Planar Imaging

Overview

The planar imaging module analyzes phantom images taken with the kV or MV imager in 2D. The following phantoms are supported:

  • Leeds TOR 18
  • Standard Imaging QC-3
  • Standard Imaging QC-kV
  • Las Vegas
  • Doselab MC2 MV
  • Doselab MC2 kV
  • SNC kV
  • SNC MV
  • PTW EPID QC

Features:

  • Automatic phantom localization - Set up your phantom any way you like; automatic positioning, angle, and inversion correction mean you can set up how you like, nor will setup variations give you headache.
  • High and low contrast determination - Analyze both low and high contrast ROIs. Set thresholds as you see fit.

Typical module usage

The following snippets can be used with any of the phantoms in this module; they all have the same or very similar methods. We will use the LeedsTOR for the example, but plug in any phantom from this module.

Running the Demo

To run the demo of any phantom, create a script or start an interpreter session and input:

from pylinac import LeedsTOR  # or LasVegas, DoselabMC2kV, etc

LeedsTOR.run_demo()

A figure showing the phantom, low contrast plot, and RMTF will be generated:

(Source code, png, hires.png, pdf)

_images/planar_imaging-1.png

Typical Use

Import the class:

from pylinac import LeedsTOR  # or whatever phantom you like from the planar imaging module

The minimum needed to get going is to:

  • Load image – Load the planar image as you would any other class: by passing the path directly to the constructor:

    leeds = LeedsTOR('my/leeds.dcm')
    

    Alternatively, a URL can be passed:

    leeds = LeedsTOR.from_url('http://myserver.com/leeds')
    

    You may also use the demo image:

    leeds = LeedsTOR.from_demo_image()
    
  • Analyze the images – Analyze the image using the analyze() method. The low and high contrast thresholds can be specified:

    leeds.analyze(low_contrast_threshold=0.01, high_contrast_threshold=0.5)
    

    Additionally, you may specify the SSD of the phantom if it is not at iso (e.g. sitting on the panel):

    leeds.analyze(..., ssd=1400)
    
  • View the results – The results of analysis can be viewed with the plot_analyzed_image() method.

    leeds.plot_analyzed_image()
    

    (Source code, png, hires.png, pdf)

    _images/planar_imaging-2.png

    Note that each subimage can be turned on or off.

    # don't show the low contrast plot
    leeds.plot_analyzed_image(low_contrast=False)
    

    The figure can also be saved:

    leeds.save_analyzed_image('myprofile.png')
    

    A PDF report can also be generated:

    leeds.publish_pdf('leeds_october16.pdf')
    

Leeds TOR Phantom

The Leeds phantom is used to measure image quality metrics for the kV imager of a linac. It contains both high and low contrast ROIs.

Image Acquisition

You can acquire the images any way you like. Just ensure that the phantom is not touching a field edge. It is also recommended by the manufacturer to rotate the phantom to a non-cardinal angle so that pixel aliasing does not occur for the high-contrast line pairs.

Algorithm

Leeds phantom analysis is straightforward: find the phantom in the image, then sample ROIs at the appropriate locations.

The algorithm works like such:

Allowances

  • The images can be acquired at any SID.
  • The images can be acquired with any size kV imager.
  • The phantom can be at any distance.
  • The phantom can be at any angle.
  • The phantom can be flipped either way.

Restrictions

Warning

Analysis can fail or give unreliable results if any Restriction is violated.

  • The phantom must not be touching or close to any image edges.
  • The blades should be fully or mostly open to correctly invert the image. This may not result in a complete failure, but you may have to force-invert the analysis if this case isn’t true (i.e. myleeds.analyze(invert=True)).
  • The phantom should be centered near the CAX (<1-2cm).

Pre-Analysis

  • Determine phantom location – The Leeds phantom is found by performing a canny edge detection algorithm to the image. The thin structures found are sifted by finding appropriately-sized ROIs. This may include the outer phantom edge and the metal ring just inside. The average central position of the circular ROIs is set as the phantom center.
  • Determine phantom angle – To find the rotational angle of the phantom, a similar process is employed, but square-like features are searched for in the edge detection image. Because there are two square areas, the ROI with the highest attenuation (lead) is chosen. The angle between the phantom center and the lead square center is set as the angle.
  • Determine rotation direction – The phantom might be placed upside down. To keep analysis consistent, a circular profile is sampled at the radius of the low contrast ROIs starting at the lead square. Peaks are searched for on each semicircle. The side with the most peaks is the side with the higher contrast ROIs. Analysis is always done counter-clockwise. If the ROIs happen to be clockwise, the image is flipped left-right and angle/center inverted.

Analysis

  • Calculate low contrast – Because the phantom center and angle are known, the angles to the ROIs can also be known. For each contrast ROI, both it and a background ROI are sampled. From here, the contrast can be known: \(Contrast_{ROI} = \frac{ROI_{val} - ROI_{background}}{ROI_{val} + ROI_{background}}\).
  • Calculate high contrast – Again, because the phantom position and angle are known, offsets are applied to sample the high contrast line pair regions. For each sample, the relative MTF is calculated: \(MTF_{ROI} = \frac{ROI_{max} - ROI_{min}}{ROI_{max} + ROI_{min}}\).

Post-Analysis

  • Determine passing low and high contrast ROIs – For each low and high contrast region, the determined value is compared to the threshold. The plot colors correspond to the pass/fail status.

Troubleshooting

If you’re having trouble getting the Leeds phantom analysis to work, first check out the Troubleshooting section. If the issue is not listed there, then it may be one of the issues below.

The most common reason for failing is having the phantom near an image edge. The resulting error is usually that the phantom angle cannot be determined. For example, this image would throw an error:

_images/bad_leeds.jpg

The below image also fails. Technically, the phantom is in the image, but the top blade skews the pixel values such that the phantom edge cannot be properly found at the top. This fails to identify the true phantom edge, causing the angle to also not be found:

_images/bad_leeds2.jpg

Another problem is that the image may have a non-uniform background. This can cause pylinac’s automatic inversion correction to incorrectly invert the image. For example, this image falsely inverts:

_images/leeds_uneven.jpg

When analyzed, the angle is 180 degrees opposite the lead square, causing the ROIs to be flipped 180 degrees. To correct this problem, pass invert=True to analyze(). This will force pylinac to invert the image the opposite way and correctly identify the lead square.

Another common problem is an offset analysis, as shown below:

_images/leeds_offset_inverted.png

This is caused by a wrong inversion.

Note

If the image flash is dark, then the image inversion is very likely wrong.

