In [1]:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import pylab
%matplotlib inline
from scipy.interpolate import interp1d
from IPython.display import Image
Below are the recorded measurements for the first batch of cutout factor measurements
The following cell is used to initialise the ionisation to dose conversion function. Data is extracted from table 20 within TRS398. R50 of the 12 MeV beam is $4.75~g/cm^2$
In [2]:
zOnR50 = np.concatenate((np.array([0.02]), np.arange(0.05,1.25,0.05)))
R50of45 = np.array([0.997,1,1.004,1.008,1.012,1.017,1.021,1.026,1.03,
1.035,1.04,1.045,1.051,1.056,1.062,1.067,1.073,1.08,
1.086,1.092,1.099,1.106,1.113,1.120,1.128])
R50of50 = np.array([0.991,0.994,0.998,1.002,1.006,1.011,1.016,1.02,1.025,
1.03,1.035,1.041,1.046,1.052,1.058,1.064,1.07,1.076,
1.083,1.09,1.097,1.104,1.112,1.119,1.128])
R50of47_5 = np.mean([R50of45,R50of50], axis=0)
stopRatio = interp1d(zOnR50 * 47.5,R50of47_5)
These measurements were done on Harry 2694, with a Markus chamber set to +300 V. The sensitivity was $1.398 \times 10^9$. All measurements were done at 100 SSD with a 12 MeV beam and a $10\times10$ cm applicator. Below are the readings recorded in chronological order.
In [3]:
def calc_display(**kwargs):
depth = np.array(kwargs['depth'])
ionisation = np.array(kwargs['ionisation'])
reference = kwargs['reference']
if len(ionisation) == 1:
factor = (
reference / ionisation *
(stopRatio(25) / stopRatio(depth[0]))
)
else:
stop_ratio_corrected = stopRatio(depth) * ionisation
plt.scatter(depth,stop_ratio_corrected)
plt.ylabel('Stopping power ratio corrected')
plt.xlabel('Depth (mm)')
plt.title('Relative dose measurements')
plt.show()
index_of_max = np.argmax(stop_ratio_corrected)
cutout_reading = ionisation[index_of_max]
factor = (
(reference / cutout_reading) *
(stopRatio(25) / stopRatio(depth[index_of_max]))
)
print(
"Cutout factor = %0.3f | %0.1f%%" %
(factor, (factor - 1) * 100)
)
return factor
In [4]:
data = dict()
In [13]:
# Standard insert
np.mean([1.033, 1.033])
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In [8]:
def new_reading(**kwargs):
data = kwargs['data']
key = kwargs['key']
ionisation = kwargs['ionisation']
depth = kwargs['depth']
data[key]['depth'].append(depth)
data[key]['ionisation'].append(np.mean(ionisation))
return data
In [9]:
key = 'concave_cutout'
data[key] = dict()
data[key]['depth'] = []
data[key]['ionisation'] = []
data[key]['reference'] = 1.033
data = new_reading(
key=key, data=data,
ionisation=[1.007, 1.006, 1.006],
depth=25
)
data = new_reading(
key=key, data=data,
ionisation=[1.009, 1.009],
depth=24
)
data = new_reading(
key=key, data=data,
ionisation=[1.011, 1.011],
depth=23
)
data = new_reading(
key=key, data=data,
ionisation=[1.013],
depth=22
)
data = new_reading(
key=key, data=data,
ionisation=[1.013],
depth=21
)
data[key]['factor'] = calc_display(**data[key])
In [10]:
key = 'concave_ellipse'
data[key] = dict()
data[key]['depth'] = []
data[key]['ionisation'] = []
data[key]['reference'] = 1.033
data = new_reading(
key=key, data=data,
ionisation=[1.001, 1.001],
depth=25
)
data = new_reading(
key=key, data=data,
ionisation=[1.004, 1.004],
depth=24
)
data = new_reading(
key=key, data=data,
ionisation=[1.008, 1.007, 1.007],
depth=23
)
data = new_reading(
key=key, data=data,
ionisation=[1.009, 1.009],
depth=22
)
data = new_reading(
key=key, data=data,
ionisation=[1.009],
depth=21
)
data[key]['factor'] = calc_display(**data[key])
In [12]:
# Standard insert
np.mean([1.033, 1.033])
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