ACR Phantoms

Overview

Added in version 3.2.

Warning

These algorithms have only a limited amount of testing data and results should be scrutinized. Further, the algorithm is more likely to change in the future when a more robust test suite is built up. If you’d like to submit data, enter it here.

The ACR module provides routines for automatically analyzing DICOM images of the ACR CT 464 phantom and Large MR phantom. It can load a folder or zip file of images, correcting for translational and rotational offsets.

Phantom reference information is drawn from the ACR CT solution article and the analysis is drawn from the ACR CT testing article. MR analysis is drawn from the ACR Guidance document.

Warning

Due to the rectangular ROIs on the MRI phantom analysis, rotational errors should be <= 1 degree. Translational errors are still accounted for however for any reasonable amount.

Typical Use

The ACR CT and MR analyses follows a similar pattern of load/analyze/output as the rest of the library. Unlike the CatPhan analysis, customization is not a goal, as the phantoms and analyses are much more well-defined. I.e. there’s less of a use case for custom phantoms in this scenario. CT is mostly used here but is interchangeable with the MRI class.

To use the ACR analysis, import the class:

from pylinac import ACRCT, ACRMRILarge

And then load, analyze, and view the results:

  • Load images – Loading can be done with a directory or zip file:

    acr_ct_folder = r"C:/CT/ACR/Sept 2021"
ct = ACRCT(acr_ct_folder)
acr_mri_folder = r"C:/MRI/ACR/Sept 2021"
mri = ACRMRILarge(acr_mri_folder)

    or load from zip:

    acr_ct_zip = r"C:/CT/ACR/Sept 2021.zip"
ct = ACRCT.from_zip(acr_ct_zip)

  • Analyze – Analyze the dataset:

    ct.analyze()

  • View the results – Reviewing the results can be done in text or dict format as well as images:

    # print text to the console
print(ct.results())
# view analyzed image summary
ct.plot_analyzed_image()
# view images independently
ct.plot_images()
# save the images
ct.save_analyzed_image()
# or
ct.save_images()
# finally, save a PDF
ct.publish_pdf()

Choosing an MR Echo

With MRI, a dual echo scan can be obtained. These can result in a combined DICOM dataset but are distinct acquisitions. To select between multiple echos, use the echo_number parameter:

from pylinac import ACRMRILarge

mri = ACRMRILarge(...)  # load zip or dir with dual echo image set
mri.analyze(echo_number=2)
mri.results()

If no echo number is passed, the first and lowest echo number is selected and analyzed.

Customizing MR/CT Modules

To customize aspects of the MR analysis modules, subclass the relevant module and set the attribute in the analysis class. E.g. to customize the “Slice1” MR module:

from pylinac.acr import ACRMRILarge, MRSlice1Module


class Slice1Modified(MRSlice1Module):
    """Custom location for the slice thickness ROIs"""

    thickness_roi_settings = {
        "Top": {"width": 100, "height": 4, "distance": -3},
        "Bottom": {"width": 100, "height": 4, "distance": 2.5},
    }


# now pass to the MR analysis class
class MyMRI(ACRMRILarge):
    slice1 = Slice1Modified


# use as normal
mri = MyMRI(...)
mri.analyze(...)

There are 4 modules in ACR MRI Large analysis that can be overridden. The attribute name should stay the same but the name of the subclassed module can be anything as long as it subclasses the original module:

class ACRMRILarge:
    # overload these as you wish. The attribute name cannot change.
    slice1 = MRSlice1Module
    geometric_distortion = GeometricDistortionModule
    uniformity_module = MRUniformityModule
    slice11 = MRSlice11PositionModule


class ACRCT:
    ct_calibration_module = CTModule
    low_contrast_module = LowContrastModule
    spatial_resolution_module = SpatialResolutionModule
    uniformity_module = UniformityModule

Customizing module offsets

Customizing the module offsets in the ACR module is easier than for the CT module. To do so, simply override any relevant constant like so:

import pylinac

pylinac.acr.MR_SLICE11_MODULE_OFFSET_MM = 95

mri = pylinac.ACRMRILarge(...)  # will use offset above

The options for module offsets are as follows along with their default value:

# CT
CT_UNIFORMITY_MODULE_OFFSET_MM = 70
CT_SPATIAL_RESOLUTION_MODULE_OFFSET_MM = 100
CT_LOW_CONTRAST_MODULE_OFFSET_MM = 30

# MR
MR_SLICE11_MODULE_OFFSET_MM = 100
MR_GEOMETRIC_DISTORTION_MODULE_OFFSET_MM = 40
MR_UNIFORMITY_MODULE_OFFSET_MM = 60

Advanced Use

Using results_data

Using the ACR module in your own scripts? While the analysis results can be printed out, if you intend on using them elsewhere (e.g. in an API), they can be accessed the easiest by using the results_data() method which returns a ACRCTResult instance. For MRI this is results_data() method and ACRMRIResult respectively.

Continuing from above:

data = ct.results_data()
data.ct_module.roi_radius_mm
# and more

# return as a dict
data_dict = ct.results_data(as_dict=True)
data_dict["ct_module"]["roi_radius_mm"]
...

Adjusting ROI locations

To adjust ROI locations, see the sister section for CT analysis: Adjusting ROI locations.

CT Analysis Parameters

See pylinac.acr.ACRCT.analyze() for details.

  • X adjustment: A fine-tuning adjustment to the detected x-coordinate of the phantom center. This will move the detected phantom position by this amount in the x-direction in mm. Positive values move the phantom to the right.

  • Y adjustment: A fine-tuning adjustment to the detected y-coordinate of the phantom center. This will move the detected phantom position by this amount in the y-direction in mm. Positive values move the phantom down.

  • Angle adjustment: A fine-tuning adjustment to the detected angle of the phantom. This will rotate the phantom by this amount in degrees. Positive values rotate the phantom clockwise.

  • ROI size factor: A fine-tuning adjustment to the ROI sizes of the phantom. This will scale the ROIs by this amount. Positive values increase the ROI sizes. In contrast to the scaling adjustment, this adjustment effectively makes the ROIs bigger or smaller, but does not adjust their position.

  • Scaling factor: A fine-tuning adjustment to the detected magnification of the phantom. This will zoom the ROIs and phantom outline (if applicable) by this amount. In contrast to the roi size adjustment, the scaling adjustment effectively moves the phantom and ROIs closer or further from the phantom center. I.e. this zooms the outline and ROI positions, but not ROI size.

