Changelog

v 3.17.0

Metrics

• Another metric is now available for 2D image analysis: GlobalDiskLocator. This metric will find a number of BBs/disks within an image. This is useful for finding BBs in an image without knowing where they might be. This is relatively efficient if there are multiple BBs in the image compared with using the DiskLocator class multiple times, even when the BB locations are known.

• The metric GlobalSizedFieldLocator is also available. This metric will find a number of open fields within an image. See Global Sized Field Locator for more.

Planar Imaging

• A new method is available for planar phantom analyses: percent_integral_uniformity(). This method will calculate the percent integral uniformity (PIU) over the low-contrast ROIs. This result will also be included in the results_data structure. This is not done for light/rad phantoms.

• If a phantom had a completely homogeneous array for an ROI, the results_data call would fail due to a division by 0 error. This has now been fixed such than an error is not raised. However, the resulting CNR and SNR will be a special case of float('inf'). This was encountered with a very low kVp analysis of the Doselab MC2 kV/MV phantom.

Picket Fence

• The Halcyon MLC configurations were incorrect and have now been fixed. Thanks to Dominic Rafferty for pointing this out. Previously, it was using a similar configuration as the TrueBeam out of lack of experience with the system. The new configuration was based on this paper.

Winston-Lutz

• Normal Winston-Lutz analyses (not multi-target/multi-field) can now plot a visualization of the BB position relative to the determined isocenter. After analyzing a WL set, call plot_location(). See Visualizing the Isocenter-to-BB.

CT

• A new class CIRS062M is now available. This will analyze the CIRS electron density phantom.

• The base class for cheese phantoms (CheesePhantomBase) now has a default implementation for results_data. Previously, it did not and required the user to create one when extending the phantom analysis to a new type.

• The TomoCheese phantom’s output from results_data has an additional key: rois. This is a dictionary of all the ROIs with the name of the ROI (usually the number) as the key. The data in the rois dict is the same information as in the roi_<n> elements. In retrospect, a simple dictionary is far more extensible when the number of ROIs vary. I.e. results_data()['rois']['1'] is the same as results_data()['roi_1']. The roi_<n> keys were left for backwards compatibility.

• A new class HypersightQuartDVT has been added that will analyze the Hypersight variant of the Quart phantom, which includes an additional water ROI.

ACR

• The z_position property for DICOM stacks (used in CT and MRI) was using SliceLocation if the tag existed and ImagePositionPatient[-1] if it did not exist. The SliceLocation tag however is apparently relative. This caused problems for the ACR MRI module on properly-acquired datasets. The ImagePositionPatient tag is now the primary lookup key and SliceLocation is only used if the former tag is unavailable.

Starshot

• The from_multiple_images method no longer requires the demo image. The demo image was just a placeholder to set up initial values.

Profiles

The following applies to the SingleProfile classes:

• Passing decreasing x-values to SingleProfile would usually result in an error because the measured width would be negative. An error will now be raised if the x-values are decreasing.

• Profiles that had non-integer increments in the x-values were not returning the right field values. I.e. when calling .field_data()['field values'] and non-integer x-values were passed at instantiation the values were not correct. Given the SingleProfile class is now frozen, it is recommended to not pass non-integer x-values and/or skip passing x-values to the profile.

The following applies to the <FWXM|InflectionDerivative|Hill>Profile classes:

• The same error of passing decreasing x-values as above was also detected in the new <FWXM|InflectionDerivative|Hill>Profile classes. Given these classes are the new standard, they have been fully fixed and can now handle decreasing x-values.

• Profiles that had non-integer increments in the x-values were not returning the right field values. I.e. when calling .field_values() and non-integer x-values were passed at instantiation the values were not correct. This has been fixed.

• The x_at_x method has been renamed to x_at_x_idx. A deprecation warning will be raised. The method will be removed in 3.18.

• The y_at_x and x_at_y and x_at_x_idx methods now all return a numpy array instead of a float.

• A new method has been added: field_x_values. This returns a numpy array of x-values that corresponds to the y-values that are returned when using field_values. This is useful for plotting the field values to the correct x-values.

• The SymmetryPointDifferenceMetric class’ plot method now uses “x” for the markers instead of “^” and “v”.

v 3.16.0

Planar Imaging

• results_data for planar imaging phantoms (Leeds, SNC kV/MV, Doselab MC2, etc) will now return a low_contrast_rois dict that contains relevant info for each low-contrast ROI.

Winston-Lutz

• The Winston Lutz module can now load CBCT datasets of a scanned BB. This is still experimental and may have bugs. Caution is warranted. See CBCT Analysis.

CBCT

• Passing expected HU values for ROIs is now much easier by passing a dictionary to the .analyze() method. See Custom HU values.

Profiles

• Profile analysis has been completely revamped. The existing SingleProfile class still exists and will not be deprecated immediately. It is frozen and will not receive updates.

• New profile classes were written that are more generalizable and extensible. These can be read about in the documentation below.

• The new profile classes also have a new plugin system for computing custom metrics. This allows for much more user-friendly, readable, and extensible code for both myself and users.

• A new documentation section has been added for profiles: Profiles. This section describes the various profile classes and how to use them.

• Internally, pylinac now uses these new profile classes. Existing calculations should be the same.

• Calculating custom profile metrics (such as symmetry or flatness) is now much easier using these new classes. The field analysis module will get a “v2” that will use these new classes and allow for these easy-to-write custom metrics.

Core

Image

• Similar to the new profile plugin architecture, 2D images also have a new plugin metric system. See the new documentation: Custom Image Metrics.

• The DicomImage class has a new class method: from_dataset(). This allows one to create a Dicom image from a pydicom dataset directly.

Image Generator

• The Simulator class and its subclasses has a new method: as_dicom(). This method will perform the same action as generate_dicom, but instead of saving to file, will return the pydicom Dataset.

v 3.15.0

Winston-Lutz

• For the MultiTargetMultiField Winston Lutz analysis, non-zero couch angles are not allowed. However, the check for this was limited to 0-5 degrees. Couch values that were on the other side of 0 were not being included. Couch angles between 355-5 degrees are now allowed as originally intended.

Planar Imaging

• The Doselab RLf light/rad phantom has been added as an analysis options: Doselab RLf.

• The IsoAlign light/rad phantom has been added as an analysis options: IsoAlign.

CT

• The catphan detection was failing if the phantom jig was touching the phantom at the center of a module. This has been fixed.

• A rounding error was fixed where the extent check was failing because of floating point rounding differences. This was causing an error to be raised when the scan extent was just slightly smaller (or appeared to be smaller) than the configuration extent.

ACR

• The ACR MRI phantom analysis was sometimes failing because the slice thickness check was failing. This was caused by a slightly inappropriate use of the profile module, causing instability under certain conditions. The MRI analysis should be more stable. Quantitative results should be the same.

VMAT

• The standard deviation for each VMAT segment is now available as the .stdev property of the segment.

  vmat = DRMLC(...)
  vmat.analyze(...)
  data = vmat.results_data()
  print(data.segments[0].stdev)  # first segment stdev
  

Core

• When saving a DICOM image, the pixel values were not “unscaling” the raw pixel values. I.e. the scaled values were being saved back to the DICOM file. If the image was then read in again, the values would be scaled twice. This has been fixed and DICOM images can now, for the most part, go “round trip” without the raw pixel values changing. An example is below:

  dcm_image = image.load("my_image.dcm")
  dcm_image.array  # this is scaled by the DICOM tags
  dcm_image.save(
      "my_output_image.dcm"
  )  # the pixel values were written back *as rescaled*
  dcm_image2 = image.load("my_output_image.dcm")
  dcm_image2.array  # this was scaling by the DICOM tags *again*
  

  Warning

  If the DICOM pixel values have been modified, such as concatenating images together, and the values are too high or too low for the original datatype (usually uint16), the values will be scaled to fit the datatype, with the maximum value being the max of the datatype. A warning will be raised when this occurs.

  Most of the time these operations are relative and absolute values don’t matter, but it’s still something to be aware of.

v 3.14.0

Planar Imaging

• An Elekta variant of the Las Vegas phantom has been added: ElektaLasVegas.

• The SSD parameter of now defaults to “auto” (.analyze(..., ssd="auto")). Previously, it was set to 1000mm. If “auto”, the phantom is first searched at 1000mm (for backwards compatibility). If the phantom isn’t found, it then searches at 5cm above the SID value. The 5cm is to account for the physical shroud of most EPID panels. If the phantom isn’t found at either of these locations an error is raised. In that case, the SSD should be provided manually, which was already the case previously.