Again, pass invert=True to the analyze method. This is the same image but with invert=True:

_images/leeds_offset_corrected.png

Standard Imaging QC-3 Phantom

The Standard Imaging phantom is an MV imaging quality assurance phantom and has high and low contrast regions, just as the Leeds phantom, but with different geometric configurations.

Image Acquisition

The Standard Imaging phantom has a specific setup as recommended by the manufacturer. The phantom should be angled 45 degrees, with the “1” pointed toward the gantry stand and centered along the CAX. For best results when using pylinac, open the jaws to fully cover the EPID, or at least give 1-2cm flash around the phantom edges.

Algorithm

The algorithm works like such:

Allowances

  • The images can be acquired at any SID.
  • The images can be acquired with any EPID.
  • The images can be acquired with the phantom at any SSD.

Restrictions

Warning

Analysis can fail or give unreliable results if any Restriction is violated.

  • The phantom must be at 45 degrees.
  • The phantom must not be touching any image edges.
  • The phantom should have the “1” pointing toward the gantry stand.
  • The phantom should be centered near the CAX (<1-2cm).

Pre-Analysis

  • Determine phantom location – A canny edge search is performed on the image. Connected edges that are semi-round and angled are thought to possibly be the phantom. Of the ROIs, the one with the longest axis is said to be the phantom edge. The center of the bounding box of the ROI is set as the phantom center.
  • Determine phantom radius and angle – The major axis length of the ROI determined above serves as the phantom radius. The orientation of the edge ROI serves as the phantom angle.

Analysis

  • Calculate low contrast – Because the phantom center and angle are known, the angles to the ROIs can also be known. For each contrast ROI, both it and a background ROI are sampled. From here, the contrast can be known: \(Contrast_{ROI} = \frac{ROI_{val} - ROI_{background}}{ROI_{val} + ROI_{background}}\).
  • Calculate high contrast – Again, because the phantom position and angle are known, offsets are applied to sample the high contrast line pair regions. For each sample, the relative MTF is calculated: \(MTF_{ROI} = \frac{ROI_{max} - ROI_{min}}{ROI_{max} + ROI_{min}}\).

Post-Analysis

  • Determine passing low and high contrast ROIs – For each low and high contrast region, the determined value is compared to the threshold. The plot colors correspond to the pass/fail status.

Troubleshooting

If you’re having issues with the StandardImaging class, make sure you have correctly positioned the phantom as per the manufacturer’s instructions (also see Image Acquisition). One issue that may arise is incorrect inversion. If the jaws are closed tightly around the phantom, the automatic inversion correction may falsely invert the image, just as for the Leeds phantom. If you have an image that looks inverted or just plain weird, add invert=True to analyze(). If this doesn’t help, reshoot the phantom with the jaws open.

Las Vegas Phantom

The Las Vegas phantom is for MV image quality testing and includes low contrast regions of varying contrast and size.

Image Acquisition

The Las Vegas phantom has a recommended position as stated on the phantom. Pylinac will however account for angles, shifts, and inversions. Best practices for the Las Vegas phantom:

  • Keep the phantom from a couch edge or any rails.

Algorithm

The algorithm works like such:

Allowances

  • The images can be acquired at any SID.
  • The images can be acquired with any EPID.

Restrictions

Warning

Analysis can fail or give unreliable results if any Restriction is violated.

  • The phantom must not be touching any image edges.
  • The phantom should be at a cardinal angle (0, 90, 180, or 270 degrees) relative to the EPID.
  • The phantom should be centered near the CAX (<1-2cm).

Pre-Analysis

  • Determine phantom location – A canny edge search is performed on the image. Connected edges that are semi-round and angled are thought to possibly be the phantom. Of the ROIs, the one with the longest axis is said to be the phantom edge. The center of the bounding box of the ROI is set as the phantom center.
  • Determine phantom radius and angle – The major axis length of the ROI determined above serves as the phantom radius. The orientation of the edge ROI serves as the phantom angle.

Analysis

  • Calculate low contrast – Because the phantom center and angle are known, the angles to the ROIs can also be known. For each contrast ROI, both it and a background ROI are sampled. From here, the contrast can be known: \(Contrast_{ROI} = \frac{ROI_{val} - ROI_{background}}{ROI_{val} + ROI_{background}}\).

Post-Analysis

  • Determine passing low and high contrast ROIs – For each low and high contrast region, the determined value is compared to the threshold. The plot colors correspond to the pass/fail status.

Doselab MC2 MV & kV

The Doselab MC2 phantom is for both kV & MV image quality testing and includes low and high contrast regions of varying contrast. There are two high contrast sections, one intended for kV and one for MV.

Image Acquisition

The Doselab phantom has a recommended position as stated on the phantom. Pylinac will however account for shifts and inversions. Best practices for the Doselab phantom:

  • Keep the phantom from a couch edge or any rails.
  • Center the phantom along the CAX.

Algorithm

The algorithm works like such:

Allowances

  • The images can be acquired at any SID.
  • The images can be acquired with any EPID.

Restrictions

Warning

Analysis can fail or give unreliable results if any Restriction is violated.

  • The phantom must not be touching any image edges.
  • The phantom should be at 45 degrees relative to the EPID.
  • The phantom should be centered near the CAX (<1-2cm).

Creating a custom phantom

In the event you would like to analyze a phantom that pylinac does not analyze out of the box, the pylinac planar imaging module structure allows for generating new phantom analysis types quickly and easily. The benefit of this design is that with a few simple definitions you inherit a strong base of methods (e.g. plotting and PDF reports come for free).

Creating a new class involves a few different steps but can be done in a few minutes. The following is a guide for custom phantoms:

  1. Subclass the ImagePhantomBase class:

    from pylinac.planar_imaging import ImagePhantomBase
    
    class CustomPhantom(ImagePhantomBase):
        pass
    
  2. Define the common_name. This is the name shown in plots and PDF reports.

    class CustomPhantom(ImagePhantomBase):
        common_name = 'Custom Phantom v2.0'
    
  3. If the phantom has a high-contrast measurement object, define the ROI locations.

    class CustomPhantom(ImagePhantomBase):
        ...
        high_contrast_roi_settings = {
            'roi 1': {'distance from center': 0.5, 'angle': 30, 'roi radius': 0.05, 'lp/mm': 0.2},
            ...  # add as many ROIs as are needed
        }
    

    Note

    The exact values of your ROIs will need to be empirically determined. This usually involves an iterative process of adjusting the values until the values are satisfactory based on the ROI sample alignment to the actual ROIs.