  • Origin slice: The slice number that corresponds to the HU linearity slice. This is a fallback mechanism in case the automatic detection fails.

Interpreting CT Results

The outcome from analyzing the phantom and calling .results_data() is a ACRCTResult instance. See the API documentation for details and also Exporting Results.

The outcome from analyzing the phantom in RadMachine will return an “Entire Result” as follows:

  • phantom_model: The model of the phantom used.

  • phantom_roll_deg: The roll of the phantom in degrees.

  • origin_slice: The slice number of the “origin” slice; for ACR this is Module 1.

  • num_images: The number of images in the passed dataset.

  • ct_module: The results of the CT module with the following items:

    • offset: The offset of the module slice in mm from the origin slice (z-direction).

    • roi_distance_from_center_mm: The distance of the ROIs from the center of the phantom in mm in the image plane.

    • roi_radius_mm: The radius of the ROIs in mm.

    • rois: The analyzed ROIs. The key is the name of the material and the value is the mean HU value. E.g. 'Air': -987.1.

    • roi_settings: The ROI settings. The keys are the material names, each with the following items:

      • angle: The angle of the ROI in degrees.

      • distance: The distance of the ROI from the center of the phantom in mm.

      • radius: The radius of the ROI in mm.

      • distance_pixels: The distance of the ROI from the center of the phantom in pixels.

      • radius_pixels: The radius of the ROI in pixels.

      • angle_corrected: The angle of the ROI corrected for phantom roll in degrees.

  • uniformity_module: The results from the Uniformity module, with the following items:

    • offset: The offset of the module slice in mm from the origin slice (z-direction).

    • roi_distance_from_center_mm: The distance of the ROIs from the center of the phantom in mm in the image plane.

    • roi_radius_mm: The radius of the ROIs in mm.

    • rois: The analyzed ROIs. The key is the location and the value is the mean HU value. E.g. 'Top': 13.2.

    • roi_settings: The ROI settings. The keys are the location names.

    • center_roi_stdev: The standard deviation of the center ROI.

  • low_contrast_module: The results of the Low-Contrast module, with the following items:

    • offset: The offset of the module slice in mm from the origin slice (z-direction).

    • roi_distance_from_center_mm: The distance of the ROIs from the center of the phantom in mm in the image plane.

    • roi_radius_mm: The radius of the ROIs in mm.

    • rois: The analyzed ROI values.

    • roi_settings: The ROI settings.

    • cnr: The contrast-to-noise ratio.

  • spatial_resolution_module: The results of the Spatial Resolution module, with the following items:

    • offset: The offset of the module slice in mm from the origin slice (z-direction).

    • roi_distance_from_center_mm: The distance of the ROIs from the center of the phantom in mm in the image plane.

    • roi_radius_mm: The radius of the ROIs in mm.

    • rois: The analyzed ROIs. The key is the location and the value is the mean HU value. E.g. 'Top': 13.2.

    • roi_settings: The ROI settings. The keys are the location names.

    • lpmm_to_rmtf: Line pair to relative modulation transfer mapping. The keys are the line pair values and the values are the relative modulation transfer values.

MRI Algorithm

The ACR MR analysis is based on the official guidance document. Because the guidance document is extremely specific (nice job ACR!) only a few highlights are given here. The guidance is followed as reasonably close as possible.

Allowances

  • Multiple MR sequences can be present in the dataset.

  • The phantom can have significant cartesian shifts.

Restrictions

  • There should be 11 slices (without sagittal slice) or 12 slices (with sagittal slice) per scan (although multiple echo scans are allowed) per the guidance document (section 0.3).

  • The phantom should have very little pitch, yaw, or roll (<1 degree).

Sagittal slice

If the sagittal slice was acquired in a separate acquisition it will have a different Series UID. By default pylinac assumes that there is only a single Series UID and therefore it will disregard the sagittal slice. To include the sagittal slice in the analysis, set the flag check_uid=False.

mri = ACRMRILarge(acr_mri_folder, check_uid=False)

Troubleshooting

The MRI phantom specifically can suffer from evaporation of the water in the phantom. This causes bubbles to appear physically and also creates artifacts in the image. Large enough air in the phantom can cause an analysis to fail and/or create shifting issues in the analysis.

_images/acr_mri_air_bubble.png

If bubbles are present, you can send it to the manufacturer to be fixed, or you can fill it yourself per this YouTube video: Filling the ACR MRI Phantom.

Analysis

Section 0.4 specifies the 8 tests to perform. Pylinac can perform 7 of these 8. The artifact assessment test is considered a visual inspection test.

  • Geometric Accuracy - The geometric accuracy is measured using profiles of slice 5. The only difference is that pylinac uses an automatic image thresholding and then takes the FWHM of several profiles of this new image. The width between the two pixels defining the FWHM is the diameter.

  • Geometric Accuracy on sagittal slice - The geometric accuracy on sagital slice is measured using profiles of a sagittal slice. This analysis is conditional on the existence of a sagittal slice in the dataset. The method follows the same principles as in the Geometric Accuracy analysis. Specifically, pylinac applies automatic image thresholding and determines FWHM across several profiles in the image. The distance between the two pixels that define the FWHM is taken as the measured length.

  • High Contrast - High contrast is hard to measure for the ACR MRI phantom simply because it does not use line pairs, but rather offset dots as well as the qualitative description in the guidance document about how to score these. Pylinac measures the high-contrast by sampling a circular ROI on the left ROI (phantom right) set. This is the baseline which all other measurements will be normalized to. The actual dot-ROIs are sampled by taking a circular ROI of the row-based set and the column-based set. Each row-based ROI is evaluated against the other row-based ROIs. The same is done for column-based ROIs. The ROIs use the maximum and minimum pixel values inside the sample ROI. No dot-counting is performed.

    Tip

    It is suggested to perform the contrast measurement visually and compare to pylinac values to establish a cross-comparison ratio. After a ratio has been established, the pylinac MTF can be used as the baseline value moving forward.

  • Slice thickness - Slice thickness is measured using the FWHM of two rectangular ROIs. This is very similar to the guidance document explanation.

