Gamma from DICOM

PyMedPhys has multiple ways to calculate Gamma. There are also a range of interfaces that can be used. Presented here is a simplified interface which receives as its input two DICOM file paths for the purpose of directly calculating Gamma from a pair of RT DICOM dose files.

import numpy as np
import matplotlib.pyplot as plt
!pip install pydicom pymedphys

import pydicom
import pymedphys
Requirement already satisfied: pydicom in /home/runner/work/pymedphys/pymedphys/.venv/lib/python3.8/site-packages (2.1.2)
Requirement already satisfied: pymedphys in /home/runner/work/pymedphys/pymedphys/.venv/lib/python3.8/site-packages (0.36.0.dev2)
Requirement already satisfied: typing-extensions in /home/runner/work/pymedphys/pymedphys/.venv/lib/python3.8/site-packages (from pymedphys) (3.7.4.3)
WARNING: You are using pip version 20.2.2; however, version 21.0.1 is available.
You should consider upgrading via the '/home/runner/work/pymedphys/pymedphys/.venv/bin/python -m pip install --upgrade pip' command.

Getting the demo DICOM files

Let’s download some demo files for the purpose of demonstrating gamma_dicom usage.

reference_filepath = pymedphys.data_path("original_dose_beam_4.dcm")
reference_filepath
PosixPath('/home/runner/.pymedphys/data/original_dose_beam_4.dcm')
evaluation_filepath = pymedphys.data_path("logfile_dose_beam_4.dcm")
evaluation_filepath
PosixPath('/home/runner/.pymedphys/data/logfile_dose_beam_4.dcm')
reference = pydicom.read_file(str(reference_filepath), force=True)
evaluation = pydicom.read_file(str(evaluation_filepath), force=True)
axes_reference, dose_reference = pymedphys.dicom.zyx_and_dose_from_dataset(reference)
axes_evaluation, dose_evaluation = pymedphys.dicom.zyx_and_dose_from_dataset(evaluation)

(z_ref, y_ref, x_ref) = axes_reference
(z_eval, y_eval, x_eval) = axes_evaluation

Importantly the shape of the coordinates are in the same order as the dose axis order

np.shape(z_ref)
(152,)
np.shape(y_ref)
(158,)
np.shape(x_ref)
(254,)
np.shape(dose_reference)
(152, 158, 254)

Calculate and display Gamma

gamma_options = {
    'dose_percent_threshold': 1,
    'distance_mm_threshold': 1,
    'lower_percent_dose_cutoff': 20,
    'interp_fraction': 10,  # Should be 10 or more for more accurate results
    'max_gamma': 2,
    'random_subset': None,
    'local_gamma': True,
    'ram_available': 2**29  # 1/2 GB
}
    
gamma = pymedphys.gamma(
    axes_reference, dose_reference, 
    axes_evaluation, dose_evaluation, 
    **gamma_options)
Calcing using local normalisation point for gamma
Global normalisation set to 1.9462822416083623
Global dose threshold set to [0.01946282] ([1]% of normalisation)
Distance threshold set to [1]
Lower dose cutoff set to 0.3892564483216725 (20% of normalisation)


Current distance: 0.00 mm | Number of reference points remaining: 106910 | Points tested per reference point: 1 | RAM split count: 1
Current distance: 0.10 mm | Number of reference points remaining: 106910 | Points tested per reference point: 23 | RAM split count: 1
Current distance: 0.20 mm | Number of reference points remaining: 74257 | Points tested per reference point: 67 | RAM split count: 2
Current distance: 0.30 mm | Number of reference points remaining: 16220 | Points tested per reference point: 135 | RAM split count: 1
Current distance: 0.40 mm | Number of reference points remaining: 2732 | Points tested per reference point: 227 | RAM split count: 1
Current distance: 0.50 mm | Number of reference points remaining: 760 | Points tested per reference point: 348 | RAM split count: 1
Current distance: 0.60 mm | Number of reference points remaining: 257 | Points tested per reference point: 485 | RAM split count: 1
Complete!
valid_gamma = gamma[~np.isnan(gamma)]

num_bins = (
    gamma_options['interp_fraction'] * gamma_options['max_gamma'])
bins = np.linspace(0, gamma_options['max_gamma'], num_bins + 1)

plt.hist(valid_gamma, bins, density=True)
plt.xlim([0, gamma_options['max_gamma']])

pass_ratio = np.sum(valid_gamma <= 1) / len(valid_gamma)

plt.title("Local Gamma (0.5%/0.5mm) | Percent Pass: {0:.2f} %".format(pass_ratio*100))
# plt.savefig('gamma_hist.png', dpi=300)
Text(0.5, 1.0, 'Local Gamma (0.5%/0.5mm) | Percent Pass: 100.00 %')
../../../_images/from-dicom_15_1.png