CT

• CBCT, ACR CT/MR, and Quart analyses will now plot a “side view” of the phantom with lines to show where the modules were sampled. This will help visualize if the module slice selection was appropriate.

  

• A new check for the scan extent vs the configuration extent is now in place. This will check that the physical extent of the scan is large enough to include all the listed modules. If it’s not an error will be raised. This improves the error diagnosis when a scan did not include enough data.

  Note

  This applies to all CT-like algorithms including the ACR analyses.

ACR

• The ACR MRI algorithm now accounts for scans where slices do not abut. E.g. if the slice thickness is 5mm and the spacing between slices is 10mm.

• The ACR MRI high-resolution ROIs have been adjusted slightly to match the increasing test suite data, however, there are still some sets that do not perfectly align. We suggest following the Customizing MR/CT Modules section and adjusting the location as needed.

• The ACR MRI algorithm has a new parameter for analyze: echo_number. This lets the user pick an echo number if the acquisition was a dual echo scan. This is not required however. If the scan is dual-echo and no echo number is passed, the scan with the first echo number is selected. See the Choosing an MR Echo.

• The ACR MRI module classes can now be defined at the class-level, similar to the ACR CT. This was changed so that users can more easily change aspects of each module. See the Customizing MR/CT Modules section for more.

• The ACR MRI phantom MRUniformityModuleOutput had a typo. The property ghost_rois was actually spelled ghose_rois. Any code using this property should be updated to the correct spelling.

• The ACR MRI results_data() method will now return ROIResult instances instead of the raw HUDiskROI classes as before. This behavior already occurs for the catphan module and will thus make the results similar in structure.

Quart

• The Quart algorithm now measures the high-contrast resolution. It is accessible via the high_contrast_resolution method. It is given in the results and results_data methods as well.

  from pylinac import QuartDVT
  
  quart = QuartDVT(...)
  quart.analyze()
  high_res = quart.geometry_module.high_resolution_contrast()
  # or
  print(quart.results())
  # or
  high_res = quart.results_data().geometric_module.high_contrast_distance
  

Core

• The DicomImage class has two new properties available: z_location and slice_spacing. These both apply to CT/MR-like datasets.

• A new contrast algorithm, “Difference”, has been added. This can be used similar to RMS, Weber, etc. The reason this might be preferred is so that the resulting CNR value is closer to the default algorithm. See Contrast for more.

• Contrast values are now case-insensitive. This applies only if you are passing a string for the contrast method.

  from pylinac import CatPhan504
  from pylinac.core.contrast import Contrast
  
  ct = CatPhan504.from_demo_images()
  # equivalent
  ct.analyze(..., contrast_method="weber")
  ct.analyze(..., contrast_method="Weber")
  ct.analyze(..., contrast_method=Contrast.WEBER)
  

• Image classes (DicomImage, ArrayImage, FileImage) have a new method: rotate(). This is a wrapper for scikit-image that allows rotation of an arbitrary angle. Previously, only rotations of 90 degrees were allowed via the rot90 method.

• The library cached_property was dropped as a requirement since it was introduced in Python 3.8

• The utility function find_nearest_index in the acr module was moved to core.array_utils.

• The utility functions abs360 and wrap360 were moved from core.utilities to core.scale.

v 3.13.0

Warning

As stated in the previous version, v3.13+ will not support Python 3.7. Python 3.8+ is required, matching the PSF’s deprecation policy.

Planar Imaging

• The Leeds phantom has had its high-contrast ROIs adjusted to better fit the majority of phantoms encountered. Additionally, due to perceived differences in manufacturing, the high-contrast ROIs are now placed according to the center of the high-contrast block. The block is found after the phantom is found and the ROI configuration is adjusted about this center. We have noticed small differences between the block and the phantom center that are large enough to move the ROIs outside the line pairs. Even this however does not correctly place the ROIs all the time.

  Warning

  This may affect your MTF values, but so far it does not significantly change it if the ROIs were already correctly on top of the high contrast pairs. Images where the ROIs were mis-aligned with the line pairs should now better match, so any change should be between noise and a healthy improvement.

  Here are two images comparing the old positions to the new ones for an image that was previously not working:

  

  Previous Leeds ROIs on a poorly-fitting image

  

  New Leeds ROIs on the same image

  Here is the demo image, where the ROIs were working before, showing that the new locations still work.

  

  Previous Leeds ROIs on the demo image

  

  New Leeds ROIs on the demo image

  Note

  At this point in time it’s unclear where the variation is coming from. This is a best-fit solution to this variation. It’s possible there was a revision along the line or the placement tolerances are simply not very tight. We have evidence of other quality issues such as off-center low-contrast ROIs as well. If you know how these differences have come to be let us know!

  Finally, if you would like to keep the old ROI locations here is a gist with the old settings: https://gist.github.com/jrkerns/10b62aad7b38c210b9213761447f6155

• Related to above, the high-contrast ROIs have been reduced in size slightly so as not to spill out of the line pair area when there are small discrepancies of location. Testing did not change the MTF significantly from reducing the ROI size.

VMAT

• Three new parameters were added to the __init__ call: raw_pixels. ground, and check_inversion. These were added to allow users to avoid applying DICOM pixel correction and analysis manipulations. The reason for this is to match the results from other programs such as Doselab. See the new section Comparison to Doselab.

Core

• The DicomImage class constructor has a new boolean parameter raw_pixels. This was implemented for the above VMAT feature, but can be applied to any image if desired. This will not apply any pixel correction tags, and simpy load the values as saved in the DICOM file.

v 3.12.1

Contrast

• The contrast logic was refactored in pylinac 3.12.0. Unfortunately, this used the “vanilla” definition of weber (see Weber). Pylinac versions 3.11 and prior used the absolute difference of the numerator. Using the signed difference caused issues for existing users and workflows. This was unintentional. For backwards compatibility, the definition has been restored to the previous behavior.

v 3.12.0

Warning

This is the last version of pylinac that will support python 3.7 as it will reach end-of-life in July 2023. pylinac v3.13+ will support python 3.8 until October 2024 when python 3.8 is deprecated, etc. You can see the end-of-life chart here.

General

• The Contrast class which contains all the contrast algorithms is no longer an enum. It is now a simple class. This should have no effect on the user unless doing something like Contrast.MICHELSON.value. No changes are required on the users’s part for normal usage patterns.

• The :ref:<contrast> section has been enhanced to provide more details.

• A new core module contrast has been created. It contains all contrast-related items. Individual functions can now be called. See the above contrast doc section for examples.

• A new contrast option is available: Root-mean-square. It is available in the Contrast class like the existing options. E.g. leeds.analyze(..., contrast_method=Contrast.RMS)

• The Image section of the documentation has been enhanced with examples for using the core image behavior.

• The LowContrastDiskROI now provides properties for all contrast types, not just the selected one. I.e. <roi>.weber, <roi>.rms, <roi>.michelson, and <roi>.ratio. The existing <roi>.contrast still exists and will respect the passed contrast algorithm as before. This provides a way to compare other contrast algorithms without needing to re-analyze an image.

• There was a bug in the equate_images function where same-sized images were causing a zero division error. See here: https://github.com/jrkerns/pylinac/issues/446. Thanks to Luis for finding it!

• The crop method for images had a bug where passing pixels=0 would cause the array to diminish to shape 0 along the axes it was cropping.

Winston-Lutz

• The smallest BB allowed for detection is now ~1mm. This was previously 2mm, but the Varian Exact cube’s BB proved to be too small.

  Warning

  Setting the BB size to a very small value increases the chance of inaccurately detecting the BB when the BB is encased in a block.

CatPhan

• A new analyse parameter has been added thickness_slice_straddle. This is to explicitly control the slice combination technique for the slice thickness measurement. The default behavior is backwards-compatible so no changes are needed. Read more here: Slice Thickness.

• The parameter clear_borders was not being propogated to all submodules for catphan analysis. This is now fixed. Thanks to Chris Williams for finding and fixing the issue here: https://github.com/jrkerns/pylinac/issues/448.

v 3.11.0

General/Core

• The docs now use the furo theme. 🎉🎉

• A new function is available under the image module that converts a TIFF image to a simple DICOM format: tiff_to_dicom().

• Saving a PDF with the default logo will now additionally try to load the logo from the demo file repository if the file is not available locally. This occurs when using pylinac as a Package in RadMachine. This will now allow users to publish PDFs within RadMachine from a custom pylinac package.

• The demo files and PDF references have been removed from the git repository to make shallow clones smaller (e.g. downloading the repo from Github). Demo files are still available publicly as they always have been. No user changes required.