    Slice thickness is defined the same as in the guidance document:

    \[Thickness = 0.2 * \frac{Top * Bottom}{Top + Bottom}\]

  • Slice Position - Slice position accuracy is measured very similarly to the manual method described in the document: “The display level setting … should be set to a level roughly half that of the signal in the bright, all-water portions of the phantom.” For each vertical bar, the pixel nearest to the mid-value between min and max of the rectangular ROI is used as the bar position:

    \[position_{bar} = \frac{ROI_{max} - ROI_{min}}{2} + ROI_{min}\]

    The difference in positions between the bars is the value reported.

  • Uniformity - Uniformity is measured using a circular ROI at the center of the phantom and ROIs to the top, bottom, left, and right of the phantom, very similar to the guidance document.

    The percent integral uniformity (PIU) is defined as:

    \[PIU = 100 * (1 - \frac{high-low}{high+low})\]

    Instead of using the WL/WW to find the low and high 1cm2 ROI, pylinac uses the 1st and 99th percentile of pixel values inside the central ROI.

  • Ghosting ratio The ghosting ratio is defined the same as the ACR guidance document:

    \[ghosting_{ratio} = |\frac{(top + bottom) - (left + right)}{2*ROI_{large}}|\]

    where all values are the median pixel values of their respective ROI. The percent-signal ghosting (PSG) is:

    \[PSG = ghosting_{ratio} * 100\]

  • Low Contrast Detectability - The low contrast detectability test analyzes slices 8, 9, 10, and 11 of the ACR MRI Large phantom. Each slice contains 10 spokes arranged in a circular pattern, with spoke diameters decreasing clockwise from 7.0 mm (spoke 1) to 1.5 mm (spoke 10). Each spoke contains 3 disks (objects) at different radial distances from the phantom center.

    The analysis counts complete spokes per slice, where a spoke is considered complete only if all 3 disks are visible. Counting starts from spoke 1 and stops at the first incomplete spoke. Complete spokes that appear after an incomplete spoke are not counted. The visibility of the individual objects is evaluated with the Visibility algorithm.

MRI Analysis Parameters

See pylinac.acr.ACRMRILarge.analyze() for details.

  • Echo Number: The echo sequence to analyze; uses the Echo Numbers DICOM tag. Only relevant if more than one echo sequence is present in the dataset. If multiple echos are detected, the default is to analyze the first echo sequence.

  • X adjustment: A fine-tuning adjustment to the detected x-coordinate of the phantom center. This will move the detected phantom position by this amount in the x-direction in mm. Positive values move the phantom to the right.

  • Y adjustment: A fine-tuning adjustment to the detected y-coordinate of the phantom center. This will move the detected phantom position by this amount in the y-direction in mm. Positive values move the phantom down.

  • Angle adjustment: A fine-tuning adjustment to the detected angle of the phantom. This will rotate the phantom by this amount in degrees. Positive values rotate the phantom clockwise.

  • ROI size factor: A fine-tuning adjustment to the ROI sizes of the phantom. This will scale the ROIs by this amount. Positive values increase the ROI sizes. In contrast to the scaling adjustment, this adjustment effectively makes the ROIs bigger or smaller, but does not adjust their position.

  • Scaling factor: A fine-tuning adjustment to the detected magnification of the phantom. This will zoom the ROIs and phantom outline (if applicable) by this amount. In contrast to the roi size adjustment, the scaling adjustment effectively moves the phantom and ROIs closer or further from the phantom center. I.e. this zooms the outline and ROI positions, but not ROI size.

  • Visibility threshold: The visibility threshold for determining if a disk is visible in the low contrast slices.

Interpreting MRI Results

The outcome from analyzing the phantom and calling .results_data() is a ACRMRIResult instance. See the API documentation for details and also Exporting Results.

The outcome from analyzing the phantom in RadMachine will return an “Entire Result” as follows:

  • phantom_model: The model of the phantom used.

  • phantom_roll_deg: The roll of the phantom in degrees.

  • origin_slice: The slice number of the “origin” slice; for ACR this is Slice 1.

  • num_images: The number of images in the passed dataset.

  • slice1: The results for the “Slice 1” module with the following items:

    • offset: The offset of the phantom in mm from the origin slice.

    • bar_difference_mm: The difference in bar positions in mm.

    • slice_shift_mm: The measured shift in slice position compared to nominal.

    • measured_slice_thickness_mm: The measured slice thickness in mm.

    • row_mtf_50: The MTF at 50% for the row-based ROIs.

    • col_mtf_50: The MTF at 50% for the column-based ROIs.

    • row_mtf_lp_mm: The MTF:lp/mm for the row-based ROIs from 10-90% in 10% increments.

    • col_mtf_lp_mm: The MTF:lp/mm for the column-based ROIs from 10-90% in 10% increments.

    • rois: A dictionary of the analyzed MTF ROIs. The key is the name of the ROI; e.g. Row 1.1 and the key is a dictionary of the following items:

      • name: The name of the ROI.

      • value: The mean HU value of the ROI.

      • stdev: The standard deviation of the ROI.

    • roi_settings: A dictionary of the ROI settings. The keys are the ROI names, each with the following items:

      • angle: The angle of the ROI in degrees.

      • distance: The distance of the ROI from the center of the phantom in mm.

      • radius: The radius of the ROI in mm.

      • distance_pixels: The distance of the ROI from the center of the phantom in pixels.

      • radius_pixels: The radius of the ROI in pixels.

      • angle_corrected: The angle of the ROI corrected for phantom roll in degrees.

  • slice11: A dictionary of results from the analysis of “Slice 11” with the ‘ following items:

    • offset: The offset of the phantom in mm from the origin slice.

    • bar_difference_mm: The difference in bar positions in mm.

    • slice_shift_mm: The measure shift in slice position compared to nominal.

    • rois: The results of the left and right bar ROIs. The key is the name of the bar and the results are a dictionary with the following items:

      • name: The name of the ROI.

      • value: The mean HU value of the ROI.

      • stdev: The standard deviation of the ROI.

      • roi_settings: The ROI settings. The keys are the ROI names, each with the following items:

        • angle: The angle of the ROI in degrees.

        • distance: The distance of the ROI from the center of the phantom in mm.

        • radius: The radius of the ROI in mm.

        • distance_pixels: The distance of the ROI from the center of the phantom in pixels.

        • radius_pixels: The radius of the ROI in pixels.

        • angle_corrected: The angle of the ROI corrected for phantom roll in degrees.

