pylinac-manteia 1.0.0
pylinac custom made by manteia
License: BSD License


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.. contents::
    :class: this-will-duplicate-information-and-it-is-still-useful-here


Introduction
------------

Pylinac provides TG-142 quality assurance (QA) tools to Python programmers in the field of
therapy and diagnostic medical physics.

Pylinac contains high-level modules for automatically analyzing images and data generated by linear accelerators, CT simulators, and other radiation oncology equipment.
Most scripts can be utilized with less than 10 lines of code.

The library also contains lower-level `modules & tools <http://pylinac.readthedocs.org/en/latest/core_modules.html>`_
for creating your own image analysis algorithms.

The major attributes of the package are:

* Simple, concise image analysis API
* Automatic analysis of imaging and performance metrics like MTF, Contrast, ROIs, etc.
* PDF report generation for solid documentation
* Automatic phantom registration even if you don't set up your phantom perfect
* Image loading from file, ZIP archives, or URLs

Installation
------------

Install via ``pip``:

.. code-block:: bash

    $ pip install pylinac

See the `Installation page <https://pylinac.readthedocs.io/en/latest/installation.html>`_ for further details.

Citation
--------

You may cite the pylinac library in publications; see `the paper <https://joss.theoj.org/papers/10.21105/joss.06001>`__ in the Journal of Open Source Software.
The citation string is:

Kerns, J. R., (2023). Pylinac: Image analysis for routine quality assurance in radiotherapy. Journal of Open Source Software, 8(92), 6001, https://doi.org/10.21105/joss.06001

And the BibTeX entry:

.. code-block::

  @article{Kerns2023, doi = {10.21105/joss.06001}, url = {https://doi.org/10.21105/joss.06001}, year = {2023}, publisher = {The Open Journal}, volume = {8}, number = {92}, pages = {6001}, author = {James R. Kerns}, title = {Pylinac: Image analysis for routine quality assurance in radiotherapy}, journal = {Journal of Open Source Software} }

Documentation
-------------

To get started, install the package, run the demos, view the API docs, and learn the module design, visit the
`Full Documentation <http://pylinac.readthedocs.org/>`_ on Read The Docs.