  4. If the phantom has a low-contrast measurement object, define the sample ROI and background ROI locations.

    class CustomPhantom(ImagePhantomBase):
        ...
        low_contrast_roi_settings = {
            'roi 1': {'distance from center': 0.5, 'angle': 30, 'roi radius': 0.05},  # no lp/mm key
            ...  # add as many ROIs as are needed
        }
        low_contrast_background_roi_settings = {
            'roi 1': {'distance from center': 0.3, 'angle': -45, 'roi radius': 0.02},
            ...  # add as many ROIs as are needed
        }
    

    Note

    The exact values of your ROIs will need to be empirically determined. This usually involves an iterative process of adjusting the values until the values are satisfactory based on the ROI sample alignment to the actual ROIs.

  5. Set the “detection conditions”, which is the list of rules that must be true to properly detect the phantom ROI. E.g. the phantom should be near the center of the image. Detection conditions must always have a specific signature as shown below:

    def my_special_detection_condition(region: RegionProperties, instance: object, rtol: float) -> bool:
        # region is a scikit regionprop (https://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.regionprops)
        # instance == self of the phantom
        # rtol is relative tolerance of agreement. Don't have to use this.
        do_stuff  # e.g. is the region size and position correct?
        return bool(result)  # must always return a boolean
    
    class CustomPhantom(ImagePhantomBase):
        detection_conditions = [my_special_detection_condition,]  # list of conditions; add as many as you want.
    
  6. Optionally, add a phantom outline object. This helps visualize the algorithm’s determination of the size, center, and angle. If no object is defined, then no outline will be shown. This step is optional.

    class CustomPhantom(ImagePhantomBase):
        ...
        phantom_outline_object = {'Circle': {'radius ratio': 0.5}}  # to create a circular outline
        # or...
        phantom_outline_object = {'Rectangle': {'width ratio': 0.5, 'height ratio': 0.3}}  # to create a rectangular outline
    

At this point you could technically call it done. You would need to always override the angle, center, and size values in the analyze method however. To automate this part you will need to fill in the associated logic. You can use whatever method you like. What I have found most useful is to use an edge detection algorithm and find the outline of the phantom.

class CustomPhantom(ImagePhantomBase):
    ...

    def _phantom_center_calc(self) -> Point:
        # do stuff in here to determine the center point location.
        # don't forget to return as a Point item (pylinac.core.geometry.Point).

    def _phantom_radius_calc(self) -> float:
        # do stuff in here to return a float that represents the phantom radius value.
        # This value does not have to relate to a physical measure. It simply defines a value that the ROIs scale by.

    def _phantom_angle_calc(self) -> float:
        # do stuff in here to return a float that represents the angle of the phantom.
        # Again, this value does not have to correspond to reality; it simply offsets the ROIs.
        # You may also return a constant if you like for any of these.

Congratulations! You now have a fully-functioning custom phantom. By using the base class and the predefined attributes and methods, the plotting and PDF report functionality comes for free.

Usage tips, tweaks, & troubleshooting

Set the SSD of your phantom

If your phantom is at a non-standard distance (!= 1000mm), e.g. sitting on the EPID panel, you can specify its distance via the ssd parameter.

Warning

The ssd should be in mm, not cm. Pylinac is moving toward consistent units on everything and it will be mm for distance.

from pylinac import StandardImagingQC3

qc = StandardImagingQC3(...)
qc.analyze(..., ssd=1500)  # distance to the phantom in mm.

Adjust an ROI on an existing phantom

To adjust an ROI, override the relevant attribute or create a subclass. E.g. to move the 2nd ROI of the high-contrast ROI set of the QC-3 phantom:

from pylinac import StandardImagingQC3

StandardImagingQC3.high_contrast_roi_settings['roi 1']['distance from center'] = 0.05  # overrides that one setting
qc3 = StandardImagingQC3(...)

# or

class TweakedStandardImagingQC3(StandardImagingQC3):
    high_contrast_roi_settings = {
        'roi 1': ...
    }  # note that you must replace ALL the values

qc3 = TweakedStandardImagingQC3(...)

Calculate a specific MTF

To calculate a specific MTF value, i.e. the frequency at a given MTF%:

dl = DoselabMC2kV(...)
dl.analyze(...)
print(dl.mtf.relative_resolution_at(x=50))  # 50% rMTF

Get/View the contrast of a low-contrast ROI

leeds = LeedsTOR(...)
leeds.analyze(...)
print(leeds.low_contrast_rois[1].contrast)  # get the 2nd ROI contrast value

Loosen the ROI finding conditions

If for some reason you have a need to loosen the existing phantom-finding algorithm conditions you can do so fairly easily by overloading the current tooling:

from pylinac.planar_imaging import is_right_size, is_centered, LeedsTOR

def is_right_size_loose(region, instance, rtol=0.3):  # rtol default is 0.1
    return is_right_size(region, instance, rtol)

# set the new condition for whatever
LeedsTOR.detection_conditions = [is_right_size_loose, is_centered]
# proceed as normal
myleeds = LeedsTOR(...)

Wrong phantom angle

It may sometimes be that the angle of the phantom appears incorrect, or the results appear incorrect. E.g. here is a QC-3 phantom:

_images/bad_qc3.png

The ROIs appear correct, the but the contrast and MTF do not monotonically decrease, indicating a problem. In this case, it is because the image acquisition rules were not followed. For the QC-3, the “1” should always point toward the gantry, as per the manual. When oriented this way, the results will be correct.

API Documentation

class pylinac.planar_imaging.LeedsTOR(filepath: Union[str, BinaryIO], normalize: bool = True)[source]

Bases: pylinac.planar_imaging.ImagePhantomBase

Parameters:
  • filepath (str) – Path to the image file.
  • normalize (bool) – Whether to “ground” and normalize the image. This can affect contrast measurements, but for backwards compatibility this is True. You may want to set this to False if trying to compare with other software.
static run_demo() → None[source]

Run the Leeds TOR phantom analysis demonstration.

analyze(low_contrast_threshold: float = 0.05, high_contrast_threshold: float = 0.5, invert: bool = False, angle_override: Optional[float] = None, center_override: Optional[tuple] = None, size_override: Optional[float] = None, ssd: float = 1000, low_contrast_method: pylinac.core.roi.Contrast = <Contrast.MICHELSON: 'Michelson'>, visibility_threshold: float = 100) → None

Analyze the phantom using the provided thresholds and settings.

Parameters:
  • low_contrast_threshold (float) – This is the contrast threshold value which defines any low-contrast ROI as passing or failing.
  • high_contrast_threshold (float) – This is the contrast threshold value which defines any high-contrast ROI as passing or failing.
  • invert (bool) – Whether to force an inversion of the image. This is useful if pylinac’s automatic inversion algorithm fails to properly invert the image.
  • angle_override (None, float) –

    A manual override of the angle of the phantom. If None, pylinac will automatically determine the angle. If a value is passed, this value will override the automatic detection.