  • uniformity_module: Results from the uniformity module with the following items:

    • offset: The offset of the phantom in mm from the origin slice.

    • ghosting_ratio: The ghosting ratio.

    • piu: The percent integral uniformity.

    • piu_passed: Whether the PIU passed the test.

    • psg: The percent signal ghosting.

    • rois: A dictionary of the analyzed ROIs. The key is the name of the ROI region with the following items:

      • name: The name of the ROI.

      • value: The mean HU value of the ROI.

      • stdev: The standard deviation of the ROI.

      • difference: The difference in HU value from the nominal value.

      • nominal_value: The nominal HU value of the ROI.

      • passed: Whether the ROI passed the test.

    • roi_settings: A dictionary of the ROI settings. The keys are the ROI names, each with the following items:

      • angle: The angle of the ROI in degrees.

      • distance: The distance of the ROI from the center of the phantom in mm.

      • radius: The radius of the ROI in mm.

      • distance_pixels: The distance of the ROI from the center of the phantom in pixels.

      • radius_pixels: The radius of the ROI in pixels.

      • angle_corrected: The angle of the ROI corrected for phantom roll in degrees.

    • ghost_rois: A dictionary of the analyzed ghosting ROIs. The key is the name of the ROI region with the following items:

      • name: The name of the ROI.

      • value: The mean HU value of the ROI.

      • stdev: The standard deviation of the ROI.

  • ghost_roi_settings: A dictionary of the ghosting ROI settings. The keys are the ROI names, each with the following items:

    • angle: The angle of the ROI in degrees.

    • distance: The distance of the ROI from the center of the phantom in mm.

    • radius: The radius of the ROI in mm.

    • distance_pixels: The distance of the ROI from the center of the phantom in pixels.

    • radius_pixels: The radius of the ROI in pixels.

    • angle_corrected: The angle of the ROI corrected for phantom roll in degrees.

  • geometric_distortion_module: Results from the geometric distortion module with the following items:

    • offset: The offset of the phantom in mm from the origin slice.

    • profiles: A dictionary of the profiles used to measure the geometric distortion. The key is the name of the profile and the value is a dictionary with the following items:

      • width_mm: The FWHM of the profile in mm.

      • line: A dictionary representing the line to be plotted.

    • distances: The lines measuring the ROI size. The key is the name of the line direction and the value is a string of the line length.

  • sagittal_localizer_module: Results from the sagittal localizer module with the following items:

    • profiles: A dictionary of the profiles used to measure the geometric distortion. The key is the name of the profile and the value is a dictionary with the following items:

      • width_mm: The FWHM of the profile in mm.

      • line: A dictionary representing the line to be plotted.

    • distances: The lines measuring the ROI size. The key is the name of the line direction and the value is a string of the line length.

API Documentation

class pylinac.acr.ACRCT(folderpath: str | Sequence[str] | Path | Sequence[Path] | Sequence[BytesIO], check_uid: bool = True, memory_efficient_mode: bool = False, is_zip: bool = False)[source]

Bases: CatPhanBase, ResultsDataMixin[ACRCTResult]

Parameters

folderpathstr, list of strings, or Path to folder

String that points to the CBCT image folder location.

check_uidbool

Whether to enforce raising an error if more than one UID is found in the dataset.

memory_efficient_modebool

Whether to use a memory efficient mode. If True, the DICOM stack will be loaded on demand rather than all at once. This will reduce the memory footprint but will be slower by ~25%. Default is False.

Raises

NotADirectoryError

If folder str passed is not a valid directory.

FileNotFoundError

If no CT images are found in the folder

ct_calibration_module

alias of CTModule

low_contrast_module

alias of LowContrastModule

spatial_resolution_module

alias of SpatialResolutionModule

uniformity_module

alias of UniformityModule

plot_analyzed_subimage(*args, **kwargs)[source]

Plot a specific component of the CBCT analysis.

Parameters

subimage{‘hu’, ‘un’, ‘sp’, ‘lc’, ‘mtf’, ‘lin’, ‘prof’, ‘side’}

The subcomponent to plot. Values must contain one of the following letter combinations. E.g. linearity, linear, and lin will all draw the HU linearity values.

  • hu draws the HU linearity image.

  • un draws the HU uniformity image.

  • sp draws the Spatial Resolution image.

  • lc draws the Low Contrast image (if applicable).

  • mtf draws the RMTF plot.

  • lin draws the HU linearity values. Used with delta.

  • prof draws the HU uniformity profiles.

  • side draws the side view of the phantom with lines of the module locations.

deltabool

Only for use with lin. Whether to plot the HU delta or actual values.

showbool

Whether to actually show the plot.

save_analyzed_subimage(*args, **kwargs)[source]

Save a component image to file.

Parameters

filenamestr, file object

The file to write the image to.

subimagestr

See plot_analyzed_subimage() for parameter info.

deltabool

Only for use with lin. Whether to plot the HU delta or actual values.

analyze(x_adjustment: float = 0, y_adjustment: float = 0, angle_adjustment: float = 0, roi_size_factor: float = 1, scaling_factor: float = 1, origin_slice: int | None = None) → None[source]

Analyze the ACR CT phantom

Parameters

x_adjustment: float

A fine-tuning adjustment to the detected x-coordinate of the phantom center. This will move the detected phantom position by this amount in the x-direction in mm. Positive values move the phantom to the right.

y_adjustment: float

A fine-tuning adjustment to the detected y-coordinate of the phantom center. This will move the detected phantom position by this amount in the y-direction in mm. Positive values move the phantom down.

angle_adjustment: float

A fine-tuning adjustment to the detected angle of the phantom. This will rotate the phantom by this amount in degrees. Positive values rotate the phantom clockwise.

roi_size_factor: float

A fine-tuning adjustment to the ROI sizes of the phantom. This will scale the ROIs by this amount. Positive values increase the ROI sizes. In contrast to the scaling adjustment, this adjustment effectively makes the ROIs bigger or smaller, but does not adjust their position.

scaling_factor: float

A fine-tuning adjustment to the detected magnification of the phantom. This will zoom the ROIs and phantom outline (if applicable) by this amount. In contrast to the roi size adjustment, the scaling adjustment effectively moves the phantom and ROIs closer or further from the phantom center. I.e. this zooms the outline and ROI positions, but not ROI size.

origin_slice: int, None

The slice number of the HU linearity module. If None, will be automatically determined. This is a fallback method in case the automatic determination fails.

plotly_analyzed_images(show: bool = True, show_colorbar: bool = True, show_legend: bool = True, **kwargs) → dict[str, Figure][source]

Plot the analyzed image using Plotly. Will create multiple figures.