Features
--------

Low-level Tooling
~~~~~~~~~~~~~~~~~

DICOM, XIM & Image Loading
##########################

Load DICOM files, XIM, and TIFF images:

.. code-block:: python

  from pylinac import image
  from pylinac.core.image import XIM

  my_dcm = image.load("path/to/my/image.dcm")
  my_dcm.metadata.GantryAngle  # the GantryAngle tag of the DICOM file
  # these won't have the metadata property as they aren't DICOM
  my_tiff = image.load("path/to/my/image.tiff")
  my_jpg = image.load("path/to/my/image.jpg")

  my_xim_file = r"C:\TDS\H12345\QA\image.xim"
  xim_img = XIM(my_xim_file)

  # plot the image
  xim_img.plot()

  # see the XIM properties
  print(xim_img.properties)


Read more about DICOM and pixel loading: `Image Loading <http://pylinac.readthedocs.org/en/latest/topics/images.html>`_.
Read more about XIM images: `XIM Images <http://pylinac.readthedocs.org/en/latest/topics/xim.html>`_.

Image Manipulation
##################

Images can be manipulated in a variety of ways. This is helpful when combined with the loading utilities above:

.. code-block:: python

      from pylinac import image

      # load an image
      my_img = image.load("path/to/my/image.dcm")

      # rotate the image
      my_img.rotate(90)

      # flip the image
      my_img.flipud()

      # crop the image
      my_img.crop(pixels=50, edges=("left", "top"))

      # invert the image
      my_img.bit_invert()

      # normalize the array (max value = 1)
      my_img.normalize()

      # plot the image
      my_img.plot()

      # save the image back out to DICOM
      my_img.save("path/to/new.dcm")

Convert TIFF to DICOM:

.. code-block:: python

    from pylinac import image

    # load the TIFF image
    new_dicom = image.tiff_to_dicom(
        "path/to/my/image.tiff", sid=1000, gantry=90, coll=0, couch=0, dpi=400
    )

    # save out the FILE to DICOM
    new_dicom.save("path/to/new.dcm")

Compute Gamma
#############

Compute gamma between two arrays:

.. code-block:: python

    from pylinac import image

    # load the images
    img1 = image.load("path/to/image1.dcm")
    img2 = image.load("path/to/image2.dcm")

    # compute gamma
    gamma = image.gamma_2d(
        reference=img1.array,
        evaluation=img2.array,
        dose_to_agreement=1,
        distance_to_agreement=1,
        gamma_cap_value=2,
        global_dose=True,
        dose_threshold=5,
    )

    # plot the gamma map
    plt.imshow(gamma)

Compute gamma for 1D profiles:

.. code-block:: python

    from pylinac import profile

    # load the images and profiles
    img1 = image.load("path/to/image1.dcm")
    img2 = image.load("path/to/image2.dcm")
    mid_img1_profile = img1.array[img1.shape[0] // 2, :]
    mid_img2_profile = img2.array[img2.shape[0] // 2, :]

    # compute gamma
    gamma = profile.gamma_1d(
        reference=mid_img1_profile,
        evaluation=mid_img2_profile,
        dose_to_agreement=1,
        distance_to_agreement=1,
        gamma_cap_value=2,
        global_dose=True,
        dose_threshold=5,
    )

    # plot the gamma map
    plt.plot(gamma)

Compute Custom Metrics on Profiles
##################################

Pylinac comes with several built-in metrics that can be computed on 1D profiles, each of which can be
configured.

`Writing new metrics <https://pylinac.readthedocs.io/en/latest/topics/profiles.html#writing-plugins>`__ is also easy.

* `Left Penumbra <https://pylinac.readthedocs.io/en/latest/topics/profiles.html#penumbra-left>`__ using ``LeftPenumbralMetric``
* `Right Penumbra <https://pylinac.readthedocs.io/en/latest/topics/profiles.html#penumbra-right>`__  using ``RightPenumbralMetric``
* `FFF "Top" <https://pylinac.readthedocs.io/en/latest/topics/profiles.html#top-position>`__ using ``TopPosition``
* `Flatness (Difference) <https://pylinac.readthedocs.io/en/latest/topics/profiles.html#flatness-difference>`__ using ``FlatnessDifferenceMetric``
* `Flatness (Ratio) <https://pylinac.readthedocs.io/en/latest/topics/profiles.html#flatness-ratio>`__ using ``FlatnessRatioMetric``
* `Symmetry (Point Difference) <https://pylinac.readthedocs.io/en/latest/topics/profiles.html#symmetry-point-difference>`__ using ``SymmetryPointDifferenceMetric``
* `Symmetry (Point Difference Quotient) <https://pylinac.readthedocs.io/en/latest/topics/profiles.html#symmetry-point-difference-quotient>`__ using ``SymmetryPointDifferenceQuotientMetric``

Calculate the penumbra of a profile using the built-in ``LeftPenumbraMetric``:

.. code-block:: python

    from pylinac import profile
    from pylinac.metrics.profile import LeftPenumbraMetric

    # load the image and profile
    img = image.load("path/to/image.dcm")
    mid_profile = FWXMProfile(img.array[img.shape[0] // 2, :])

    # compute the penumbra
    left_penumbra = mid_profile.compute(metrics=[LeftPenumbraMetric(upper=80, lower=20)])

    print(left_penumbra)  # prints the penumbra value

Read more about 1D metrics: `Profiles & 1D Metrics <http://pylinac.readthedocs.org/en/latest/topics/profiles.html#metric-plugins>`_.

Convert Gantry, Collimator, Couch Coordinate Systems
####################################################

Convert gantry, collimator, and couch coordinates to and from each other:

.. code-block:: python

    from pylinac.core.scale import convert, MachineScale

    gantry = 0
    coll = 90
    couch = 45

    new_gantry, new_coll, new_couch = convert(
        input_scale=MachineScale.Varian,
        output_scale=MachineScale.IEC61217,
        gantry=gantry,
        collimator=coll,
        rotation=couch,
    )

Read more: `Coordinate Systems <http://pylinac.readthedocs.org/en/latest/topics/scale.html>`_.

Generate Synthetic Images
#########################

Want to generate images to test out your image analysis algorithms? Pylinac can do that.

Generate an AS1000 50x50mm, centered open field image at gantry 45:

.. code-block:: python

  from matplotlib import pyplot as plt

  from pylinac.core.image_generator import AS1000Image
  from pylinac.core.image_generator.layers import FilteredFieldLayer, GaussianFilterLayer

  as1000 = AS1000Image()  # this will set the pixel size and shape automatically
  as1000.add_layer(
      FilteredFieldLayer(field_size_mm=(50, 50))
  )  # create a 50x50mm square field
  as1000.add_layer(
      GaussianFilterLayer(sigma_mm=2)
  )  # add an image-wide gaussian to simulate penumbra/scatter
  as1000.generate_dicom(
      file_out_name="my_AS1000.dcm", gantry_angle=45
  )  # create a DICOM file with the simulated image
  # plot the generated image
  plt.imshow(as1000.image)


TG-51
~~~~~