    Note

    0 is pointing from the center toward the right and positive values go counterclockwise.

  • center_override (None, 2-element tuple) – A manual override of the center point of the phantom. If None, pylinac will automatically determine the center. If a value is passed, this value will override the automatic detection. Format is (x, y)/(col, row).
  • size_override (None, float) –

    A manual override of the relative size of the phantom. This size value is used to scale the positions of the ROIs from the center. If None, pylinac will automatically determine the size. If a value is passed, this value will override the automatic sizing.

    Note

    This value is not necessarily the physical size of the phantom. It is an arbitrary value.

  • ssd – The SSD of the phantom itself in mm.
  • low_contrast_method – The equation to use for calculating low contrast.
  • visibility_threshold – The threshold for whether an ROI is “seen”.
classmethod from_demo_image()

Instantiate and load the demo image.

classmethod from_url(url: str)
Parameters:url (str) – The URL to the image.
phantom_bbox_size_px

The phantom bounding box size in pixels^2.

phantom_ski_region

The skimage region of the phantom outline.

plot_analyzed_image(image: bool = True, low_contrast: bool = True, high_contrast: bool = True, show: bool = True)

Plot the analyzed image.

Parameters:
  • image (bool) – Show the image.
  • low_contrast (bool) – Show the low contrast values plot.
  • high_contrast (bool) – Show the high contrast values plot.
  • show (bool) – Whether to actually show the image when called.
publish_pdf(filename: str, notes: str = None, open_file: bool = False, metadata: Optional[dict] = None)

Publish (print) a PDF containing the analysis, images, and quantitative results.

Parameters:
  • filename ((str, file-like object}) – The file to write the results to.
  • notes (str, list of strings) – Text; if str, prints single line. If list of strings, each list item is printed on its own line.
  • open_file (bool) – Whether to open the file using the default program after creation.
  • metadata (dict) – Extra data to be passed and shown in the PDF. The key and value will be shown with a colon. E.g. passing {‘Author’: ‘James’, ‘Unit’: ‘TrueBeam’} would result in text in the PDF like: ————– Author: James Unit: TrueBeam ————–
results() → str

Return the results of the analysis.

save_analyzed_image(filename: str, **kwargs)

Save the analyzed image to a file.

Parameters:
  • filename (str) – The location and filename to save to.
  • kwargs – Keyword arguments are passed to plt.savefig().
class pylinac.planar_imaging.StandardImagingQC3(filepath: Union[str, BinaryIO], normalize: bool = True)[source]

Bases: pylinac.planar_imaging.ImagePhantomBase

Parameters:
  • filepath (str) – Path to the image file.
  • normalize (bool) – Whether to “ground” and normalize the image. This can affect contrast measurements, but for backwards compatibility this is True. You may want to set this to False if trying to compare with other software.
classmethod from_demo_image()[source]

Instantiate and load the demo image.

static run_demo() → None[source]

Run the Standard Imaging QC-3 phantom analysis demonstration.

analyze(low_contrast_threshold: float = 0.05, high_contrast_threshold: float = 0.5, invert: bool = False, angle_override: Optional[float] = None, center_override: Optional[tuple] = None, size_override: Optional[float] = None, ssd: float = 1000, low_contrast_method: pylinac.core.roi.Contrast = <Contrast.MICHELSON: 'Michelson'>, visibility_threshold: float = 100) → None

Analyze the phantom using the provided thresholds and settings.

Parameters:
  • low_contrast_threshold (float) – This is the contrast threshold value which defines any low-contrast ROI as passing or failing.
  • high_contrast_threshold (float) – This is the contrast threshold value which defines any high-contrast ROI as passing or failing.
  • invert (bool) – Whether to force an inversion of the image. This is useful if pylinac’s automatic inversion algorithm fails to properly invert the image.
  • angle_override (None, float) –

    A manual override of the angle of the phantom. If None, pylinac will automatically determine the angle. If a value is passed, this value will override the automatic detection.

    Note

    0 is pointing from the center toward the right and positive values go counterclockwise.

  • center_override (None, 2-element tuple) – A manual override of the center point of the phantom. If None, pylinac will automatically determine the center. If a value is passed, this value will override the automatic detection. Format is (x, y)/(col, row).
  • size_override (None, float) –

    A manual override of the relative size of the phantom. This size value is used to scale the positions of the ROIs from the center. If None, pylinac will automatically determine the size. If a value is passed, this value will override the automatic sizing.

    Note

    This value is not necessarily the physical size of the phantom. It is an arbitrary value.

  • ssd – The SSD of the phantom itself in mm.
  • low_contrast_method – The equation to use for calculating low contrast.
  • visibility_threshold – The threshold for whether an ROI is “seen”.
classmethod from_url(url: str)
Parameters:url (str) – The URL to the image.
phantom_bbox_size_px

The phantom bounding box size in pixels^2.

phantom_ski_region

The skimage region of the phantom outline.

plot_analyzed_image(image: bool = True, low_contrast: bool = True, high_contrast: bool = True, show: bool = True)

Plot the analyzed image.

Parameters:
  • image (bool) – Show the image.
  • low_contrast (bool) – Show the low contrast values plot.
  • high_contrast (bool) – Show the high contrast values plot.
  • show (bool) – Whether to actually show the image when called.
publish_pdf(filename: str, notes: str = None, open_file: bool = False, metadata: Optional[dict] = None)

Publish (print) a PDF containing the analysis, images, and quantitative results.

Parameters:
  • filename ((str, file-like object}) – The file to write the results to.
  • notes (str, list of strings) – Text; if str, prints single line. If list of strings, each list item is printed on its own line.
  • open_file (bool) – Whether to open the file using the default program after creation.
  • metadata (dict) – Extra data to be passed and shown in the PDF. The key and value will be shown with a colon. E.g. passing {‘Author’: ‘James’, ‘Unit’: ‘TrueBeam’} would result in text in the PDF like: ————– Author: James Unit: TrueBeam ————–
results() → str

Return the results of the analysis.

save_analyzed_image(filename: str, **kwargs)

Save the analyzed image to a file.