Parameters

showbool

Whether to show the images. Set to False if doing further processing of the figure.

show_colorbarbool

Whether to show the colorbar on the images.

show_legendbool

Whether to show the legend on the images.

kwargs

Additional keyword arguments to pass to the figure.

plot_analyzed_image(show: bool = True, **plt_kwargs) → Figure[source]

Plot the analyzed image

Parameters

show

Whether to show the image.

plt_kwargs

Keywords to pass to matplotlib for figure customization.

save_analyzed_image(filename: str | Path | BytesIO, **plt_kwargs) → None[source]

Save the analyzed image to disk or stream

Parameters

filename

Where to save the image to

plt_kwargs

Keywords to pass to matplotlib for figure customization.

plot_images(show: bool = True, **plt_kwargs) → dict[str, Figure][source]

Plot all the individual images separately

Parameters

show

Whether to show the images.

plt_kwargs

Keywords to pass to matplotlib for figure customization.

save_images(directory: Path | str | None = None, to_stream: bool = False, **plt_kwargs) → list[Path | BytesIO][source]

Save separate images to disk or stream.

Parameters

directory

The directory to write the images to. If None, will use current working directory

to_stream

Whether to write to stream or disk. If True, will return streams. Directory is ignored in that scenario.

plt_kwargs

Keywords to pass to matplotlib for figure customization.

find_phantom_roll(func=<function ACRCT.<lambda>>) → float[source]

Determine the “roll” of the phantom.

Only difference of base method is that we sort the ROIs by size, not by being in the center since the two we’re looking for are both right-sided.

results() → str[source]

Return the results of the analysis as a string. Use with print().

publish_pdf(filename: str | Path, notes: str | None = None, open_file: bool = False, metadata: dict | None = None, logo: Path | str | None = None) → None[source]

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

Parameters

filename(str, file-like object}

The file to write the results to.

notesstr, list of strings

Text; if str, prints single line. If list of strings, each list item is printed on its own line.

open_filebool

Whether to open the file using the default program after creation.

metadatadict

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 ————–

logo: Path, str

A custom logo to use in the PDF report. If nothing is passed, the default pylinac logo is used.

property catphan_size: float

The expected size of the phantom in pixels, based on a 20cm wide phantom.

clear_captured_warnings() → None

Clear the list of captured warnings.

find_origin_slice() → int

Using a brute force search of the images, find the median HU linearity slice.

This method walks through all the images and takes a collapsed circle profile where the HU linearity ROIs are. If the profile contains both low (<800) and high (>800) HU values and most values are the same (i.e. it’s not an artifact), then it can be assumed it is an HU linearity slice. The median of all applicable slices is the center of the HU slice.

Returns

int

The middle slice of the HU linearity module.

find_phantom_axis()

We fit all the center locations of the phantom across all slices to a 1D poly function instead of finding them individually for robustness.

Normally, each slice would be evaluated individually, but the RadMachine jig gets in the way of detecting the HU module (🤦‍♂️). To work around that in a backwards-compatible way we instead look at all the slices and if the phantom was detected, capture the phantom center. ALL the centers are then fitted to a 1D poly function and passed to the individual slices. This way, even if one slice is messed up (such as because of the phantom jig), the poly function is robust to give the real center based on all the other properly-located positions on the other slices.

classmethod from_demo_images()

Construct a CBCT object from the demo images.

classmethod from_url(url: str, check_uid: bool = True)

Instantiate a CBCT object from a URL pointing to a .zip object.

Parameters

urlstr

URL pointing to a zip archive of CBCT images.

check_uidbool

Whether to enforce raising an error if more than one UID is found in the dataset.

classmethod from_zip(zip_file: str | ZipFile | BinaryIO, check_uid: bool = True, memory_efficient_mode: bool = False)

Construct a CBCT object and pass the zip file.

Parameters

zip_filestr, ZipFile

Path to the zip file or a ZipFile object.

check_uidbool

Whether to enforce raising an error if more than one UID is found in the dataset.

memory_efficient_modebool

Whether to use a memory efficient mode. If True, the DICOM stack will be loaded on demand rather than all at once. This will reduce the memory footprint but will be slower by ~25%. Default is False.

Raises

FileExistsError : If zip_file passed was not a legitimate zip file. FileNotFoundError : If no CT images are found in the folder

get_captured_warnings() → list[dict]

Retrieve the list of captured warnings.

localize(origin_slice: int | None) → None

Find the slice number of the catphan’s HU linearity module and roll angle

property mm_per_pixel: float

The millimeters per pixel of the DICOM images.

property num_images: int

The number of images loaded.

plot_side_view(axis: Axes) → None

Plot a view of the scan from the side with lines showing detected module positions

refine_origin_slice(initial_slice_num: int) → int

Apply a refinement to the origin slice. This was added to handle the catphan 604 at least due to variations in the length of the HU plugs.

results_data(as_dict: bool = False, as_json: bool = False, by_alias: bool = False, exclude: set[str] | None = None) → T | dict | str

Present the results data and metadata as a dataclass, dict, or tuple. The default return type is a dataclass.

Parameters

as_dictbool

If True, return the results as a dictionary.

as_jsonbool

If True, return the results as a JSON string. Cannot be True if as_dict is True.

by_aliasbool

If True, use the alias names of the dataclass fields. These are generally the more human-readable names.

excludeset

A set of fields to exclude from the results data.

to_quaac(path: str | Path, performer: User, primary_equipment: Equipment, format: Literal['json', 'yaml'] = 'yaml', attachments: list[Attachment] | None = None, overwrite: bool = False, **kwargs) → None

Write an analysis to a QuAAC file. This will include the items from results_data() and the PDF report.

Parameters

pathstr, Path

The file to write the results to.

performerUser

The user who performed the analysis.

primary_equipmentEquipment

The equipment used in the analysis.

format{‘json’, ‘yaml’}

The format to write the file in.

attachmentslist of Attachment

Additional attachments to include in the QuAAC file.

overwritebool

Whether to overwrite the file if it already exists.

kwargs

Additional keyword arguments to pass to the Document instantiation.

pydantic model pylinac.acr.ACRCTResult[source]

Bases: ResultBase

This class should not be called directly. It is returned by the results_data() method.