`TG-51 & TRS-398 Absolute Dose Calibration <http://pylinac.readthedocs.org/en/latest/calibration_docs.html>`__ -
Input the raw data and pylinac can calculate either individual values (kQ, PDDx, Pion, etc) or use the
provided classes to input all measurement data and have it calculate all factors and dose values automatically.

Example script:

.. code-block:: python

        from pylinac import tg51, trs398

        ENERGY = 6
        TEMP = 22.1
        PRESS = tg51.mmHg2kPa(755.0)
        CHAMBER = "30013"  # PTW
        P_ELEC = 1.000
        ND_w = 5.443  # Gy/nC
        MU = 200
        CLINICAL_PDD = 66.5

        tg51_6x = tg51.TG51Photon(
            unit="TrueBeam1",
            chamber=CHAMBER,
            temp=TEMP,
            press=PRESS,
            n_dw=ND_w,
            p_elec=P_ELEC,
            measured_pdd10=66.4,
            lead_foil=None,
            clinical_pdd10=66.5,
            energy=ENERGY,
            voltage_reference=-300,
            voltage_reduced=-150,
            m_reference=(25.65, 25.66, 25.65),
            m_opposite=(25.64, 25.65, 25.65),
            m_reduced=(25.64, 25.63, 25.63),
            mu=MU,
            tissue_correction=1.0,
        )

        # Done!
        print(tg51_6x.dose_mu_dmax)

        # examine other parameters
        print(tg51_6x.pddx)
        print(tg51_6x.kq)
        print(tg51_6x.p_ion)

        # change readings if you adjust output
        tg51_6x.m_reference_adjusted = (25.44, 25.44, 25.43)
        # print new dose value
        print(tg51_6x.dose_mu_dmax_adjusted)

        # generate a PDF for record-keeping
        tg51_6x.publish_pdf(
            "TB1 6MV TG-51.pdf",
            notes=["My notes", "I used Pylinac to do this; so easy!"],
            open_file=False,
        )

        # TRS-398 is very similar and just as easy!

Planar Phantom Analysis
~~~~~~~~~~~~~~~~~~~~~~~

`Planar Phantom Analysis (Leeds TOR, StandardImaging QC-3 & QC-kV, Las Vegas, Doselab MC2 (kV & MV), SNC kV & MV, PTW EPID QC) <http://pylinac.readthedocs.org/en/latest/planar_imaging.html>`__ -
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.

Example script:

.. code-block:: python

      from pylinac import LeedsTOR, StandardImagingQC3, LasVegas, DoselabMC2kV, DoselabMC2MV

      leeds = LeedsTOR("my_leeds.dcm")
      leeds.analyze()
      leeds.plot_analyzed_image()
      leeds.publish_pdf()

      qc3 = StandardImagingQC3("my_qc3.dcm")
      qc3.analyze()
      qc3.plot_analyzed_image()
      qc3.publish_pdf("qc3.pdf")

      lv = LasVegas("my_lv.dcm")
      lv.analyze()
      lv.plot_analyzed_image()
      lv.publish_pdf("lv.pdf", open_file=True)  # open the PDF after publishing

      ...

Winston-Lutz Analysis
~~~~~~~~~~~~~~~~~~~~~