Parameters:
  • filename (str) – The location and filename to save to.
  • kwargs – Keyword arguments are passed to plt.savefig().
class pylinac.planar_imaging.StandardImagingQCkV(filepath: Union[str, BinaryIO], normalize: bool = True)[source]

Bases: pylinac.planar_imaging.StandardImagingQC3

Parameters:
  • filepath (str) – Path to the image file.
  • normalize (bool) – Whether to “ground” and normalize the image. This can affect contrast measurements, but for backwards compatibility this is True. You may want to set this to False if trying to compare with other software.
static run_demo() → None[source]

Run the Standard Imaging QC-3 phantom analysis demonstration.

analyze(low_contrast_threshold: float = 0.05, high_contrast_threshold: float = 0.5, invert: bool = False, angle_override: Optional[float] = None, center_override: Optional[tuple] = None, size_override: Optional[float] = None, ssd: float = 1000, low_contrast_method: pylinac.core.roi.Contrast = <Contrast.MICHELSON: 'Michelson'>, visibility_threshold: float = 100) → None

Analyze the phantom using the provided thresholds and settings.

Parameters:
  • low_contrast_threshold (float) – This is the contrast threshold value which defines any low-contrast ROI as passing or failing.
  • high_contrast_threshold (float) – This is the contrast threshold value which defines any high-contrast ROI as passing or failing.
  • invert (bool) – Whether to force an inversion of the image. This is useful if pylinac’s automatic inversion algorithm fails to properly invert the image.
  • angle_override (None, float) –

    A manual override of the angle of the phantom. If None, pylinac will automatically determine the angle. If a value is passed, this value will override the automatic detection.

    Note

    0 is pointing from the center toward the right and positive values go counterclockwise.

  • center_override (None, 2-element tuple) – A manual override of the center point of the phantom. If None, pylinac will automatically determine the center. If a value is passed, this value will override the automatic detection. Format is (x, y)/(col, row).
  • size_override (None, float) –

    A manual override of the relative size of the phantom. This size value is used to scale the positions of the ROIs from the center. If None, pylinac will automatically determine the size. If a value is passed, this value will override the automatic sizing.

    Note

    This value is not necessarily the physical size of the phantom. It is an arbitrary value.

  • ssd – The SSD of the phantom itself in mm.
  • low_contrast_method – The equation to use for calculating low contrast.
  • visibility_threshold – The threshold for whether an ROI is “seen”.
classmethod from_demo_image()

Instantiate and load the demo image.

classmethod from_url(url: str)
Parameters:url (str) – The URL to the image.
phantom_bbox_size_px

The phantom bounding box size in pixels^2.

phantom_ski_region

The skimage region of the phantom outline.

plot_analyzed_image(image: bool = True, low_contrast: bool = True, high_contrast: bool = True, show: bool = True)

Plot the analyzed image.

Parameters:
  • image (bool) – Show the image.
  • low_contrast (bool) – Show the low contrast values plot.
  • high_contrast (bool) – Show the high contrast values plot.
  • show (bool) – Whether to actually show the image when called.
publish_pdf(filename: str, notes: str = None, open_file: bool = False, metadata: Optional[dict] = None)

Publish (print) a PDF containing the analysis, images, and quantitative results.

Parameters:
  • filename ((str, file-like object}) – The file to write the results to.
  • notes (str, list of strings) – Text; if str, prints single line. If list of strings, each list item is printed on its own line.
  • open_file (bool) – Whether to open the file using the default program after creation.
  • metadata (dict) – Extra data to be passed and shown in the PDF. The key and value will be shown with a colon. E.g. passing {‘Author’: ‘James’, ‘Unit’: ‘TrueBeam’} would result in text in the PDF like: ————– Author: James Unit: TrueBeam ————–
results() → str

Return the results of the analysis.

save_analyzed_image(filename: str, **kwargs)

Save the analyzed image to a file.

Parameters:
  • filename (str) – The location and filename to save to.
  • kwargs – Keyword arguments are passed to plt.savefig().
class pylinac.planar_imaging.LasVegas(filepath: Union[str, BinaryIO], normalize: bool = True)[source]

Bases: pylinac.planar_imaging.ImagePhantomBase

Parameters:
  • filepath (str) – Path to the image file.
  • normalize (bool) – Whether to “ground” and normalize the image. This can affect contrast measurements, but for backwards compatibility this is True. You may want to set this to False if trying to compare with other software.
static run_demo()[source]

Run the Las Vegas phantom analysis demonstration.

analyze(low_contrast_threshold: float = 0.05, high_contrast_threshold: float = 0.5, invert: bool = False, angle_override: Optional[float] = None, center_override: Optional[tuple] = None, size_override: Optional[float] = None, ssd: float = 1000, low_contrast_method: pylinac.core.roi.Contrast = <Contrast.MICHELSON: 'Michelson'>, visibility_threshold: float = 100) → None

Analyze the phantom using the provided thresholds and settings.

Parameters:
  • low_contrast_threshold (float) – This is the contrast threshold value which defines any low-contrast ROI as passing or failing.
  • high_contrast_threshold (float) – This is the contrast threshold value which defines any high-contrast ROI as passing or failing.
  • invert (bool) – Whether to force an inversion of the image. This is useful if pylinac’s automatic inversion algorithm fails to properly invert the image.
  • angle_override (None, float) –

    A manual override of the angle of the phantom. If None, pylinac will automatically determine the angle. If a value is passed, this value will override the automatic detection.

    Note

    0 is pointing from the center toward the right and positive values go counterclockwise.

  • center_override (None, 2-element tuple) – A manual override of the center point of the phantom. If None, pylinac will automatically determine the center. If a value is passed, this value will override the automatic detection. Format is (x, y)/(col, row).
  • size_override (None, float) –

    A manual override of the relative size of the phantom. This size value is used to scale the positions of the ROIs from the center. If None, pylinac will automatically determine the size. If a value is passed, this value will override the automatic sizing.

    Note

    This value is not necessarily the physical size of the phantom. It is an arbitrary value.

  • ssd – The SSD of the phantom itself in mm.
  • low_contrast_method – The equation to use for calculating low contrast.
  • visibility_threshold – The threshold for whether an ROI is “seen”.
classmethod from_demo_image()

Instantiate and load the demo image.

classmethod from_url(url: str)
Parameters:url (str) – The URL to the image.
phantom_bbox_size_px

The phantom bounding box size in pixels^2.

phantom_ski_region

The skimage region of the phantom outline.

plot_analyzed_image(image: bool = True, low_contrast: bool = True, high_contrast: bool = True, show: bool = True)

Plot the analyzed image.

Parameters:
  • image (bool) – Show the image.
  • low_contrast (bool) – Show the low contrast values plot.
  • high_contrast (bool) – Show the high contrast values plot.
  • show (bool) – Whether to actually show the image when called.
publish_pdf(filename: str, notes: str = None, open_file: bool = False, metadata: Optional[dict] = None)

Publish (print) a PDF containing the analysis, images, and quantitative results.

