sequences and DICOM part 03 section 17.

TG43

Python package to calculate the TG43 formalism based on xarray and the data from ESTRO.

Available seeds

• SelectSeed
• MicroSelectronV2
• MBDCA
• Flexisource

Usage

from pygrpm.tg43 import Seed
import matplotlib.pyplot as plt

seed = Seed("MBDCA")
print(seed.gL)
print(seed.F)

# Display dosimetric grid
grid = seed.grid()
# Plot arbitrary 100th slice
plt.imshow(grid.data[:, :, 100])
plt.show()

# Plot mean dose vs radius with std
mean = seed.get_mean(grid)
std = seed.get_std(grid)
plt.errorbar(mean["r"].data, mean.data, std.data, fmt='o')
plt.show()

Index tracker

Submodule that allows scrolling through slices of 3-D images using the matplotlib backend.

import matplotlib.pyplot as plt
import numpy
from pygrpm.visualization.index_tracker import IndexTracker

tracker = IndexTracker(
    *plt.subplot(),
    numpy.random.rand(512, 512, 10),
    ...
)
tracker.ax.set_title('My 3-D random image')
tracker.show()

See this for more information

NISTParser

A simple class to extract information from certain NIST webpages. At the time of writing this covers atomic and electronic cross-sections, material attenuation, as well as material composition.

get_cross_sections

This method retrieves the desired cross-sections of an element at given energies on the NIST website in (barns/electron), barn=10^-24cm^2.

Simple use example:

import numpy as np
from pygrpm.material.nistparser import get_cross_sections

# Define the energies in KeV
# Numpy array is not mandatory, can be any sequence
energies = np.linspace(30, 200, 200)

# Prints the returned list
print(get_cross_sections("H", energies))

get_electronic_cross_section

See get_cross_section(), method use is identical minus the options argument

get_attenuations

Method to retrieve the attenuation of a material in cm^2/g at given energies on the NIST website.

• Note the option parameter which can specify the attenuation physics types
• Note the outopt parameter which can alter the returned information

Example is similar to get_cross_sections()

get_composition

This method is used to get and parse material composition from https://physics.nist.gov/cgi-bin/Star/compos.pl

Simple use example:

from pygrpm.material.nistparser import get_composition
from pygrpm.material.nistparser import NISTMaterials

# Prints the returned dictionary
print(get_composition(NISTMaterials.M3_WAX))

Note that get_composition expects the material to be of instance NISTMaterials

Acknowledgements

• This submodule makes use of the HTMLTablePasrer built by Josua Schmid, further information can be found in the pygrpm.nistparser.nistparser.py file header.
• This submodule is also dependent on the data provided by https://www.nist.gov/pml

Hounsfield conversion

A helper class meant to offer a quick and simple means to convert an HU array to density values based on a provided conversion table. Note that the conversion factors for HU to density are generally provided by the CT manufacturer. This class is currently only able to be read under csv format type.

Usage

Assuming the following sample data as ./curve.csv file

HU,Mass density [g/cm^3]
-1000,0.00121
-804,0.217
-505,0.508
-72,0.967
-32,0.99
7,1.018
44,1.061
52,1.071
254,1.159
4000,3.21

A call through the class can be made to rescale an arbitrary array of Hounsfield unit to density values.

import numpy as np
from pygrpm.ct_utils import ConvertHUToDensity

fithu = ConvertHUToDensity()
my_curve = fithu.load_curve_csv("./curve.csv")

# Note that setting plot to True generates a matplotlib plot of the curve fitting
data, fit_params, labels = fithu.fit_curve(my_curve, plot=True)
# Fit returns unused in this example

my_real_image = np.array([-980, -1000., -823., 1, 20, 700, 2900])
densities = fithu.apply_fit(my_real_image, my_curve)

print(densities)  # [0.02702014 0.00652871 0.18787786 1.0291972 1.03955733 1.41034076, 2.60993423]

TG263

Basic TG263 implementation. Use this module to filter structure names based on the TG263.

More information here