`Winston-Lutz Analysis <http://pylinac.readthedocs.org/en/latest/winston_lutz.html>`_ -
The Winston-Lutz module analyzes EPID images taken of a small radiation field and BB to determine the 2D
distance from BB to field CAX. Additionally, the isocenter size of the gantry, collimator, and couch can
all be determined *without the BB being at isocenter*. Analysis is based on
`Winkler et al <http://iopscience.iop.org/article/10.1088/0031-9155/48/9/303/meta;jsessionid=269700F201744D2EAB897C14D1F4E7B3.c2.iopscience.cld.iop.org>`_
, `Du et al <http://scitation.aip.org/content/aapm/journal/medphys/37/5/10.1118/1.3397452>`_, and
`Low et al <https://aapm.onlinelibrary.wiley.com/doi/abs/10.1118/1.597475>`_.

Features:

* **Couch shift instructions** - After running a WL test, get immediate feedback on how to shift the couch.
  Couch values can also be passed in and the new couch values will be presented so you don't have to do that pesky conversion.
  "Do I subtract that number or add it?"
* **Automatic field & BB positioning** - When an image or directory is loaded, the field CAX and the BB
  are automatically found, along with the vector and scalar distance between them.
* **Isocenter size determination** - Using backprojections of the EPID images, the 3D gantry isocenter size
  and position can be determined *independent of the BB position*. Additionally, the 2D planar isocenter size
  of the collimator and couch can also be determined.
* **Image plotting** - WL images can be plotted separately or together, each of which shows the field CAX, BB and
  scalar distance from BB to CAX.
* **Axis deviation plots** - Plot the variation of the gantry, collimator, couch, and EPID in each plane
  as well as RMS variation.
* **File name interpretation** - Rename DICOM filenames to include axis information for linacs that don't include
  such information in the DICOM tags. E.g. "myWL_gantry45_coll0_couch315.dcm".

Example script:

.. code-block:: python

    from pylinac import WinstonLutz

    wl = WinstonLutz("wl/image/directory")  # images are analyzed upon loading
    wl.plot_summary()
    print(wl.results())
    wl.publish_pdf("my_wl.pdf")

Starshot Analysis
~~~~~~~~~~~~~~~~~

`Starshot Analysis <http://pylinac.readthedocs.org/en/latest/starshot_docs.html>`_ -
The Starshot module analyses a starshot image made of radiation spokes, whether gantry, collimator, MLC or couch.
It is based on ideas from `Depuydt et al <http://iopscience.iop.org/0031-9155/57/10/2997>`_
and `Gonzalez et al <http://dx.doi.org/10.1118/1.1755491>`_.

Features:

* **Analyze scanned film images, single EPID images, or a set of EPID images** -
  Any image that you can load in can be analyzed, including 1 or a set of EPID DICOM images and
  films that have been digitally scanned.
* **Any image size** - Have machines with different EPIDs? Scanned your film at different resolutions? No problem.
* **Dose/OD can be inverted** - Whether your device/image views dose as an increase in value or a decrease, pylinac
  will detect it and invert if necessary.
* **Automatic noise detection & correction** - Sometimes there's dirt on the scanned film; sometimes there's a dead pixel on the EPID.
  Pylinac will detect these spurious noise signals and can avoid or account for them.
* **Accurate, FWHM star line detection** - Pylinac uses not simply the maximum value to find the center of a star line,
  but analyzes the entire star profile to determine the center of the FWHM, ensuring small noise or maximum value bias is avoided.
* **Adaptive searching** - If you passed pylinac a set of parameters and a good result wasn't found, pylinac can recover and
  do an adaptive search by adjusting parameters to find a "reasonable" wobble.

Example script:

.. code-block:: python

    from pylinac import Starshot

    star = Starshot("mystarshot.tif")
    star.analyze(radius=0.75, tolerance=1.0, fwhm=True)
    print(star.results())  # prints out wobble information
    star.plot_analyzed_image()  # shows a matplotlib figure
    star.publish_pdf()  # publish a PDF report

VMAT Analysis
~~~~~~~~~~~~~