Plotting the Dose and the Gamma

max_ref_dose = np.max(dose_reference)

lower_dose_cutoff = gamma_options['lower_percent_dose_cutoff'] / 100 * max_ref_dose

relevant_slice = (
    np.max(dose_reference, axis=(1, 2)) > 
    lower_dose_cutoff)
slice_start = np.max([
        np.where(relevant_slice)[0][0], 
        0])
slice_end = np.min([
        np.where(relevant_slice)[0][-1], 
        len(z_ref)])
z_vals = z_ref[slice(slice_start, slice_end, 5)]

eval_slices = [
    dose_evaluation[np.where(z_i == z_eval)[0][0], :, :]
    for z_i in z_vals
]

ref_slices = [
    dose_reference[np.where(z_i == z_ref)[0][0], :, :]
    for z_i in z_vals
]

gamma_slices = [
    gamma[np.where(z_i == z_ref)[0][0], :, :]
    for z_i in z_vals
]

diffs = [
    eval_slice - ref_slice
    for eval_slice, ref_slice 
    in zip(eval_slices, ref_slices)
]

max_diff = np.max(np.abs(diffs))



for i, (eval_slice, ref_slice, diff, gamma_slice) in enumerate(zip(eval_slices, ref_slices, diffs, gamma_slices)):    
    fig, ax = plt.subplots(figsize=(13,10), nrows=2, ncols=2)
   
    c00 = ax[0,0].contourf(
        x_eval, y_eval, eval_slice, 100, 
        vmin=0, vmax=max_ref_dose)
    ax[0,0].set_title("Evaluation")
    fig.colorbar(c00, ax=ax[0,0], label='Dose (Gy)')
    ax[0,0].invert_yaxis()
    ax[0,0].set_xlabel('x (mm)')
    ax[0,0].set_ylabel('z (mm)')
    
    c01 = ax[0,1].contourf(
        x_ref, y_ref, ref_slice, 100, 
        vmin=0, vmax=max_ref_dose)
    ax[0,1].set_title("Reference")  
    fig.colorbar(c01, ax=ax[0,1], label='Dose (Gy)')
    ax[0,1].invert_yaxis()
    ax[0,1].set_xlabel('x (mm)')
    ax[0,1].set_ylabel('z (mm)')

    c10 = ax[1,0].contourf(
        x_ref, y_ref, diff, 100, 
        vmin=-max_diff, vmax=max_diff, cmap=plt.get_cmap('seismic'))
    ax[1,0].set_title("Dose difference")    
    fig.colorbar(c10, ax=ax[1,0], label='[Dose Eval] - [Dose Ref] (Gy)')
    ax[1,0].invert_yaxis()
    ax[1,0].set_xlabel('x (mm)')
    ax[1,0].set_ylabel('z (mm)')
    
    c11 = ax[1,1].contourf(
        x_ref, y_ref, gamma_slice, 100, 
        vmin=0, vmax=2, cmap=plt.get_cmap('coolwarm'))
    ax[1,1].set_title(
        f"Local Gamma ({gamma_options['dose_percent_threshold']} % / {gamma_options['distance_mm_threshold']} mm)")    
    fig.colorbar(c11, ax=ax[1,1], label='Gamma Value')
    ax[1,1].invert_yaxis()
    ax[1,1].set_xlabel('x (mm)')
    ax[1,1].set_ylabel('z (mm)')
    
    plt.show()
    print("\n")    
../../../_images/from-dicom_18_0.png

../../../_images/from-dicom_18_2.png

../../../_images/from-dicom_18_4.png

../../../_images/from-dicom_18_6.png

../../../_images/from-dicom_18_8.png

../../../_images/from-dicom_18_10.png

../../../_images/from-dicom_18_12.png

Comments