• Type errors should no longer occur for older version of Python.

Cheese Phantoms

• The cheese module has been refactored to be more generalizable so that new cheese-like phantoms can be easily created. Documentation on doing this has been added here.

• The hu attribute of the TomoCheese class has been renamed to module. This doesn’t affect typical use patterns.

Field Analysis

Warning

TL;DR: Symmetry will statistically go down and Flatness may rise slightly due to an off-by-one bug. For flat DICOM beams, this is insignificant.

A bug was fixed that caused the data considered to be the “field” to be off-by-one. The last element was not included. A visualization can be seen here: https://github.com/jrkerns/pylinac/issues/440. This caused BOTH symmetry and flatness to be affected when using FieldAnalysis and DeviceFieldAnalysis classes.

The value by which the symmetry and flatness will change depends a few factors. The largest factor is the resolution of the original image/dataset. For fields with high resolution, e.g. an AS1200 image, the effects will be smaller than for low-resolution datasets such as the Profiler. The gradient of the beam is also a large factor and FFF beams are the most affected. Interpolation does not have an effect.

To give an idea of when and how much the values will change, the change was performed on all the available data we have for open fields using DICOM and Profiler data and are presented in the table below. Approximately 400 datasets were evaluated.

For DICOM, only flat beams were available for analysis. For all analyses, the field ratio was 0.8, i.e. 80% field width.

Symmetry & Flatness changes in % after the bug fix by data and beam type

	Horizontal Symmetry	Horizontal Flatness	Vertical Symmetry	Vertical Flatness
DICOM (Flat)	0	+0.02	+0.01	+0.01
Profiler (Overall)	-0.20	+0.11	-0.26	+0.08
Profiler (Flat)	-0.16	+0.04	-0.09	+0.01
Profiler (FFF)	-0.80	+0.33	-1.26	+0.22
Profiler (Electron)	-0.08	+0.30	-0.52	+0.26

Positive values indicate the value went up, while negative values indicate the average went down.

The data shows that for DICOM data of flat beams, the effect was negligible. This makes sense since an off-by-one error for a field several hundred pixels wide will hardly register. It is the low-resolution datasets that show a difference. The values make general sense in that symmetry generally got better and flatness got somewhat worse. The right-most element was not being evaluated and generally speaking, that’s where the beam is starting to fall off. So flatness would likely stay the same or get worse, never get better. Symmetry generally improved because now the calculation is actually being done for the points that are truly opposite it across the CAX. Previously, a given element was being compared to its opposite one element closer to the CAX than it should have been.

FFF beams change the most and this can be attributed to the larger gradients causing larger differences in the calculation for both symmetry and flatness.

I understand that this may cause some consternation because the values are suddenly changing. However, I believe this is an improvement for the better since it is now more accurate. Additionally, symmetry values are generally getting better, which is a good thing. Flatness is usually not within our control either so changes here are bothersome, but know that your energy likely hasn’t changed. As always, measure PDD for true energy determination.

Even before this issue was raised, I have been working on refactoring the profile and field analysis modules to be easier to test as well as to extend. Stay tuned.

Thanks to Stephen Terry for pointing this out. We all get better together!

Winston-Lutz

• The WL module can now handle TIFF images. This is still provisional and may have bugs. Caution is warranted. See Using TIFF images.

Machine Logs

• Anonymization (anonymize()) of trajectory logs now includes the Metadata->Patient ID field in the .bin file for v4+ logs.

v 3.10.0

Machine Logs

• Trajectory Log CSV files now include the Jaw positions (X1, X2, Y1, Y2) as well as couch vert and couch pitch and roll if the couch was a 6D couch.

• Dynalog loading and Trajectory to_csv calls will now use UTF-8 encoding by default when reading/writing files.

Tomo

• The TomoCheese phantom can now accept density information via an roi_config parameter to analyze. This is completely optional. See Plotting density.

• A new method plot_density_curve is available. It requires that an ROI configuration has been passed per above.

Field Analysis

• The results_data from a DeviceFieldAnalysis was throwing an error previously. It will now return a DeviceResult, which is the same as a FieldResult save for ROI information since a device is set of profiles and does not have a ROI to speak of.

VMAT

Warning

The SEGMENT_X_POSITIONS_MM class attribute has been deprecated. Use the new roi_config parameter described below which is a replacement and more.

• The VMAT classes can now accept an ROI configuration dictionary to the analyze method. This replaces the SEGMENT_X_POSITIONS_MM attribute. This allows the user to pass in the same details as well as ROI names. See the updated Customizing the analysis Section.

• The VMATResult class has a new attribute: named_segment_data. This is the exact same data as segment_data except it is a dictionary keyed with the same names given in the roi configuration. Note that for backwards compatibility segment_data has been kept.

• Plotting the analyzed image now renders the names of the ROIs on the image by default along with the ROI deviation value. A new parameter controls this in the analyze method: show_text.

Winston-Lutz

• Analyzing kV WL images is now a bit easier. A new parameter open_field has been added to the .analyze method. Setting this flag to True will set the field center to the center of the image. See the new section: kV Analysis/Imaging-only iso evaluation.

• Very small BBs (<2mm) may not be found. Pylinac was never meant to handle BB’s smaller than this, but it may have worked. This is now hardcoded because pylinac will add a tolerance of +/-2mm to the input BB size. For inputs of 2mm BB size, this would lead to almost any ROI being detected. This is far more likely in phantoms where there is a block + BB vs a BB in air alone. Issues finding very small BBs were resolved with this hard lower limit.

  Warning

  It is very unlikely but this may break your analysis if your BB is very small (<1.5mm diameter). If you are affected please reach out on the forum and I will provide you a workaround.

• Winston-Lutz individual images will now show the X and Y component of the distance to the BB.

  

• A new key has been added to the WinstonLutzResult class (what is returned from results_data()) called keyed_image_details. This is a dict that lets the user key off of the axes values. E.g. data['G0C90B0'] will return the WinstonLutz2DResult for that image. This is in contrast to the existing image_details attribute that returns a simple list of the results. Images that are taken at the same axes values have a _{idx} appended to them. E.g. 3 images at the same position would look like G0C0B0, G0C0B0_1, and G0C0B0_2.

  wl = WinstonLutz(...)
  wl.analyze(...)
  results = wl.results_data()
  # knowing a priori I had a G90C0B0 image
  g90_image_data = results.keyed_image_details["G90B0P0"]
  # this is in contrast to having to iterate/search over the images
  g90_image_data = [r.gantry_angle == 90 for r in wl.images][0].results_data()
  

• The user can now pass the precision desired for the axes values using a new parameter: axes_precision. This lets the user decide how to round (if at all) the axes values. E.g. a gantry at 90.1 with axes_precision=0 will get rounded to 90. This can be useful with the above if using string keys to get details from a specific image as per the example above. E.g.:

  # Assume an image set with G=359.9
  
  wl = WinstonLutz(...)  # default, no rounding.
  wl.analyze(...)
  wl.results_data().keyed_image_details[
      "G359.9B0P0"
  ]  # we would have to know the delivery was at 359.9 and use the appropriate key
  
  # vs
  wl = WinstonLutz(..., axes_precision=0)
  wl.analyze(...)
  wl.results_data().keyed_image_details[
      "G0B0P0"
  ]  # whether delivered at G=359.9 or 0.1, this will always round to the nearest integer
  

  Note

  If you consistently deliver images on the “other side of 0” you may want to set axes_precision=0 which will round to the nearest integer. I.e. if you usually do 359.9 and want it be displayed as 0 do the above. This is helpful for the example above where even if the image was at 359.9 or 89.9, setting axes_precision=0 will let you use the same consistent key, such as data['G0C0B0'] rather than having to do data['G359.9C0B0'].

  Warning

  Due to this new axes precision, the default sorting MAY result in a different sorting of the images. This would only affect you if doing <wl>.images[idx]. If images are delivered on the “other side of 0” the image will bubble down to the bottom of the stack. I.e. an image delivered as G=359.9, B=0, P=0.1 will now bubble to near the bottom of the stack because the images are sorted first by gantry. Previously, the image would be rounded under the hood to be G=0, B=0, P=0. You can largely restore the prior behavior by passing axes_precision=0

Core

• Using pylinac.core.profile.stretch is now deprecated and will flag a warning on usage. The only current usage in the library is for load_multiples with the parameter stretch_each=True. This is unlikely to be used by end users and will be removed in v3.11. A new function of the same name is now available as pylinac.core.array_utils.stretch. For the normal use case where an array is to be stretched to have a new minimum and maximum, the result is the same. The use case stretch(..., fill_dtype=...) is deprecated as it is confusing and can potentially error out going from integer-like dtypes to float-like dtypes.

