# MU Density¶

## Summary¶

A suite of functions to determine the MU Density for a range of formats

Warning

Although this is a useful tool in the toolbox for patient specific IMRT QA, in and of itself it is not a sufficient replacement for direct dosimetric measurement. This tool does not verify that the reported dose within the treatment planning system matches that delivered by the Linac.

Deficiencies in a plan’s agreement between the treatment planning system and the Linac due to deviations such as small field output factors or the MLC model will not be able to be picked up by this tool.

Furthermore, when the MU Density is calculated off of log files alone this uses leaf and jaw positions as reported by the Linac which may not agree with their position in reality due to issues such as a hardware or calibration fault.

## API¶

pymedphys.mudensity.calculate(mu, mlc, jaw, grid_resolution=None, max_leaf_gap=None, leaf_pair_widths=None, min_step_per_pixel=None)

Determine the MU Density.

Both jaw and mlc positions are defined in bipolar format for each control point. A negative value indicates travel over the isocentre. All positional arguments are defined at the isocentre projection with the units of mm.

Parameters: mu (numpy.ndarray) – 1-D array containing an MU value for each control point. mlc (numpy.ndarray) – 3-D array containing the MLC positions axis 0: control point axis 1: mlc pair axis 2: leaf bank jaw (numpy.ndarray) – 2-D array containing the jaw positions. axis 0: control point axis 1: diaphragm grid_resolution (float, optional) – The calc grid resolution. Defaults to 1 mm. max_leaf_gap (float, optional) – The maximum possible distance between opposing leaves. Defaults to 400 mm. leaf_pair_widths (tuple, optional) – The widths of each leaf pair in the MLC limiting device. The number of entries in the tuples defines the number of leaf pairs. Each entry itself defines that particular leaf pair width. Defaults to 80 leaf pairs each 5 mm wide. min_step_per_pixel (int, optional) – The minimum number of time steps used per pixel for each control point. Defaults to 10. mu_density – 2-D array containing the calculated mu density. axis 0: jaw direction axis 1: mlc direction numpy.ndarray

Examples

>>> from pymedphys._imports import numpy as np
>>> import pymedphys
>>>
>>> leaf_pair_widths = (5, 5, 5)
>>> max_leaf_gap = 10
>>> mu = np.array([0, 2, 5, 10])
>>> mlc = np.array([
...     [
...         [1, 1],
...         [2, 2],
...         [3, 3]
...     ],
...     [
...         [2, 2],
...         [3, 3],
...         [4, 4]
...     ],
...     [
...         [-2, 3],
...         [-2, 4],
...         [-2, 5]
...     ],
...     [
...         [0, 0],
...         [0, 0],
...         [0, 0]
...     ]
... ])
>>> jaw = np.array([
...     [7.5, 7.5],
...     [7.5, 7.5],
...     [-2, 7.5],
...     [0, 0]
... ])
>>>
>>> grid = pymedphys.mudensity.grid(
...    max_leaf_gap=max_leaf_gap, leaf_pair_widths=leaf_pair_widths)
>>> grid['mlc']
array([-5., -4., -3., -2., -1.,  0.,  1.,  2.,  3.,  4.,  5.])
>>>
>>> grid['jaw']
array([-8., -7., -6., -5., -4., -3., -2., -1.,  0.,  1.,  2.,  3.,  4.,
5.,  6.,  7.,  8.])
>>>
>>> mu_density = pymedphys.mudensity.calculate(
...    mu, mlc, jaw, max_leaf_gap=max_leaf_gap,
...    leaf_pair_widths=leaf_pair_widths)
>>> pymedphys.mudensity.display(grid, mu_density)
>>>
>>> np.round(mu_density, 1)
array([[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0.3, 1.9, 2.2, 1.9, 0.4, 0. , 0. , 0. ],
[0. , 0. , 0. , 0.4, 2.2, 2.5, 2.2, 0.6, 0. , 0. , 0. ],
[0. , 0. , 0. , 0.4, 2.4, 2.8, 2.5, 0.8, 0. , 0. , 0. ],
[0. , 0. , 0. , 0.4, 2.5, 3.1, 2.8, 1. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0.4, 2.5, 3.4, 3.1, 1.3, 0. , 0. , 0. ],
[0. , 0. , 0.4, 2.3, 3.2, 3.7, 3.7, 3.5, 1.6, 0. , 0. ],
[0. , 0. , 0.4, 2.3, 3.2, 3.8, 4. , 3.8, 1.9, 0.1, 0. ],
[0. , 0. , 0.4, 2.3, 3.2, 3.8, 4.3, 4.1, 2.3, 0.1, 0. ],
[0. , 0. , 0.4, 2.3, 3.2, 3.9, 5.2, 4.7, 2.6, 0.2, 0. ],
[0. , 0. , 0.4, 2.3, 3.2, 3.8, 5.4, 6.6, 3.8, 0.5, 0. ],
[0. , 0.3, 2.2, 3. , 3.5, 4. , 5.1, 7.5, 6.7, 3.9, 0.5],
[0. , 0.3, 2.2, 3. , 3.5, 4. , 4.7, 6.9, 6.7, 3.9, 0.5],
[0. , 0.3, 2.2, 3. , 3.5, 4. , 4.5, 6.3, 6.4, 3.9, 0.5],
[0. , 0.3, 2.2, 3. , 3.5, 4. , 4.5, 5.6, 5.7, 3.8, 0.5],
[0. , 0.3, 2.2, 3. , 3.5, 4. , 4.5, 5.1, 5.1, 3.3, 0.5],
[0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]])


MU Density from a Mosaiq record

>>> import pymedphys
>>>
>>> def mu_density_from_mosaiq(msq_server_name, field_id):
...     with pymedphys.mosaiq.connect(msq_server_name) as cursor:
...         delivery = pymedphys.Delivery.from_mosaiq(cursor, field_id)
...
...     grid = pymedphys.mudensity.grid()
...     mu_density = delivery.mudensity()
...     pymedphys.mudensity.display(grid, mu_density)
>>>
>>> mu_density_from_mosaiq('a_server_name', 11111)


MU Density from a logfile at a given filepath

>>> import pymedphys
>>>
>>> def mu_density_from_logfile(filepath):
...     delivery_data = Delivery.from_logfile(filepath)
...     mu_density = Delivery.mudensity()
...
...     grid = pymedphys.mudensity.grid()
...     pymedphys.mudensity.display(grid, mu_density)
>>>
>>> mu_density_from_logfile(r"a/path/goes/here")

pymedphys.mudensity.grid(max_leaf_gap=400, grid_resolution=1, leaf_pair_widths=(5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5))

Get the MU Density grid for plotting purposes.

Examples

pymedphys.mudensity.display(grid, mu_density, grid_resolution=None, cmap=None, vmin=None, vmax=None)

Prints a colour plot of the MU Density.

Examples