Use the following attributes as normal class attributes.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field phantom_model: str [Required]

The model of the phantom used.

field phantom_roll_deg: float [Required]

The roll of the phantom in degrees.

field origin_slice: int [Required]

The slice number of the ‘origin’ slice; for ACR this is Module 1.

field num_images: int [Required]

The number of images in the passed dataset.

field ct_module: CTModuleOutput [Required]

The results of the CT module.

field uniformity_module: UniformityModuleOutput [Required]

The results of the Uniformity module.

field low_contrast_module: LowContrastModuleOutput [Required]

The results of the Low Contrast module.

field spatial_resolution_module: SpatialResolutionModuleOutput [Required]

The results of the Spatial Resolution module.

pydantic model pylinac.acr.CTModuleOutput[source]

Bases: BaseModel

This class should not be called directly. It is returned by the results_data() method.

Use the following attributes as normal class attributes.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field offset: float [Required]

The offset of the module slice in mm from the origin slice (z-direction).

field roi_distance_from_center_mm: float [Required]

The distance of the ROIs from the center of the phantom in mm in the image plane.

field roi_radius_mm: float [Required]

The radius of the ROIs in mm.

field roi_settings: dict [Required]

The ROI settings. The keys are the material names.

field rois: dict[str, float] [Required]

The analyzed ROIs. The key is the name of the material and the value is the mean HU value. E.g. 'Air': -987.1.

pydantic model pylinac.acr.UniformityModuleOutput[source]

Bases: CTModuleOutput

This class should not be called directly. It is returned by the results_data() method.

Use the following attributes as normal class attributes.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field center_roi_stdev: float [Required]

The standard deviation of the center ROI.

pydantic model pylinac.acr.SpatialResolutionModuleOutput[source]

Bases: CTModuleOutput

This class should not be called directly. It is returned by the results_data() method.

Use the following attributes as normal class attributes.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field lpmm_to_rmtf: dict [Required]

Line pair to relative modulation transfer mapping. The keys are the line pair values and the values are the relative modulation transfer values.

pydantic model pylinac.acr.LowContrastModuleOutput[source]

Bases: CTModuleOutput

This class should not be called directly. It is returned by the results_data() method. It is a dataclass under the hood and thus comes with all the dunder magic.

Use the following attributes as normal class attributes.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field cnr: float [Required]

The contrast-to-noise ratio.

class pylinac.acr.ACRMRILarge(folderpath: str | Sequence[str] | Path | Sequence[Path] | Sequence[BytesIO], check_uid: bool = True, memory_efficient_mode: bool = False, is_zip: bool = False)[source]

Bases: CatPhanBase, ResultsDataMixin[ACRMRIResult]

Parameters

folderpathstr, list of strings, or Path to folder

String that points to the CBCT image folder location.

check_uidbool

Whether to enforce raising an error if more than one UID is found in the dataset.

memory_efficient_modebool

Whether to use a memory efficient mode. If True, the DICOM stack will be loaded on demand rather than all at once. This will reduce the memory footprint but will be slower by ~25%. Default is False.

Raises

NotADirectoryError

If folder str passed is not a valid directory.

FileNotFoundError

If no CT images are found in the folder

slice1

alias of MRSlice1Module

geometric_distortion

alias of GeometricDistortionModule

uniformity_module

alias of MRUniformityModule

slice11

alias of MRSlice11PositionModule

sagittal_localization

alias of SagittalLocalizationModule

low_contrast_multi_slice

alias of MRLowContrastMultiSliceModule

plot_analyzed_subimage(*args, **kwargs)[source]

Plot a specific component of the CBCT analysis.

Parameters

subimage{‘hu’, ‘un’, ‘sp’, ‘lc’, ‘mtf’, ‘lin’, ‘prof’, ‘side’}

The subcomponent to plot. Values must contain one of the following letter combinations. E.g. linearity, linear, and lin will all draw the HU linearity values.

  • hu draws the HU linearity image.

  • un draws the HU uniformity image.

  • sp draws the Spatial Resolution image.

  • lc draws the Low Contrast image (if applicable).

  • mtf draws the RMTF plot.

  • lin draws the HU linearity values. Used with delta.

  • prof draws the HU uniformity profiles.

  • side draws the side view of the phantom with lines of the module locations.

deltabool

Only for use with lin. Whether to plot the HU delta or actual values.

showbool

Whether to actually show the plot.

save_analyzed_subimage(*args, **kwargs)[source]

Save a component image to file.

Parameters

filenamestr, file object

The file to write the image to.

subimagestr

See plot_analyzed_subimage() for parameter info.

deltabool

Only for use with lin. Whether to plot the HU delta or actual values.

localize() → None[source]

Find the slice number of the catphan’s HU linearity module and roll angle

find_phantom_roll() → float[source]

Determine the “roll” of the phantom. This algorithm uses the circular left-upper hole on slice 1 as the reference

Returns

float : the angle of the phantom in degrees.

analyze(echo_number: int | None = None, x_adjustment: float = 0, y_adjustment: float = 0, angle_adjustment: float = 0, roi_size_factor: float = 1, scaling_factor: float = 1, low_contrast_method: str = 'Weber', low_contrast_visibility_threshold: float = 0.001, low_contrast_visibility_sanity_multiplier: float = 3) → None[source]

Analyze the ACR CT phantom

Parameters

echo_number:

The echo to analyze. If not passed, uses the minimum echo number found.

x_adjustment: float

A fine-tuning adjustment to the detected x-coordinate of the phantom center. This will move the detected phantom position by this amount in the x-direction in mm. Positive values move the phantom to the right.

y_adjustment: float

A fine-tuning adjustment to the detected y-coordinate of the phantom center. This will move the detected phantom position by this amount in the y-direction in mm. Positive values move the phantom down.

angle_adjustment: float

A fine-tuning adjustment to the detected angle of the phantom. This will rotate the phantom by this amount in degrees. Positive values rotate the phantom clockwise.

roi_size_factor: float

A fine-tuning adjustment to the ROI sizes of the phantom. This will scale the ROIs by this amount. Positive values increase the ROI sizes. In contrast to the scaling adjustment, this adjustment effectively makes the ROIs bigger or smaller, but does not adjust their position.