  Deprecated since version 3.11.

• A new method bit_invert has been added to the Image classes and subclasses as well as Profile classes and subclasses. This lets the user flip the image bit-wise. This is a better alternative than the existing invert as it takes into account the datatype. This will eventually become the default inversion method.

• A new method convert_to_dtype has been added to the Image and Profile classes and subclasses. This method will let the user pass a new numpy datatype and the array and values will be converted to that new datatype. Unlike a simple datatype casting however, this will keep the relative values to the same w/r/t the datatype max and min. E.g. an array of type uint8 has an element of value 100. Converting this to uint16 would result in a new value of 25,690 (100/255 = 0.392 = x/65535, x = 25,690). This is mostly helpful for combining images together but is a generally-helpful way of converting datatypes regardless of use case.

• The default value for a profile’s normalize method has changed from max to None. The same is true of an Image class’s normalize method. max and None do the same thing and max is still a valid argument. No change is needed by the user.

• Precision for axes values of LinacDicomImage s and subclasses are now more consistent and also allow the precision value to be set using a new parameter to the init call: axes_precision. Previously, any angle between 359-360 and 0-1 were considered “0”. However, this was not true for any other axes value. I.e. the above values were rounded, but no other rounding occurred. This would also only happen if using the automatic DICOM tag values. If the user passed in the axis values directly, they were used as-is. Now, the precision of all axes values can be set using the new axes_precision parameter. This will round the axes values to the given precision level. This will apply to both DICOM tag values as well as manually-passed values. The default behavior is to not perform any rounding. The only difference users may notice is that axes values about 359-1 are no longer rounded to 0 by default. To restore this type of behavior pass axes_precision=0 which will round 359.5+ to 0 and 359.5- to 359.

v 3.9.1

• A missing dependency in the built wheel tabulate was added. This only affected users who were trying to use the new WinstonLutzMultiTargetMultiField class. This can also be remedied by installing the package on its own: pip install tabulate.

v 3.9.0

General

• A new dependency has been added: tabulate. This is a Python-only library used for the new multi-target WL module. It is also a dependency of pandas, which will likely be a dependency of pylinac in the future.

CatPhan

• ROI details have been added to the CTP515Result class.

• Passing delta to save_analyzed_subimage would fail because the parameter was not being passed. This is now fixed.

Cheese

• A new module for “cheese” phantoms has been created. Only one routine currently exists: the TomoCheese, but more will be added later. Documentation for this new phantom can be found here: “Cheese” Phantoms.

Winston-Lutz

• Multi-Target, Multi-Field Winston-Lutz is now available. This means phantoms such as the SNC MultiMet can be analyzed. The algorithm is generalized however, and any reasonable configuration of BBs can be analyzed, meaning custom phantoms and new commercial phantoms are easy to make. Read the new section here.

• BBs with low density compared to surrounding material can now be analyzed via a new parameter low_density_bb. See the analyze() method.

Image Generator

• The generate_winstonlutz() utility script now accepts a field_alpha and bb_alpha parameter to set each item respectively.

Bug Fixes

• Certain XIM images were failing to render. This has been fixed.

v 3.8.2

• Using use_filenames with axis_mapping when instantiating Winston-Lutz would not respect the use_filenames flag. Now, use_filenames takes precedent. Normally, these should not be used together since they are both trying to set the axis values.

v 3.8.1

• The SNC phantoms (kV, MV, MV 12510) have had their ROI localization algorithms adjusted slightly. These phantoms are commonly used with the acrylic jig. That jig is very dense and often causes issues detecting the phantom separate from the phantom itself. This fix should remove the effect of the acrylic jig and allow any jig to be used, assuming the central ROI area is not occluded.

• Winston-Lutz axis-specific RMS calculations (“Maximum <Gantry | Collimator | Couch> RMS deviation”) from the results and results_data method calls were potentially erroneous if the maximum error was in a “Reference” image (gantry=coll=couch=0). Users are urged to upgrade if using these outputs. Note that the Maximum/Median/Mean 2D CAX->BB distances are unaffected.

v 3.8.0

General

• .xim files are now able to be opened. These are Varian-specific images usually taken during MPC or in service mode. Currently, it is not natively integrated into other analyses (e.g. analyzing a .xim picket-fence via PicketFence(...)), but depending on the usage it will have more mainstream support in the other modules. However, this will allow the user to export to other, common file formats like png, jpeg, and tiff as well as access the properties of the .xim image such as acquisition mode, MLC positions, etc. Read about it here: XIM images.

Image Generator

• The image generator module has had tests added to increase robustness as well as docstrings for the parameters.

• The RandomNoiseLayer has been adjusted to provide noise irrespective of the signal. Previously, the noise was dependent on the intensity of the pixel. To be consistent with the intention of applying dark current, the layer now adds noise consistently across the image. The default sigma value has been adjusted to be roughly the same as before.

Picket Fence

• The PDF generated when the orientation was up/down would sometimes occlude the text on the report. The image placement has been adjusted.

Winston Lutz

• The results_data() for a normal WL analysis now include the details of each image as well. I.e. Each WinstonLutzResult contains N WinstonLutz2DResult , one for each image, under the image_details key.

CBCT

• The MTF returned in results_data now includes 10-90 in steps of 10. Previously, only the 80, 50, and 30% were reported.

v 3.7.2

Field Analysis

• Performing a field analysis on a very small field (a few mm) would error out. To get around this, pass a larger slope_exclusion_ratio to analyze().

v 3.7.1

Planar Imaging

• The SNC MV 12510 ROIs were slightly downscaled. This caused an issue in contrast and CNR calculation being lower than reality by ~20%. It was introduced in v3.6. Users are encouraged to upgrade if using this specific phantom analysis.

v 3.7.0

General

• Logos can now be passed to any publish_pdf method to insert a custom logo (e.g. an institution logo). The size of the logo as it appears on the PDF is fixed.

Picket Fence

• The max_error_picket and max_error_leaf have been added to the results returned from <pf>.results_data().

• Elekta MLC options have been added to the MLC enum.

Planar Imaging

• Inversion detection for the Leeds and PTW EPID QC phantoms have been improved.

  Warning

  If you are passing invert=True to the analyze method for these phantoms double check the outcome. There is a good chance that parameter can be removed.

• An angle check has been added to the SNC kV phantom. Previously, the angle was hardcoded at 135 degrees per the manufacturer recommendation. It now checks the detected angle. If the value is 135+/-5 degrees the detected angle is passed, otherwise an error is thrown.

CBCT

• The phantom center detection was refactored. This was because the RadMachine jig was touching the CatPhan and causing detection issues on a handful of slices. Unfortunately, these few handful of slices were important to the detection algorithm as they occurred around the HU linearity module for the 604. The phantom center of each slice along the Z axis (in/out) is now detected by fitting a 1D polynomial for all the slices where the phantom is detected. I.e. x, y = f(z). This removes some of the error associated with having something touching the phantom for just a few slices. E.g. a clinic was using BBs on the side of their Catphan for alignment which was causing issues. Situations like these are more likely to be recovered from.

  Note

  This change is internal and should not cause issues; all tests passed without modification but there is a small possibility a dataset with some kind of interference will now analyze and cause detection issues.

v 3.6.3

CBCT

• Cropping a catphan dataset before analysis would result in an analysis failure.

• Datasets that had a deep-curve couch very close to the phantom (e.g. head cradles) would fail.

v 3.6.2

CBCT

• The phantom center detection was refactored. This was because the RadMachine jig was touching the CatPhan and causing detection issues on a handful of slices. Unfortunately, these few handful of slices were important to the detection algorithm as they occurred around the HU linearity module for the 604. The phantom center of each slice along the Z axis (in/out) is now detected by fitting a 1D polynomial for all the slices where the phantom is detected. I.e. x, y = f(z). This removes some of the error associated with having something touching the phantom for just a few slices. E.g. a clinic was using BBs on the side of their Catphan for alignment which was causing issues. Situations like these are more likely to be recovered from.

  Note

  This change is internal and should not cause issues; all tests passed without modification but there is a small possibility a dataset with some kind of interference will now analyze and cause detection issues.

v 3.6.1

• Fixed a bug with the SNC MV phantom analysis where the ROI scaling for the entire phantom was slightly over-sized.

v 3.6.0

Planar Imaging

• Planar analyses had a discrepancy in the number of low-contrast ROIs “seen” in the plot vs what was given in the numerical results. This is because the numeric results were still using the older method of contrast analysis, which does not take into account the ROI size. The plot uses the newer method of Visibility. The quantitative results have been changed to use the visibility.