Parameters:
  • filename ((str, file-like object}) – The file to write the results to.
  • notes (str, list of strings) – Text; if str, prints single line. If list of strings, each list item is printed on its own line.
  • open_file (bool) – Whether to open the file using the default program after creation.
  • metadata (dict) – Extra data to be passed and shown in the PDF. The key and value will be shown with a colon. E.g. passing {‘Author’: ‘James’, ‘Unit’: ‘TrueBeam’} would result in text in the PDF like: ————– Author: James Unit: TrueBeam ————–
results() → str

Return the results of the analysis.

save_analyzed_image(filename: str, **kwargs)

Save the analyzed image to a file.

Parameters:
  • filename (str) – The location and filename to save to.
  • kwargs – Keyword arguments are passed to plt.savefig().
class pylinac.planar_imaging.DoselabMC2MV(filepath: Union[str, BinaryIO], normalize: bool = True)[source]

Bases: pylinac.planar_imaging.DoselabMC2kV

Parameters:
  • filepath (str) – Path to the image file.
  • normalize (bool) – Whether to “ground” and normalize the image. This can affect contrast measurements, but for backwards compatibility this is True. You may want to set this to False if trying to compare with other software.
static run_demo() → None[source]

Run the Doselab MC2 MV-area phantom analysis demonstration.

analyze(low_contrast_threshold: float = 0.05, high_contrast_threshold: float = 0.5, invert: bool = False, angle_override: Optional[float] = None, center_override: Optional[tuple] = None, size_override: Optional[float] = None, ssd: float = 1000, low_contrast_method: pylinac.core.roi.Contrast = <Contrast.MICHELSON: 'Michelson'>, visibility_threshold: float = 100) → None

Analyze the phantom using the provided thresholds and settings.

Parameters:
  • low_contrast_threshold (float) – This is the contrast threshold value which defines any low-contrast ROI as passing or failing.
  • high_contrast_threshold (float) – This is the contrast threshold value which defines any high-contrast ROI as passing or failing.
  • invert (bool) – Whether to force an inversion of the image. This is useful if pylinac’s automatic inversion algorithm fails to properly invert the image.
  • angle_override (None, float) –

    A manual override of the angle of the phantom. If None, pylinac will automatically determine the angle. If a value is passed, this value will override the automatic detection.

    Note

    0 is pointing from the center toward the right and positive values go counterclockwise.

  • center_override (None, 2-element tuple) – A manual override of the center point of the phantom. If None, pylinac will automatically determine the center. If a value is passed, this value will override the automatic detection. Format is (x, y)/(col, row).
  • size_override (None, float) –

    A manual override of the relative size of the phantom. This size value is used to scale the positions of the ROIs from the center. If None, pylinac will automatically determine the size. If a value is passed, this value will override the automatic sizing.

    Note

    This value is not necessarily the physical size of the phantom. It is an arbitrary value.

  • ssd – The SSD of the phantom itself in mm.
  • low_contrast_method – The equation to use for calculating low contrast.
  • visibility_threshold – The threshold for whether an ROI is “seen”.
classmethod from_demo_image()

Instantiate and load the demo image.

classmethod from_url(url: str)
Parameters:url (str) – The URL to the image.
phantom_bbox_size_px

The phantom bounding box size in pixels^2.

phantom_ski_region

The skimage region of the phantom outline.

plot_analyzed_image(image: bool = True, low_contrast: bool = True, high_contrast: bool = True, show: bool = True)

Plot the analyzed image.

Parameters:
  • image (bool) – Show the image.
  • low_contrast (bool) – Show the low contrast values plot.
  • high_contrast (bool) – Show the high contrast values plot.
  • show (bool) – Whether to actually show the image when called.
publish_pdf(filename: str, notes: str = None, open_file: bool = False, metadata: Optional[dict] = None)

Publish (print) a PDF containing the analysis, images, and quantitative results.

Parameters:
  • filename ((str, file-like object}) – The file to write the results to.
  • notes (str, list of strings) – Text; if str, prints single line. If list of strings, each list item is printed on its own line.
  • open_file (bool) – Whether to open the file using the default program after creation.
  • metadata (dict) – Extra data to be passed and shown in the PDF. The key and value will be shown with a colon. E.g. passing {‘Author’: ‘James’, ‘Unit’: ‘TrueBeam’} would result in text in the PDF like: ————– Author: James Unit: TrueBeam ————–
results() → str

Return the results of the analysis.

save_analyzed_image(filename: str, **kwargs)

Save the analyzed image to a file.

Parameters:
  • filename (str) – The location and filename to save to.
  • kwargs – Keyword arguments are passed to plt.savefig().
class pylinac.planar_imaging.DoselabMC2kV(filepath: Union[str, BinaryIO], normalize: bool = True)[source]

Bases: pylinac.planar_imaging.ImagePhantomBase

Parameters:
  • filepath (str) – Path to the image file.
  • normalize (bool) – Whether to “ground” and normalize the image. This can affect contrast measurements, but for backwards compatibility this is True. You may want to set this to False if trying to compare with other software.
static run_demo() → None[source]

Run the Doselab MC2 kV-area phantom analysis demonstration.

analyze(low_contrast_threshold: float = 0.05, high_contrast_threshold: float = 0.5, invert: bool = False, angle_override: Optional[float] = None, center_override: Optional[tuple] = None, size_override: Optional[float] = None, ssd: float = 1000, low_contrast_method: pylinac.core.roi.Contrast = <Contrast.MICHELSON: 'Michelson'>, visibility_threshold: float = 100) → None

Analyze the phantom using the provided thresholds and settings.

Parameters:
  • low_contrast_threshold (float) – This is the contrast threshold value which defines any low-contrast ROI as passing or failing.
  • high_contrast_threshold (float) – This is the contrast threshold value which defines any high-contrast ROI as passing or failing.
  • invert (bool) – Whether to force an inversion of the image. This is useful if pylinac’s automatic inversion algorithm fails to properly invert the image.
  • angle_override (None, float) –

    A manual override of the angle of the phantom. If None, pylinac will automatically determine the angle. If a value is passed, this value will override the automatic detection.

    Note

    0 is pointing from the center toward the right and positive values go counterclockwise.

  • center_override (None, 2-element tuple) – A manual override of the center point of the phantom. If None, pylinac will automatically determine the center. If a value is passed, this value will override the automatic detection. Format is (x, y)/(col, row).
  • size_override (None, float) –

    A manual override of the relative size of the phantom. This size value is used to scale the positions of the ROIs from the center. If None, pylinac will automatically determine the size. If a value is passed, this value will override the automatic sizing.

    Note

    This value is not necessarily the physical size of the phantom. It is an arbitrary value.