`VMAT QA <http://pylinac.readthedocs.org/en/latest/vmat_docs.html>`_ -
The VMAT module consists of two classes: ``DRGS`` and ``DRMLC``, which are capable of loading an EPID DICOM Open field image and MLC field image and analyzing the
images according to the Varian RapidArc QA tests and procedures, specifically the Dose-Rate & Gantry-Speed (DRGS) and MLC speed (MLCS) tests.

Features:

* **Do both tests** - Pylinac can handle either DRGS or DRMLC tests.
* **Adjust for offsets** - Older VMAT patterns were off-center. Pylinac will find the field regardless.

Example script:

.. code-block:: python

    from pylinac import DRGS, DRMLC

    drgs = DRGS(image_paths=["path/to/DRGSopen.dcm", "path/to/DRGSdmlc.dcm"])
    drgs.analyze(tolerance=1.5)
    print(drgs.results())  # prints out ROI information
    drgs.plot_analyzed_image()  # shows a matplotlib figure
    drgs.publish_pdf("mydrgs.pdf")  # generate a PDF report

CatPhan, Quart, ACR, Cheese Phantom Analysis
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

`CatPhan, Quart, ACR phantom QA <http://pylinac.readthedocs.org/en/latest/cbct_docs.html>`_ -
The CBCT module automatically analyzes DICOM images of a CatPhan 504, 503, 600, 604, Quart DVT, and ACR CT/MR acquired when doing CT, CBCT, or MR quality assurance. It can load a folder or zip file that
the images are in and automatically correct for phantom setup in 6 axes.
CatPhans analyze the HU regions and image scaling (CTP404), the high-contrast line pairs (CTP528) to calculate the modulation transfer function (MTF), and the HU
uniformity (CTP486) on the corresponding slice. Quart and ACR analyze similar metrics where possible.

Features:

* **Automatic phantom registration** - Your phantom can be tilted, rotated, or translated--pylinac will register the phantom.
* **Automatic testing of all major modules** - Major modules are automatically registered and analyzed.
* **Any scan protocol** - Scan your CatPhan with any protocol; or even scan it in a regular CT scanner.
  Any field size or field extent is allowed.
* **Customize modules** - You can easily override settings in the event you have a custom scenario such as a partial scan.

Example script:

.. code-block:: python

    from pylinac import (
        CatPhan504,
        CatPhan503,
        CatPhan600,
        CatPhan604,
        QuartDVT,
        ACRCT,
        ACRMRILarge,
    )

    # for this example, we'll use the CatPhan504
    cbct = CatPhan504("my/cbct_image_folder")
    cbct.analyze(
        hu_tolerance=40,
        scaling_tolerance=1,
        thickness_tolerance=0.2,
        low_contrast_threshold=1,
    )
    print(cbct.results())
    cbct.plot_analyzed_image()
    cbct.publish_pdf("mycbct.pdf")

Trajectory & Dynalog Analysis
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

`Log Analysis <http://pylinac.readthedocs.org/en/latest/log_analyzer.html>`_ -
The log analyzer module reads and parses Varian linear accelerator machine logs, both Dynalogs and Trajectory logs. The module also
calculates actual and expected fluences as well as performing gamma evaluations. Data is structured to be easily accessible and
easily plottable.

Unlike most other modules of pylinac, the log analyzer module has no end goal. Data is parsed from the logs, but what is done with that
info, and which info is analyzed is up to the user.