  Warning

  Your detected ROIs may be different moving forward, although the visibility default value in the analyze() method was chosen to be as close as possible to the existing contrast results, meaning that the ROIs should be similar out of the gate. If you’d like to still use the older metric it is still available:

  num_rois_simple_contrast = sum(
      roi.passed for roi in my_planar_phantom.low_contrast_rois
  )
  

Picket Fence

• The max_error_leaf property will now return an int, where previously it returned a single-element list for classic/combined analysis. I.e. doing <pf>.max_error_leaf used to return something like [42] but now returns 42. The signature type has also been updated to reflect this. This change allows the user to do this: <pf>.plot_leaf_profile(leaf=<pf>.max_error_leaf, picket=<pf>.max_error_picket). Previously, this would fail because the max_error_leaf was a list and the user would have to do ...leaf=<pf>.max_error_leaf[0]....

  Note

  Users that perform “separate” analysis are unaffected (.analyse(... separate_leaves=True).

Winston-Lutz

• The BB-finding algorithm has been hardened and can now find the BB even in the presence of artifacts such as the couch. This most often applies when very large fields are used. A side effect is that the BB-finding algorithm is also now faster and reduces analysis time up to 50%.

• The machine coordinate system/scale can now be given as a parameter. This will affect the BB shift vector and shift instructions. The default scale is IEC61217, which was the implicit default previously and is thus backwards-consistent. A small section has been added here: Passing a coordinate system.

• Due to the above change, there is no need for the couch_angle_varian_scale property of the WinstonLutz2D class. It has been removed to reduce confusion. Use the new feature above if you had been using/overriding this property.

• A bug was fixed where repeating analysis would give different results. This was because the image pre-processing was being performed each time .analyze() was called. This only applies if you perform .analyze() more than once on the same instance.

Catphan

• The Catphan 600 MTF algorithm had a bug of not using the correct “windows” of peaks/valleys when finding the MTF. Each CatPhan model’s high-resolution pairs are at slightly different angles. The 600 was inadvertently using the 504’s window positions. This has been updated to use the correct windows. The problem can be visualized below, where the red lines show each MTF resolution window previously, vs the green which is the updated window. The result is that MTF will now be lower than previously because the old windows were sometimes including a peak of the previous line pair, causing the apparent MTF value to be higher than it really was.

  Warning

  MTF values for the CatPhan 600 will now be ~15% lower than previously due to this bug fix.

  

Field Analysis

• A visual bug was fixed with the blue ROI display. The horizontal ROI was being offset slightly based on the vertical width. This only applied when the width of the horizontal and vertical parameters were different and is completely visual. No quantitative results are affected.

• The statistics from the central area within the horizontal and vertical windows is now reported. I.e. the stats from the pixel values within the overlap of vertical window and horizontal window are now available like so:

  fa = FieldAnalysis(...)
  fa.analyze(...)
  results = fa.results_data()
  results.central_roi_max
  results.central_roi_mean
  ...
  

  The stats are also available directly from the FieldAnalysis instance:

  fa = FieldAnalysis(...)
  fa.analyze(...)
  fa.central_roi.mean
  fa.central_roi.max
  ...
  

  If the width is 0 for both parameters a 2x2 matrix is sampled around the central pixel.

Core

• The RectangleROI class now has additional statistical results available computed from the pixel array: .mean, .std, .min, .max.

v 3.5.0

Planar Imaging

• Older SNC MV phantoms (observed as model #1251000) can now be analyzed with the new SNCMV12510. They have a slightly different size and ROI locations but appears to be functionally the same.

• The IBA Primus A phantom is now supported.

• Planar image analyses now take into account the image SAD; previously this was assumed to always be 1000mm. This only affects users with non-standard SADs such as proton gantries. Linac-based users should see no difference.

• Most planar phantoms will now show an “x” marker on the analyzed image showing the detected center of the phantom. This can help in evaluating the algorithm’s accuracy in phantom detection.

• Two methods, window_floor and window_ceiling, were added to the image analysis classes. This lets the user define the min and max values of display for plotting the image. These are convenience functions only and currently only affect the Primus phantom, but will likely be adopted for the other phantoms.

Core

• A source-to-axis sad property was added to the DicomImage class. This property looks up the “RadiationMachineSAD” tag. This was added because non-1000mm SADs are being encountered.

• The dpmm property now takes into account the SAD (see above). Previously, the SAD was assumed to be 1000mm. For Linac users there will be no visible change.

Bug Fixes

• The PDFs from planar imaging analyses would have the text collapsed to one line. This has been fixed.

• The planar imaging module was starting to use scikit-image attributes that were introduced in 0.19 inadvertently. This has been fixed. For previous versions, update scikit-image to v0.19 or higher.

v 3.4.0

Picket Fence

• There is now a skew() method, returning the skew of each picket.

Planar Imaging

• A new class for analyzing older Leeds phantoms that have a blue label on the back (vs the red ring) has been added: LeedsTORBlue

Winston-Lutz

• The cax2bb_distance() method can now accept mean for the metric.

• The cax2epid_distance() method can now accept mean for the metric.

• The results_data() now includes the mean CAX->BB distance and mean CAX->EPID distance.

CT

• The CatPhan600 detection has changed to use the bottom Air ROI and the Teflon ROI (just to the right of bottom air ROI). This is because the top air ROI can sometimes (and purposefully) contains a water vial. When inserted, the water vial makes angle detection untenable using this ROI. The result should be <0.5 degrees difference from previous versions, however, it was never 0. The only result this should affect (other than the angle) is the very small ROI low-contrast detection values, as it was found that even with a few tenths of degrees, a single pixel or two would be included or excluded compared to the previous algorithm. This is really a reflection of the sensitivity of the noise, which should likely use a global noise value instead of the local noise.

• Related to above, the same class now will have an extra ROI “Vial” with an expected value of 0. However, if the detected ROI is closer in value to air than water, the ROI will not be evaluated. This gives backwards-compatibility with existing scans that don’t use the vial. I.e. if you don’t use the water vial nothing should be different.

v 3.3.0

Core

• 1D gamma evaluation between two profiles can now be performed via the new gamma() function.

• Resampling of SingleProfile can now be done with the resample() function. This allows the user to resample a profile after it’s already been created to achieve a specific interpolation resolution.

Field Analysis

• The DeviceFieldAnalysis class has been removed. Only the SNC Profiler was supported and even then it didn’t work very well. Further, RadMachine is utilizing profile/file parsing that will be brought to pylinac. This new generalized scan parsing will eventually restore similar behavior, but for now it is deprecated. Sorry ☹

Planar Imaging

• The SNC FSQA light/rad phantom is now able to be analyzed. Docs can be found here: SNC FSQA.

Bug Fixes

• #1705 - PDDx for measurements with no lead and PDD < 75 would calculate using the interim equation of 1.267*pdd - 20. This should return the PDD if the PDD<75. This will result in ~0.3% difference for 10MV with PDD just under 75. Depending on the chamber you’re using, this could result in a difference of kQ by ~0.0005.

• The planar imaging detection routines have slightly improved robustness. This was caused by using scikit-image’s major_axis_length property, which is somewhat more finicky than other properties. The detection now uses the area_bbox property which appears to curb some edge-case phantom analyses. This should not affect results for images that are already detected properly.

• Linear and Spline interpolation for SingleProfile contained an error in how it was interpolating data (it wasn’t) at the very edges. The problem is that if we upsample, the left and right ends are not equally sampled. E.g. upsampling a 3-pixel array (0, 1, 2) by 10 normally results in ~20 elements. You interpolate between 0 and 1, and 1 and 2. The first issue is that you do not have a simple X proportion of elements (3 * 10 = 30 but we get 20). Additionally, if these are pixels they have a finite, physical size and technically those values are at the center of the pixels. Thus, you actually need to sample beyond the left and right edges. In the above case you’d really need to sample from approximately -0.5 to 2.5 to get ~10 pixels for each original pixel. We also need to offset the x-values to be back to 0 again from -0.5. We solve this by offsetting the new x-values by a proportion of the sampling ratio. A ratio of 1 (identical sampling) should not have any offset and return the same values. As the ratio goes up, we approach the limit of 0.5 pixels. This follows a proportional relationship with the ratio. The end result actually does not change much in the way of measurement results as nearly every previously-existing tests passed. 2 out of ~50 field analysis tests had a slightly different penumbra measurement and 1 had a slightly changed vert symmetry.

v 3.2.0

General

• The codebase as been blackened. This does not affect functionality but will change code line numbers when comparing to previous versions.