  • ssd – The SSD of the phantom itself in mm.
  • low_contrast_method – The equation to use for calculating low contrast.
  • visibility_threshold – The threshold for whether an ROI is “seen”.
classmethod from_demo_image()

Instantiate and load the demo image.

classmethod from_url(url: str)
Parameters:url (str) – The URL to the image.
phantom_bbox_size_px

The phantom bounding box size in pixels^2.

phantom_ski_region

The skimage region of the phantom outline.

plot_analyzed_image(image: bool = True, low_contrast: bool = True, high_contrast: bool = True, show: bool = True)

Plot the analyzed image.

Parameters:
  • image (bool) – Show the image.
  • low_contrast (bool) – Show the low contrast values plot.
  • high_contrast (bool) – Show the high contrast values plot.
  • show (bool) – Whether to actually show the image when called.
publish_pdf(filename: str, notes: str = None, open_file: bool = False, metadata: Optional[dict] = None)

Publish (print) a PDF containing the analysis, images, and quantitative results.

Parameters:
  • filename ((str, file-like object}) – The file to write the results to.
  • notes (str, list of strings) – Text; if str, prints single line. If list of strings, each list item is printed on its own line.
  • open_file (bool) – Whether to open the file using the default program after creation.
  • metadata (dict) – Extra data to be passed and shown in the PDF. The key and value will be shown with a colon. E.g. passing {‘Author’: ‘James’, ‘Unit’: ‘TrueBeam’} would result in text in the PDF like: ————– Author: James Unit: TrueBeam ————–
results() → str

Return the results of the analysis.

save_analyzed_image(filename: str, **kwargs)

Save the analyzed image to a file.

Parameters:
  • filename (str) – The location and filename to save to.
  • kwargs – Keyword arguments are passed to plt.savefig().
class pylinac.planar_imaging.SNCMV(filepath: Union[str, BinaryIO], normalize: bool = True)[source]

Bases: pylinac.planar_imaging.SNCkV

Parameters:
  • filepath (str) – Path to the image file.
  • normalize (bool) – Whether to “ground” and normalize the image. This can affect contrast measurements, but for backwards compatibility this is True. You may want to set this to False if trying to compare with other software.
static run_demo() → None[source]

Run the Sun Nuclear MV-QA phantom analysis demonstration.

analyze(low_contrast_threshold: float = 0.05, high_contrast_threshold: float = 0.5, invert: bool = False, angle_override: Optional[float] = None, center_override: Optional[tuple] = None, size_override: Optional[float] = None, ssd: float = 1000, low_contrast_method: pylinac.core.roi.Contrast = <Contrast.MICHELSON: 'Michelson'>, visibility_threshold: float = 100) → None

Analyze the phantom using the provided thresholds and settings.

Parameters:
  • low_contrast_threshold (float) – This is the contrast threshold value which defines any low-contrast ROI as passing or failing.
  • high_contrast_threshold (float) – This is the contrast threshold value which defines any high-contrast ROI as passing or failing.
  • invert (bool) – Whether to force an inversion of the image. This is useful if pylinac’s automatic inversion algorithm fails to properly invert the image.
  • angle_override (None, float) –

    A manual override of the angle of the phantom. If None, pylinac will automatically determine the angle. If a value is passed, this value will override the automatic detection.

    Note

    0 is pointing from the center toward the right and positive values go counterclockwise.

  • center_override (None, 2-element tuple) – A manual override of the center point of the phantom. If None, pylinac will automatically determine the center. If a value is passed, this value will override the automatic detection. Format is (x, y)/(col, row).
  • size_override (None, float) –

    A manual override of the relative size of the phantom. This size value is used to scale the positions of the ROIs from the center. If None, pylinac will automatically determine the size. If a value is passed, this value will override the automatic sizing.

    Note

    This value is not necessarily the physical size of the phantom. It is an arbitrary value.

  • ssd – The SSD of the phantom itself in mm.
  • low_contrast_method – The equation to use for calculating low contrast.
  • visibility_threshold – The threshold for whether an ROI is “seen”.
classmethod from_demo_image()

Instantiate and load the demo image.

classmethod from_url(url: str)
Parameters:url (str) – The URL to the image.
phantom_bbox_size_px

The phantom bounding box size in pixels^2.

phantom_ski_region

The skimage region of the phantom outline.

plot_analyzed_image(image: bool = True, low_contrast: bool = True, high_contrast: bool = True, show: bool = True)

Plot the analyzed image.

Parameters:
  • image (bool) – Show the image.
  • low_contrast (bool) – Show the low contrast values plot.
  • high_contrast (bool) – Show the high contrast values plot.
  • show (bool) – Whether to actually show the image when called.
publish_pdf(filename: str, notes: str = None, open_file: bool = False, metadata: Optional[dict] = None)

Publish (print) a PDF containing the analysis, images, and quantitative results.

Parameters:
  • filename ((str, file-like object}) – The file to write the results to.
  • notes (str, list of strings) – Text; if str, prints single line. If list of strings, each list item is printed on its own line.
  • open_file (bool) – Whether to open the file using the default program after creation.
  • metadata (dict) – Extra data to be passed and shown in the PDF. The key and value will be shown with a colon. E.g. passing {‘Author’: ‘James’, ‘Unit’: ‘TrueBeam’} would result in text in the PDF like: ————– Author: James Unit: TrueBeam ————–
results() → str

Return the results of the analysis.

save_analyzed_image(filename: str, **kwargs)

Save the analyzed image to a file.

Parameters:
  • filename (str) – The location and filename to save to.
  • kwargs – Keyword arguments are passed to plt.savefig().
class pylinac.planar_imaging.SNCkV(filepath: Union[str, BinaryIO], normalize: bool = True)[source]

Bases: pylinac.planar_imaging.ImagePhantomBase

Parameters:
  • filepath (str) – Path to the image file.
  • normalize (bool) – Whether to “ground” and normalize the image. This can affect contrast measurements, but for backwards compatibility this is True. You may want to set this to False if trying to compare with other software.
static run_demo() → None[source]

Run the Sun Nuclear kV-QA phantom analysis demonstration.

analyze(low_contrast_threshold: float = 0.05, high_contrast_threshold: float = 0.5, invert: bool = False, angle_override: Optional[float] = None, center_override: Optional[tuple] = None, size_override: Optional[float] = None, ssd: float = 1000, low_contrast_method: pylinac.core.roi.Contrast = <Contrast.MICHELSON: 'Michelson'>, visibility_threshold: float = 100) → None

Analyze the phantom using the provided thresholds and settings.