scaling_factor: float

A fine-tuning adjustment to the detected magnification of the phantom. This will zoom the ROIs and phantom outline (if applicable) by this amount. In contrast to the roi size adjustment, the scaling adjustment effectively moves the phantom and ROIs closer or further from the phantom center. I.e. this zooms the outline and ROI positions, but not ROI size.

low_contrast_method: str

The contrast equation to use in the low contrast modules. See Low contrast.

low_contrast_visibility_threshold: float

The visibility threshold for determining if an object is visible in the low contrast modules.

low_contrast_visibility_sanity_multiplier: float

This applies to the smallest low-contrast ROIs. Due to the low standard deviation in small ROIs, the calculated visibility can become significantly larger than is physically reasonable. This sanity threshold ensures that a small ROI is considered not visible if its visibility exceeds that of the first spoke by this multiplier.

plotly_analyzed_images(show: bool = True, show_colorbar: bool = True, show_legend: bool = True, **kwargs) → dict[str, Figure][source]

Plot the analyzed set of images to Plotly figures.

Parameters

showbool

Whether to show the plot.

show_colorbarbool

Whether to show the colorbar on the plot.

show_legendbool

Whether to show the legend on the plot.

kwargs

Additional keyword arguments to pass to the plot.

Returns

dict

A dictionary of the Plotly figures where the key is the name of the image and the value is the figure.

plot_analyzed_image(show: bool = True, **plt_kwargs) → Figure[source]

Plot the analyzed image

Parameters

show

Whether to show the image.

plt_kwargs

Keywords to pass to matplotlib for figure customization.

plot_images(show: bool = True, **plt_kwargs) → dict[str, Figure][source]

Plot all the individual images separately

Parameters

show

Whether to show the images.

plt_kwargs

Keywords to pass to matplotlib for figure customization.

save_images(directory: Path | str | None = None, to_stream: bool = False, **plt_kwargs) → list[Path | BytesIO][source]

Save separate images to disk or stream.

Parameters

directory

The directory to write the images to. If None, will use current working directory

to_stream

Whether to write to stream or disk. If True, will return streams. Directory is ignored in that scenario.

plt_kwargs

Keywords to pass to matplotlib for figure customization.

publish_pdf(filename: str | Path, notes: str | None = None, open_file: bool = False, metadata: dict | None = None, logo: Path | str | None = None) → None[source]

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

Parameters

filename(str, file-like object}

The file to write the results to.

notesstr, list of strings

Text; if str, prints single line. If list of strings, each list item is printed on its own line.

open_filebool

Whether to open the file using the default program after creation.

metadatadict

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 ————–

logo: Path, str

A custom logo to use in the PDF report. If nothing is passed, the default pylinac logo is used.

results(as_str: bool = True) → str | tuple[source]

Return the results of the analysis as a string. Use with print().

property catphan_size: float

The expected size of the phantom in pixels, based on a 20cm wide phantom.

clear_captured_warnings() → None

Clear the list of captured warnings.

find_origin_slice() → int

Using a brute force search of the images, find the median HU linearity slice.

This method walks through all the images and takes a collapsed circle profile where the HU linearity ROIs are. If the profile contains both low (<800) and high (>800) HU values and most values are the same (i.e. it’s not an artifact), then it can be assumed it is an HU linearity slice. The median of all applicable slices is the center of the HU slice.

Returns

int

The middle slice of the HU linearity module.

find_phantom_axis()

We fit all the center locations of the phantom across all slices to a 1D poly function instead of finding them individually for robustness.

Normally, each slice would be evaluated individually, but the RadMachine jig gets in the way of detecting the HU module (🤦‍♂️). To work around that in a backwards-compatible way we instead look at all the slices and if the phantom was detected, capture the phantom center. ALL the centers are then fitted to a 1D poly function and passed to the individual slices. This way, even if one slice is messed up (such as because of the phantom jig), the poly function is robust to give the real center based on all the other properly-located positions on the other slices.

classmethod from_demo_images()

Construct a CBCT object from the demo images.

classmethod from_url(url: str, check_uid: bool = True)

Instantiate a CBCT object from a URL pointing to a .zip object.

Parameters

urlstr

URL pointing to a zip archive of CBCT images.

check_uidbool

Whether to enforce raising an error if more than one UID is found in the dataset.

classmethod from_zip(zip_file: str | ZipFile | BinaryIO, check_uid: bool = True, memory_efficient_mode: bool = False)

Construct a CBCT object and pass the zip file.

Parameters

zip_filestr, ZipFile

Path to the zip file or a ZipFile object.

check_uidbool

Whether to enforce raising an error if more than one UID is found in the dataset.

memory_efficient_modebool

Whether to use a memory efficient mode. If True, the DICOM stack will be loaded on demand rather than all at once. This will reduce the memory footprint but will be slower by ~25%. Default is False.

Raises

FileExistsError : If zip_file passed was not a legitimate zip file. FileNotFoundError : If no CT images are found in the folder

get_captured_warnings() → list[dict]

Retrieve the list of captured warnings.

property mm_per_pixel: float

The millimeters per pixel of the DICOM images.

property num_images: int

The number of images loaded.

plot_side_view(axis: Axes) → None

Plot a view of the scan from the side with lines showing detected module positions

refine_origin_slice(initial_slice_num: int) → int

Apply a refinement to the origin slice. This was added to handle the catphan 604 at least due to variations in the length of the HU plugs.

results_data(as_dict: bool = False, as_json: bool = False, by_alias: bool = False, exclude: set[str] | None = None) → T | dict | str

Present the results data and metadata as a dataclass, dict, or tuple. The default return type is a dataclass.

Parameters

as_dictbool

If True, return the results as a dictionary.

as_jsonbool

If True, return the results as a JSON string. Cannot be True if as_dict is True.

by_aliasbool

If True, use the alias names of the dataclass fields. These are generally the more human-readable names.

excludeset

A set of fields to exclude from the results data.

save_analyzed_image(filename: str | Path | BinaryIO, **kwargs) → None

Save the analyzed summary plot.