• All internal imports have been converted to relative imports. This does not change functionality but does mean that the pylinac repo can now be easily forked and included as a package in RadMachine. This would typically be done to use a pinned version as the embedded pylinac in RadMachine is updated regularly.

• Pylinac has dropped support for Python 3.6, following the security support timetable. 3.7 support will drop in the next version after June 2023.

Planar Imaging

• The IMT L-Rad light/rad phantom is now able to be analyzed. It is part of the planar imaging module. Docs are here.

• The SI QCkV phantom was accidentally analyzing the reference/background ROI. This resulted in a contrast of 0 for the first ROI all the time. This has been removed from the results. Calculations using the average contrast will be affected. Existing ROI analysis values are not affected, but will be off by one if accessing the roi directly. I.e. “roi 3” is now “roi 2”, etc as the reference ROI was originally “roi 1”.

CT

• The Quart phantom can now be analyzed. Docs are here.

• The ACR CT and ACR MRI Large phantom can now be analyzed. These should be considered experimental and subject to breaking changes in future versions until substantial data/tests can be had. Docs are here.

• The catphan and quart classes have a new attribute: hu_origin_slice_variance. This allows users to override the acceptable variance used to find the HU linearity module. Existing functionality is not changed.

Winston-Lutz

• Axis data can now be passed in as a dictionary. This is mostly for Elekta users. This is an alternative to renaming files. See the updated section on passing in data.

• The ImageManager class has been removed. The functionality has been absorbed into the existing classes.

v 3.1.0

General

• For the picket fence, field analysis, and planar imaging modules, image keyword args can now be passed on instantiation. This is helpful for images that don’t have even basic tags like DPI/DPMM or SID. The keyword args that can be passed are those consumed by load().

  from pylinac import PicketFence
  
  path = ...  # very sad image that has no DICOM tags for DPI or SID
  pf = PicketFence(path, image_kwargs={"dpi": 184, "sid": 1500})
  pf.analyze()
  ...
  

• Matplotlib keyword args can now be passed to most modules that save a figure, allowing the user to specify the figure size and other parameters

  from pylinac import LeedsTOR
  
  leeds = LeedsTOR.from_demo_image()
  leeds.analyze()
  leeds.plot_analyzed_image(
      ..., figsize=(10, 10)
  )  # figsize is passed to matplotlib to generate a figure of said size
  

• Pylinac is now compatible with scikit-image 0.19

Picket Fence

• Individual leaf errors (on each side of the picket) can now be analyzed. New parameters were introduced to add this and related information needed to compute this. For backwards-compatibility this is set to False. See the picket fence documentation and analyze() parameter descriptions, specifically the separate_leaves and nominal_gap_mm parameters.

• Algorithm benchmarking has been added to the PF docs.

Planar Imaging

• The Standard Imaging FC-2 light/rad phantom is now able to be analyzed.

• The Las Vegas contrast analysis has been reverted to pre-3.0 behavior. This is because there is no reference position like there is for other phantoms. Mistakenly, the “reference” was set to the first ROI, but because visibility is dependent on both ROI size and contrast for Las Vegas, the background ROIs outside the milled disc areas have been restored.

• Plots can now be separated. Use .plot_analyzed_image(... split_plots=True). This will now show multiple matplotlib plots.

• You may save analyzed images to individual files. I.e. when splitting per above each plot will be saved to a separate file. See save_analyzed_image(). This will return the filenames on disk.

• Finally, you may save split plots to stream using to_streams. This will return a dictionary of the plot name (image, low contrast, …) and stream.

Field Analysis

• The plotting behavior described above for planar imaging is also true now for field analysis.

• Passing a string for centering, interpolation, edge and normalization methods is now an option. E.g. <field analysis instance>.analyze(..., centering='manual', ...).

CBCT

• The catphan module can now accept a list of paths on instantiation. E.g. Catphan504([path1, path2, path3, ...]).

Winston-Lutz

• The plot_summary() method now allows you to pass a figure size.

• With the above, save_summary() also allows you pass the figure size.

Bug Fixes

• #1464 - Off-center CBCT could give faulty slice thickness numbers. The row/col were inverted for the sampling, meaning the left ROI was really sampling the top ROI and vic versa. For an on-center catphan, this would not change the results. Results appear to only have changed if the catphan was 5+ mm off-center. The change of outcome for offsets large than this are indeterminate but likely you weren’t getting good results to begin with under that scenario, so it should only improve.

• #405 - The picket fence results() were reporting the wrong picket for the maximum error. It was selecting from a wrongly-ordered list, instead giving the picket with the least error. Note that the maximum error value was not incorrect, only the reported picket.

• PDF generation for field analysis with a device (i.e. SNC Profiler data) would fail as there was no true image. The PDF generation simply skips the image plotting for devices now.

• #416 - The CBCT docs now correctly state that the slice thickness is based on all the wire profiles, not just the longest two.

• #408 - The Dynalog isoplane correction factor was changed from 1.99614 to 1.96078 to match Varian documentation. This should have a difference of <0.3% of positioning error and should not affect gamma (since the errors canceled out) but would affect comparison to a TPS fluence.

v 3.0.0

Warning

Version 3.0 contains numerous breaking changes (hence the increment). Review the changelog before upgrading.

General

• A new method, results_data has been added to most modules (excluding calibration and log analyzer). This is complementary to results. results_data will return a dataclass or dictionary, which includes pretty much everything in results as well as metadata (e.g. pylinac version). This dictionary will be useful for APIs and referencing certain information that will be more stable across versions 🤞. Thanks to @crcrewso for the suggestion.

• Nearly all major modules can now handle file objects and streams (Dynalogs cannot yet). These may be passed as would a disk file path.

  with open("mystarshot.dcm", "rb") as f:
      star = Starshot(f)
      ...
  

• Enums have been added in numerous places to mostly replace string options. E.g. for picket fence instead of specifying “up-down” as the orientation literally, the user now has the option to pass an Enum:

  from pylinac.picketfence import PicketFence, Orientation
  
  pf = PicketFence(...)
  pf.analyze(..., orientation=Orientation.UP_DOWN)  # specify the orientation via an Enum
  

  The advantage here is two-fold: 1) introspection/autocompletion using your IDE vs remembering/looking up documentation, 2) easier to generate documentation as now we can point to a class with the options. Note however that string options are still available for backwards compatibility.

  pf = PicketFence(...)
  pf.analyze(
      ..., orientation="Up-Down"
  )  # specify the orientation via a string. Works the same as above
  

  Assuming you’d like to use the string version instead of using enums all over, how do you know the options? Go to the auto-generated documentation of the enum! =) E.g. Orientation.

  Note

  Relying on your IDE is a good idea. A smart one can warn you of incompatible data types.

• The github repo has been “minified” by removing excess demo files and also removing the basic test files. These files are now cloud-hosted and downloaded as needed. This makes git clone significantly faster since the repo size has been reduced from ~1.6GB to ~60MB. Note that this does not affect the pip package since that package already had most of this excess data removed.

• Image inversion detection has changed slightly. Some images have proper tags such as rescale slope and intercept. If they do have the tags, they are applied and no inversion is applied. If they do not have the tags, an inversion is then applied. Previously, the tags were applied if they were there, and nothing if not and inversion was ALWAYS applied. This should result in better inversion defaults for images from different machines/platforms and fewer invert=True additions. See Images.

• A CONTRAST enum has been added that can be used for low-contrast analysis of planar images and CBCT images. See Contrast.

  from pylinac.core.roi import Contrast
  
  leeds = LeedsTOR(...)
  leeds.analyze(..., low_contrast_method=Contrast.WEBER)
  ...
  
  ct = CatPhan504(...)
  ct.analyze(..., contrast_method=Contrast.MICHELSON)
  ...
  

• The algorithm for low contrast contrast constant detection has changed slightly. See Visibility. This means the # of detected low-contrast ROIs may change for cbct. You may pass in a contrast technique per above and also a visibility threshold. See the .analyze method of the respective class.

• The contrast-to-noise property of the LowContrastDiskROI now uses contrast/stdev, where contrast is defined/chosen per above.

• Several LowContrastDiskROI properties have been deprecated such as contrast_constant. Use visibility instead. The old properties still work but come with a deprecation warning and will be removed in a future release.

