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/docs/checkouts/readthedocs.org/user_builds/pymedphys/envs/latest/lib/python3.8/site-packages (2.3.1)
Requirement already satisfied: pymedphys in /home/docs/checkouts/readthedocs.org/user_builds/pymedphys/envs/latest/lib/python3.8/site-packages (0.40.0.dev2)

Requirement already satisfied: setuptools in /home/docs/checkouts/readthedocs.org/user_builds/pymedphys/envs/latest/lib/python3.8/site-packages (from pymedphys) (67.8.0)
Requirement already satisfied: typing-extensions in /home/docs/checkouts/readthedocs.org/user_builds/pymedphys/envs/latest/lib/python3.8/site-packages (from pymedphys) (4.6.2)

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/docs/.pymedphys/data/original_dose_beam_4.dcm')

evaluation_filepath = pymedphys.data_path("logfile_dose_beam_4.dcm")
evaluation_filepath

PosixPath('/home/docs/.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)

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)
#if density is True, y value is probability density; otherwise, it is count in a bin
plt.xlim([0, gamma_options['max_gamma']])
plt.xlabel('gamma index')
plt.ylabel('probability density')
    
pass_ratio = np.sum(valid_gamma <= 1) / len(valid_gamma)

if gamma_options['local_gamma']:
    gamma_norm_condition = 'Local gamma'
else:
    gamma_norm_condition = 'Global gamma'

plt.title(f"Dose cut: {gamma_options['lower_percent_dose_cutoff']}% | {gamma_norm_condition} ({gamma_options['dose_percent_threshold']}%/{gamma_options['distance_mm_threshold']}mm) | Pass Rate(\u03B3<=1): {pass_ratio*100:.2f}% \n ref pts: {len(z_ref)*len(y_ref)*len(x_ref)} | valid \u03B3 pts: {len(valid_gamma)}")

# plt.savefig('gamma_hist.png', dpi=300)

Text(0.5, 1.0, 'Dose cut: 20% | Local gamma (1%/1mm) | Pass Rate(γ<=1): 100.00% \n ref pts: 6100064 | valid γ pts: 106910')

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)
   
    fig.suptitle('Slice Z_{0}'.format(slice_start+i*5), fontsize=12)
    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('y (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('y (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")    
    cbar = fig.colorbar(c10, ax=ax[1,0], label='[Dose Eval] - [Dose Ref] (Gy)')
    cbar.formatter.set_powerlimits((0, 0)) #use scientific notation
    ax[1,0].invert_yaxis()
    ax[1,0].set_xlabel('x (mm)')
    ax[1,0].set_ylabel('y (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"{gamma_norm_condition} ({gamma_options['dose_percent_threshold']} % / {gamma_options['distance_mm_threshold']} mm)")    
    fig.colorbar(c11, ax=ax[1,1], label='gamma index')
    ax[1,1].invert_yaxis()
    ax[1,1].set_xlabel('x (mm)')
    ax[1,1].set_ylabel('y (mm)')
    
    plt.show()
    print("\n")