Parameters:
  • low_contrast_threshold (float) – This is the contrast threshold value which defines any low-contrast ROI as passing or failing.
  • high_contrast_threshold (float) – This is the contrast threshold value which defines any high-contrast ROI as passing or failing.
  • invert (bool) – Whether to force an inversion of the image. This is useful if pylinac’s automatic inversion algorithm fails to properly invert the image.
  • angle_override (None, float) –

    A manual override of the angle of the phantom. If None, pylinac will automatically determine the angle. If a value is passed, this value will override the automatic detection.

    Note

    0 is pointing from the center toward the right and positive values go counterclockwise.

  • center_override (None, 2-element tuple) – A manual override of the center point of the phantom. If None, pylinac will automatically determine the center. If a value is passed, this value will override the automatic detection. Format is (x, y)/(col, row).
  • size_override (None, float) –

    A manual override of the relative size of the phantom. This size value is used to scale the positions of the ROIs from the center. If None, pylinac will automatically determine the size. If a value is passed, this value will override the automatic sizing.

    Note

    This value is not necessarily the physical size of the phantom. It is an arbitrary value.

  • ssd – The SSD of the phantom itself in mm.
  • low_contrast_method – The equation to use for calculating low contrast.
  • visibility_threshold – The threshold for whether an ROI is “seen”.
classmethod from_demo_image()

Instantiate and load the demo image.

classmethod from_url(url: str)
Parameters:url (str) – The URL to the image.
phantom_bbox_size_px

The phantom bounding box size in pixels^2.

phantom_ski_region

The skimage region of the phantom outline.

plot_analyzed_image(image: bool = True, low_contrast: bool = True, high_contrast: bool = True, show: bool = True)

Plot the analyzed image.

Parameters:
  • image (bool) – Show the image.
  • low_contrast (bool) – Show the low contrast values plot.
  • high_contrast (bool) – Show the high contrast values plot.
  • show (bool) – Whether to actually show the image when called.
publish_pdf(filename: str, notes: str = None, open_file: bool = False, metadata: Optional[dict] = None)

Publish (print) a PDF containing the analysis, images, and quantitative results.

Parameters:
  • filename ((str, file-like object}) – The file to write the results to.
  • notes (str, list of strings) – Text; if str, prints single line. If list of strings, each list item is printed on its own line.
  • open_file (bool) – Whether to open the file using the default program after creation.
  • metadata (dict) – Extra data to be passed and shown in the PDF. The key and value will be shown with a colon. E.g. passing {‘Author’: ‘James’, ‘Unit’: ‘TrueBeam’} would result in text in the PDF like: ————– Author: James Unit: TrueBeam ————–
results() → str

Return the results of the analysis.

save_analyzed_image(filename: str, **kwargs)

Save the analyzed image to a file.

Parameters:
  • filename (str) – The location and filename to save to.
  • kwargs – Keyword arguments are passed to plt.savefig().
class pylinac.planar_imaging.PTWEPIDQC(filepath: Union[str, BinaryIO], normalize: bool = True)[source]

Bases: pylinac.planar_imaging.ImagePhantomBase

Parameters:
  • filepath (str) – Path to the image file.
  • normalize (bool) – Whether to “ground” and normalize the image. This can affect contrast measurements, but for backwards compatibility this is True. You may want to set this to False if trying to compare with other software.
static run_demo() → None[source]

Run the Standard Imaging QC-3 phantom analysis demonstration.

analyze(low_contrast_threshold: float = 0.05, high_contrast_threshold: float = 0.5, invert: bool = False, angle_override: Optional[float] = None, center_override: Optional[tuple] = None, size_override: Optional[float] = None, ssd: float = 1000, low_contrast_method: pylinac.core.roi.Contrast = <Contrast.MICHELSON: 'Michelson'>, visibility_threshold: float = 100) → None

Analyze the phantom using the provided thresholds and settings.

Parameters:
  • low_contrast_threshold (float) – This is the contrast threshold value which defines any low-contrast ROI as passing or failing.
  • high_contrast_threshold (float) – This is the contrast threshold value which defines any high-contrast ROI as passing or failing.
  • invert (bool) – Whether to force an inversion of the image. This is useful if pylinac’s automatic inversion algorithm fails to properly invert the image.
  • angle_override (None, float) –

    A manual override of the angle of the phantom. If None, pylinac will automatically determine the angle. If a value is passed, this value will override the automatic detection.

    Note

    0 is pointing from the center toward the right and positive values go counterclockwise.

  • center_override (None, 2-element tuple) – A manual override of the center point of the phantom. If None, pylinac will automatically determine the center. If a value is passed, this value will override the automatic detection. Format is (x, y)/(col, row).
  • size_override (None, float) –

    A manual override of the relative size of the phantom. This size value is used to scale the positions of the ROIs from the center. If None, pylinac will automatically determine the size. If a value is passed, this value will override the automatic sizing.

    Note

    This value is not necessarily the physical size of the phantom. It is an arbitrary value.

  • ssd – The SSD of the phantom itself in mm.
  • low_contrast_method – The equation to use for calculating low contrast.
  • visibility_threshold – The threshold for whether an ROI is “seen”.
classmethod from_demo_image()

Instantiate and load the demo image.

classmethod from_url(url: str)
Parameters:url (str) – The URL to the image.
phantom_bbox_size_px

The phantom bounding box size in pixels^2.

phantom_ski_region

The skimage region of the phantom outline.

plot_analyzed_image(image: bool = True, low_contrast: bool = True, high_contrast: bool = True, show: bool = True)

Plot the analyzed image.

Parameters:
  • image (bool) – Show the image.
  • low_contrast (bool) – Show the low contrast values plot.
  • high_contrast (bool) – Show the high contrast values plot.
  • show (bool) – Whether to actually show the image when called.
publish_pdf(filename: str, notes: str = None, open_file: bool = False, metadata: Optional[dict] = None)

Publish (print) a PDF containing the analysis, images, and quantitative results.

Parameters:
  • filename ((str, file-like object}) – The file to write the results to.
  • notes (str, list of strings) – Text; if str, prints single line. If list of strings, each list item is printed on its own line.
  • open_file (bool) – Whether to open the file using the default program after creation.
  • metadata (dict) – Extra data to be passed and shown in the PDF. The key and value will be shown with a colon. E.g. passing {‘Author’: ‘James’, ‘Unit’: ‘TrueBeam’} would result in text in the PDF like: ————– Author: James Unit: TrueBeam ————–
results() → str

Return the results of the analysis.

save_analyzed_image(filename: str, **kwargs)

Save the analyzed image to a file.

Parameters:
  • filename (str) – The location and filename to save to.
  • kwargs – Keyword arguments are passed to plt.savefig().