Features:

* **Analyze Dynalogs or Trajectory logs** - Either platform is supported. Tlog versions 2.1 and 3.0 supported.
* **Save Trajectory log data to CSV** - The Trajectory log binary data format does not allow for easy export of data. Pylinac lets you do
  that so you can use Excel or other software that you use with Dynalogs.
* **Plot or analyze any axis** - Every data axis can be plotted: the actual, expected, and even the difference.
* **View actual or expected fluences & calculate gamma** - View fluences and gamma maps for any log.
* **Anonymization** - Anonymize your logs so you can share them with others.

Example script:

.. code-block:: python

    from pylinac import load_log

    tlog = load_log("tlog.bin")
    # after loading, explore any Axis of the Varian structure
    tlog.axis_data.gantry.plot_actual()  # plot the gantry position throughout treatment
    tlog.fluence.gamma.calc_map(doseTA=1, distTA=1, threshold=10, resolution=0.1)
    tlog.fluence.gamma.plot_map()  # show the gamma map as a matplotlib figure
    tlog.publish_pdf()  # publish a PDF report

    dlog = load_log("dynalog.dlg")
    ...

Picket Fence MLC Analysis
~~~~~~~~~~~~~~~~~~~~~~~~~

`Picket Fence MLC Analysis <http://pylinac.readthedocs.org/en/latest/picketfence.html>`_ -
The picket fence module is meant for analyzing EPID images where a "picket fence" MLC pattern has been made.
Physicists regularly check MLC positioning through this test. This test can be done using film and one can
"eyeball" it, but this is the 21st century and we have numerous ways of quantifying such data. This module
attains to be one of them. It will load in an EPID dicom image and determine the MLC peaks, error of each MLC
pair to the picket, and give a few visual indicators for passing/warning/failing.

Features:

* **Preset & customizable MLC configurations** - Standard configurations are built-in and you can create your own configuration of leaves if needed.
* **Easy-to-read pass/warn/fail overlay** - Analysis gives you easy-to-read tools for determining the status of an MLC pair.
* **Any Source-to-Image distance** - Whatever your clinic uses as the SID for picket fence, pylinac can account for it.
* **Account for panel translation** - Have an off-CAX setup? No problem. Translate your EPID and pylinac knows.
* **Account for panel sag** - If your EPID sags at certain angles, just tell pylinac and the results will be shifted.

Example script:

.. code-block:: python

    from pylinac import PicketFence

    pf = PicketFence("mypf.dcm")
    pf.analyze(tolerance=0.5, action_tolerance=0.25)
    print(pf.results())
    pf.plot_analyzed_image()
    pf.publish_pdf()

Open Field Analysis
~~~~~~~~~~~~~~~~~~~

`Open Field Analysis <http://pylinac.readthedocs.org/en/latest/field_analysis.html>`_ -
Field analysis from a digital image such as EPID DICOM or 2D device array can easily be analyzed. The module contains built-in
flatness and symmetry equation definitions but is extensible to quickly create custom F&S equations.

Features:
* **EPID or device data** - Any EPID image or the SNC Profiler.
* **Built-in F&S equations** - The common Elekta, Varian, and Siemens definitions are included
* **Extensible equations** - Adding custom equations for image metrics are easy

Example script:

.. code-block:: python

    from pylinac import FieldAnalysis, DeviceFieldAnalysis, Protocol

    fa = FieldAnalysis(path="myFS.dcm")  # equivalently, DeviceFieldAnalysis
    fa.analyze(protocol=Protocol.VARIAN)
    # print results
    print(fa.results())
    # get results as a dict
    fa.results_data()
    # plot results
    fa.plot_analyzed_image()
    # publish a PDF file
    fa.publish_pdf(filename="my field analysis.pdf")

Discussion
----------

Have questions? Ask them on the `pylinac discourse server <https://forum.pylinac.com>`_.

Contributing
------------

Contributions to pylinac can be many. The most useful things a non-programmer can contribute are images to analyze and bug reports. If
you have VMAT images, starshot images, machine log files, CBCT DICOM files, or anything else you want analyzed, upload them privately
`here <https://forms.gle/RBR5ubFvjogE9iC67>`_.

See the full `Contributing page <https://pylinac.readthedocs.io/en/latest/contributing.html>`_ for more details.