Parameters

filenamestr, file object

The name of the file to save the image to.

kwargs :

Any valid matplotlib kwargs.

to_quaac(path: str | Path, performer: User, primary_equipment: Equipment, format: Literal['json', 'yaml'] = 'yaml', attachments: list[Attachment] | None = None, overwrite: bool = False, **kwargs) → None

Write an analysis to a QuAAC file. This will include the items from results_data() and the PDF report.

Parameters

pathstr, Path

The file to write the results to.

performerUser

The user who performed the analysis.

primary_equipmentEquipment

The equipment used in the analysis.

format{‘json’, ‘yaml’}

The format to write the file in.

attachmentslist of Attachment

Additional attachments to include in the QuAAC file.

overwritebool

Whether to overwrite the file if it already exists.

kwargs

Additional keyword arguments to pass to the Document instantiation.

pydantic model pylinac.acr.ACRMRIResult[source]

Bases: ResultBase

This class should not be called directly. It is returned by the results_data() method.

Use the following attributes as normal class attributes.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field phantom_model: str [Required]

The model of the phantom used.

field phantom_roll_deg: float [Required]

The roll of the phantom in degrees.

field origin_slice: int [Required]

The slice number of the ‘origin’ slice; for ACR this is Slice 1.

field num_images: int [Required]

The number of images in the passed dataset.

field slice1: MRSlice1ModuleOutput [Required]

The results for the ‘Slice 1’ module

field slice11: MRSlice11ModuleOutput [Required]

The results for the ‘Slice 11’ module

field uniformity_module: MRUniformityModuleOutput [Required]

Results from the uniformity module

field geometric_distortion_module: MRGeometricDistortionModuleOutput [Required]

Results from the geometric distortion module

field sagittal_localizer_module: MRSagittalLocalizationModuleOutput [Required]

Results from the sagittal localizer module

field low_contrast_multi_slice_module: MRLowContrastMultiSliceModuleOutput [Required]

Results from the low contrast modules

pydantic model pylinac.acr.MRSlice11ModuleOutput[source]

Bases: BaseModel

This class should not be called directly. It is returned by the results_data() method.

Use the following attributes as normal class attributes.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field offset: int [Required]

The offset of the phantom in mm from the origin slice.

field roi_settings: dict [Required]

The ROI settings. The keys are the ROI names.

field rois: dict [Required]

The results of the left and right bar ROIs. The key is the name of the bar

field bar_difference_mm: float [Required]

The difference in bar positions in mm.

field slice_shift_mm: float [Required]

The measure shift in slice position compared to nominal.

pydantic model pylinac.acr.MRSlice1ModuleOutput[source]

Bases: BaseModel

This class should not be called directly. It is returned by the results_data() method.

Use the following attributes as normal class attributes.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field offset: int [Required]

The offset of the phantom in mm from the origin slice.

field roi_settings: dict [Required]

A dictionary of the ROI settings. The keys are the ROI names.

field rois: dict [Required]

A dictionary of the analyzed MTF ROIs. The key is the name of the ROI; e.g. Row 1.1.

field bar_difference_mm: float [Required]

The difference in bar positions in mm.

field slice_shift_mm: float [Required]

The measured shift in slice position compared to nominal.

field measured_slice_thickness_mm: float [Required]

The measured slice thickness in mm.

field row_mtf_50: float [Required]

The MTF at 50% for the row-based ROIs.

field col_mtf_50: float [Required]

The MTF at 50% for the column-based ROIs.

field row_mtf_lp_mm: dict[int, float] [Required]

A key-value pair of the MTF. The key is the relative resolution in % and the value is the lp/mm at that resolution

field col_mtf_lp_mm: dict[int, float] [Required]

A key-value pair of the MTF. The key is the relative resolution in % and the value is the lp/mm at that resolution

pydantic model pylinac.acr.MRUniformityModuleOutput[source]

Bases: BaseModel

This class should not be called directly. It is returned by the results_data() method.

Use the following attributes as normal class attributes.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field offset: int [Required]

The offset of the phantom in mm from the origin slice.

field roi_settings: dict [Required]

A dictionary of the ROI settings. The keys are the ROI names.

field rois: dict [Required]

A dictionary of the analyzed ROIs.

field ghost_roi_settings: dict [Required]

A dictionary of the ghost ROI settings. The keys are the ROI names.

field ghost_rois: dict [Required]

A dictionary of the ghost ROIs.

field psg: float [Required]

The percent signal ghosting.

field ghosting_ratio: float [Required]

The ghosting ratio.

field piu_passed: bool [Required]

Whether the percent integral uniformity passed the test.

field piu: float [Required]

The percent integral uniformity.

pydantic model pylinac.acr.MRGeometricDistortionModuleOutput[source]

Bases: BaseModel

This class should not be called directly. It is returned by the results_data() method.

Use the following attributes as normal class attributes.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field offset: int [Required]

The offset of the phantom in mm from the origin slice.

field profiles: dict[str, dict[str, float | LineSerialized]] [Required]

A dictionary of the profiles used to measure the geometric distortion. The key is the name of the profile.

field distances: dict [Required]

The lines measuring the ROI size. The key is the name of the line direction and the value is a string of the line length.

pydantic model pylinac.acr.MRSagittalLocalizationModuleOutput[source]

Bases: BaseModel

This class should not be called directly. It is returned by the results_data() method.

Use the following attributes as normal class attributes.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field profiles: dict[str, dict[str, float | LineSerialized]] [Required]

A dictionary of the profiles used to measure the geometric distortion. The key is the name of the profile.

field distances: dict [Required]

The lines measuring the ROI size. The key is the name of the line direction and the value is a string of the line length.

pydantic model pylinac.acr.MRLowContrastModuleOutput[source]

Bases: BaseModel

This class should not be called directly. It is returned by the results_data() method.

Use the following attributes as normal class attributes.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field offset: float [Required]

The offset of the slice in mm from the origin slice.

field slice_num: int [Required]

The slice number.

field spoke_settings: dict [Required]

A dictionary of the spoke settings.

field background_settings: dict [Required]

A dictionary of the background roi settings.

field spokes: dict [Required]

A dictionary of the spokes.

pydantic model pylinac.acr.MRLowContrastMultiSliceModuleOutput[source]

Bases: BaseModel

This class should not be called directly. It is returned by the results_data() method.

Use the following attributes as normal class attributes.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

field score: int [Required]

The total score across all slices (sum of all slice scores).

field low_contrast_rois: dict [Required]

Low contrast outputs.