• #270 Pylinac had a memory leak that was apparent when running on a server. This was caused by old instances being held in memory from and incorrect usage of the lru_cache. This has been fixed.

• Documentation about topics has been added.

• Documentation benchmarking several algorithms has been added. See the “Benchmarking the Algorithm” section for vmat, winston-lutz, and starshot modules. Picket fence will come soon.

Note

Upgrade Hints

Besides the above notes and any module-specific steps, due to the modified method of loading images and inversion, other downstream modules may be affected. This means that some images that needed invert=True before may not need it, and some images that previously worked may need an invert=True. So generally, if the image fails when it passed with previous versions, try adding/removing forced inversion first. This should only be an issue for older images. Images generated on new linac platforms should be handled just fine.

Dependencies

A new dependency has been added: cached_property.

Field Analysis (previously Flatness/Symmetry)

Danger

This release introduced numerous breaking changes to this module. Existing code will break.

• Two classes are now offered: FieldAnalysis and DeviceFieldAnalysis.

• Many, many options were added to the analyze() method. See below and the documentation page for all the details.

• The flatsym module has been renamed to field_analysis to reflect the generalized nature of the module. Many thanks to Alan Chamberlain (@alanphys) for suggesting and doing the initial implementation for this. This also introduced some early support for NCS-33 , which gives guidance on FFF beams.

• From the above report, a “top” position as well as field slope values are calculated for FFF beams. See FFF fields.

• The new module can handle files from devices, specifically the SNC Profiler.

• Extensibility was greatly enhanced. Users can now easily add their own custom analysis routines to the module. See Creating & Using Custom Protocols.

• New options for Centering, Normalization, Edge detection, and Interpolation were introduced. Each of these can be granularly controlled.

VMAT

• Leveraging the new profile module, the field edge detection has been improved and can detect “wide-gap” or overlapping ROIs more robustly.

Calibration

• #353 The bounds for most functions/methods have been converted to constants. This lets users override the default values should they wish it.

Winston-Lutz

• #366 #333 The analysis will fail if the BB is not detected within 20mm of the center of the field. This should help artifacts from being detected.

• The Winston-Lutz analysis has added an .analyze routine, just like all other major modules.

• #358 The user can now pass in an expected BB size. This will help analyses with smaller or very large BBs.

• The WLImage class has been renamed to WinstonLutz2D. This is to clarify usage as now documentation has been expanded to show using WL with a single image.

Note

Upgrade Hints

• Replace any uses of axis constants (GANTRY, COLLIMATOR, etc) with the enum version: Axis.GANTRY, …

• Add a <instance>.analyze(...) call to each WinstonLutz instantiation.

• Set the BB size if needed. The algorithm has a default of 5mm and is relatively forgiving (+/-2mm), but for very small BBs you should set it lower than the default of 5mm. E.g. .analyze(bb_size_mm=3)

• If using WLImage, rename to WinstonLutz2D. Add .analyze() calls as well as appropriate.

I/O

• An SNC Profiler file parser has been added: pylinac.core.io.SNCProfiler. This can be used standalone, but since the data is not encoded to begin with it’s really about handling it as a tool for other modules. Currently, this is being used in the Field Analysis module.

  from pylinac.core.io import SNCProfiler
  
  snc = SNCProfiler("path/to/data.prs")
  snc.data  # ndarray
  x, y, pos, neg = snc.to_profiles()  # returns SingleProfiles
  

Planar Imaging

• Sun Nuclear kV and MV phantoms have been added to the arsenal.

• The PTW EPID QC phantom has been added to the arsenal.

• The Standard Imaging QC-kV1 phantom has been added to the arsenal.

• #339 The user can now pass an SSD value for their phantoms. The default is 1000mm, but if you set it on your panel you can pass something like 1400mm.

• The phantom-finding algorithm has been refactored to be more extensible. This does not affect normal users, but reduces the amount of duplicate code. It also makes adding new phantoms easier.

• Generally speaking, the phantoms should all be roughly centered along the CAX. Previously, the phantom could be offset from the CAX. Due to general difficulty in finding the phantom reliably for the majority of clinics, I am enforcing this as a restriction. This shouldn’t affect too many people but should make the ROI-finding algorithm better.

• The low contrast background ROI (i.e. the base level of contrast) has been adjusted for some phantoms (QC-3 and Doselab). Previously, it could either be in a “dark” region, meaning a high-attenuation area, or a “light” region, meaning a low-attenuation area. This has been standardized for all phantoms to be the “light” region. A new doc page for contrast has been added to the online documentation.

• 3 more high-contrast ROIs have been added to the LeedsTOR to help get rMTFs below 50%.

• The SI QC-3 analysis will now handle both typical orientations (gantry 0 and 90), where the “1” is pointing toward the gantry. This produces two different angles. The phantom should still be angled at 45 degrees from a cardinal angle.

Note

Upgrade Hints

• If you have defined any custom phantoms, read the new documentation: Creating a custom phantom. Your existing code will likely NOT break but the new format is much easier for extensibility.

• Evaluate the new contrast values versus your existing ones for the QC3 and Doselab phantoms. Moving forward, the above definition of contrast ROI-picking will be used.

• For the LeedsTOR, check the MTF of an existing image. Since adding more high-contrast ROIs, the rMTF may change if you were using a value below the lowest detected value. You do/will get warnings about being below the minimum MTF if you already do so.

Picket Fence

Overall, most code shouldn’t need to change from v2.5. From v2.4 or below, the way MLCs are passed and used has changed.

• Wide-gap tests should now work better than before. However, please read the Acquiring good images section.

• The mlc parameter of the PicketFence constructor has been changed to use an Enum or MLCArrangement: MLC. See the Customizing MLCs section for more.

• A crop_mm parameter has been added to the PicketFence constructor. This is for cropping the edges of images. The primary cause of issues with the PF module is dirty/noisy/dead edges.

• The orientation parameter of the analyze method has been changed to use an Enum or str: Orientation.

• A required_prominence parameter has been added to analyze. This is to prevent multiple peaks detection for wide-gap images.

• A fwxm parameter has been added to analyze. This is to allow the user to set the FWXM height to use for the MLC kiss profile.

• A results_data method has been added. See General above.

• The colored rectangular overlay has been reduced in size slightly.

CBCT

• A contrast parameter was added to analyze. This uses an Enum and has 3 options; see Low contrast.

• A visibility_threshold parameter was added and is a replacement for cnr_threshold. See the General section and Visibility. Compared to cnr_threshold, the default value will give approximately the same results for # of low-contrast ROIs “seen”. About 30% of the test datasets had a different # detected, but the detected vs expected number were either too high or too low, so there was no single value to perfectly replace the default cnr_threshold value.

• With the above, the contrast calculations have been standardized. Compared to previously, the contrast and contrast-to-noise now use the same equation for contrast. Previously, contrast was using the Michelson equation and contrast-to-noise was using the Weber definition. Now, contrast is always calculated with the definition given during instantiation.

• ROI colors for low contrast ROIs that are “seen” have changed from blue to green to match other modules.

Note

Upgrade Hints

• Change/check the contrast method of .analyze().

• Change/check the visibility threshold of .analyze().

• Verify the # of low contrast ROIs “seen”.

Machine logs

• #161 Trajectory logs v4.0 are now supported

v 2.5.0

Warning

There appears to be an issue with reading TIFF images on Windows with libtiff=4.1.0. If you experience TIFF header errors, downgrade libtiff to <4.1.

General

• This release adds utility functions to the image generator module and also a change in configuration of the picket fence module, allowing users to create their own MLC configurations.

Dependencies

• py-linq has been added as a dependency. It’s pure python so it will not add secondary dependencies.

Picket Fence

• MLC configuration has changed from being empirical to a priori, meaning that leaves are no longer determined, but passed in via configuration. This allows users to configure their own custom MLCs arrangements. See Customizing MLCs.

• Linked with the above, the is_hdmlc parameter is deprecated and users should now use the mlc parameter in the constructor.

• Also due to above, new parameters have been added to the analyze method. Please see the documentation for more info.

• The colored overlay is now broken up into the individual leaf kisses rather than one line.

• Several internal classes were removed or overhauled. This should not affect you if you’re just using the basic routines like analyze(). Settings no longer exists, MLCMeas is now MLCValue. PicketManager no longer exists.

VMAT

• The ROI segment size can now be specified in analyze. This is discussed in the new section Customizing the analysis.

Image generator

In the previous release, a new image generator module was introduced. This release adds utility scripts for easily creating Winston-Lutz and picket fence image sets. See the Helpers section of the generator documentation.

v 2.4.0

General

Thanks to several contributors for making pull requests in this release!

• A new image generator module has been added. This module can generate custom test images easily: Image Generator.

• The core peak-finding functionality used in several modules was refactored to use scipy’s implementation. When pylinac was built, such a function did not exist. Now that it does, the custom code has been removed (yay!). The major difference between this implementation and pylinac’s is the use of “prominence”, which is a concept I had never heard of. The resulting peak-finding functionality is the same for max-value peak-finding. For FWXM peak finding, this can have small differences. The biggest differences would be for profiles that have a very asymmetric “floor”. I.e. if one valley on one side of the peak has a very different value than the other side then a difference would be detected. Fortunately, this is a very rare scenario.

• Documentation plots have been updated to be generated on-the-fly. This will result in better agreement with documentation plots vs. what people experience. Previously, some old figures were used that did not match the functionality.

• The GUI function was removed from the pylinac init file. This was causing issues when deploying to Heroku as calls to tkinter caused failures. The GUI should be called from the submodule now:

  # old
  import pylinac
  
  pylinac.gui()
  
  # new
  from pylinac.py_gui import gui
  
  gui()
  

Dependencies

Two requirements have been bumped: scipy>=1.1 and scikit-image>=0.17.

CT Module

If you do not perform any advanced functionality, no changes are noteworthy.

The CT module has been reworked to be far more extensible to adjust individual component modules as desired. Previously, only the offset of the modules was easily adjustable. To edit individual modules the user would have to edit the source code directly. Now, the user can subclass individual modules, overload attributes as desired and pass those to the parent CatPhan class. A new tutorial section has been added to the documentation showing examples of this functionality.

• The CTP404 and 528 modules have been refactored into CatPhan-specific classes for easier overloading by appending “CP<model>”. E.g. CTP404CP503.

• CTP modules had an inconsistent naming scheme for rois. E.g. CTP404 had hu_rois and bg_hu_rois while CTP515 had inner_bg_rois and rois. This has been standardized (mostly) into rois for all modules and, where applicable, background_rois. Some modules still have more relevant attrs, e.g. thickness_rois for CTP404, but they all have have rois.

• Due to the above refactor, you may notice small differences in the contrast constant value and thus the ROIs “seen”.

• HU differences are now signed. Previously the absolute value of the difference was taken.

• HU nominal values have been adjusted to be the mean of the range listed in the CatPhan manuals. The changes are as follows: Air: N/A (this is because most systems have a lower limit of -1000), PMP: -200 -> -196, LDPE: -100 -> -104, Poly: -35 -> -47, Acrylic 120 -> 115, Delrin: 340 -> 365, Teflon: 990 -> 1000, Bone (20%): 240 -> 237, Bone (50%): N/A.

Flatness & Symmetry

The flatness & symmetry module has been updated to allow for profiles of a select width to be analyzed rather than a single pixel profile.

• A filter parameter has been added to the constructor. This filter will apply a median filter of pixel size x.

• Due to the new peak-finding function, flatness and symmetry values may be slightly different. In testing, if a filter was not used the values could change by up to 0.3%. However, when a filter was applied the difference was negligible.

• Two new keyword parameters were added to analyze: vert_width and horiz_width. You can read about their usage in the analyze documentation.

• The plot() method was renamed to plot_analyzed_image() to match the rest of the modules.

Watcher

The watcher script has been officially deprecated for now (it was broken for a long time anyway). A better overall solution is to use something like QATrack+ anyway =).

Bug Fixes

• #325 The Leeds angle detection should be more robust when the phantom angle is very close to 0.

• #313 The catphan CTP486 module had an inverted top and bottom ROI assignment.

• #305 The Leeds invert parameter was not being respected.

• #303 Un-inverted WL image analysis would give an error.

• #290 Catphan HU linearity differences are now signed.

• #301 Loading starshots and picket fences from multiple images has been fixed.

• #199 Printing Picket Fence PDFs with a log has been fixed.

v 2.3.2

Bug Fixes

• #285 The SI QC-3 module was incorrectly failing when the phantom was at 140cm due to a faulty mag factor.

v 2.3.1

Bug Fixes

• #281 The ct module had a wrong usage of the new MTF module that caused a break.

v 2.3.0

General

• The dependencies have been updated. Scikit-image min version is now 0.13 from 0.12. There is also no upper pin on numpy or scikit-image.

• The planar imaging module was overhauled.

• An MTF core module was introduced to refactor and standardize the MTF calculations performed across pylinac.

• The Winston-Lutz 2D and 3D algorithms were improved.

Winston Lutz

• The coordinate space definition has changed to be compatible with IEC 61217. This affects how to understand the 3D shift vector. The bb_shift_instructions have been modified accordingly to still give colloquial instructions correctly (i.e. “Left 0.3mm”).

• The WL module received an internal overhaul with respect to the 3D shift algorithm (i.e. the BB shift vector/instructions). The 3D algorithm was reimplemented according to D Low’s 1994 paper. Generally speaking, the results are more stable across multiple datasets, however, you may see individual differences of up to 0.3mm.

• Due to above, the bb_<axis>_offset and epid_<axis>_offset properties have been removed.

• Two new image categorizations have been added: GB Combo and GBP Combo. These represent a gantry/collimator combination image with the couch at 0 and gantry/collimator/couch image where all axes are rotated. GBP Combo is a replacement for ALL. This change should only affect users who explicitly call methods that ask for the image set like .axis_rms_deviation, .plot_axis_images, etc.

• A new property has been added: .gantry_coll_iso_size which calculates the isocenter size using both gantry and collimator images.

• A new property has been added to individual images: .couch_angle_varian_scale. This conversion is needed to go from IEC 61217 to “Varian” scale for proper 3D shift vector calculation per the 3D algorithm change. Users likely wouldn’t need this, but it’s there.

• The 2D CAX->BB vector is improved slightly (#268). Thanks to @brjdenis and @SimonBiggs for bringing this to my attention and helping out.

Planar Imaging

• The Doselab MC2 (MV & kV) phantom has been added to the planar imaging module.

• The planar imaging module has been overhauled. The automatic detection algorithms have been spotty with no easy way of correcting the inputs. Further, each phantom had a few subtle differences making them just different enough to be annoying.

• To this end, the phantom classes have been refactored to consistently use a base class. This means all main methods behave the same and give a standardized output.

• Creating new custom phantom classes is now very easy. A new section of the planar imaging documentation has been added as a guide.

• A results method has been added to the base class, thus inherited by all phantom classes.

• The parameter hi_contrast_threshold has been refactored to high_contrast_threshold.

• The attributes lc_rois and hc_rois have been refactored to low_contrast_rois and high_contrast_rois, respectively.

• The analyze method now includes new standardized parameters angle_override, size_override, and center_override. Each of these is exactly what it sounds like: overriding pylinac’s automatic algorithm. This is useful if the automatic algorithm gives an incorrect value.

• A phantom outline is now displayed on images. This outline is a simple representation and should only be used as a guide to the accuracy of the phantom spatial detection. I.e. you can use this outline to potentially override the center, size, or angle based on the outline.

• The automatic rotation analysis of the phantoms has been problematic. After spending a significant amount of time on the issue a satisfactory solution was not found. Therefore, the default angle or phantoms is that of the recommendation of the manufacturer. I.e. for the QC-3 phantom this means 45 degrees, as is the value when properly set up to the crosshairs.

• High and low contrast ROIs now show as red if they were below the defined threshold.

Core Modules

• A new core module mtf has been created to standardize all MTF calculations in pylinac. Previously, these were handled independently. The new module contains one class MTF with one method relative_resolution to calculate the lp/mm value at the passed rMTF percentage.

Bug Fixes

• This release contains critical fixes. All users of the Winston-Lutz and VMAT modules are strongly encouraged to upgrade as soon as possible.

• #268 The Winston-Lutz BB-finding method contained an error that would cause the BB center to be slightly off-center. After running unit tests, 5/16 datasets had a couch isocenter size difference of >0.2mm. Of those, 3 were around 0.2mm greater and 2 were around 0.2mm smaller. No other changes to iso sizes were detected within the testing tolerance of 0.2mm.

• #204 The VMAT module was sometimes using raw pixel values to calculate the ROI deviations. This would cause the deviations to appear smaller than they should have been if the Rescale and Intercept had been applied to the pixel data.

• #280 The Winston-Lutz 3D BB shift vector was underestimating the shifts by ~30-40%. A new 3D algorithm was implemented.

• #275 Requirements no longer have an upper pinning, although scikit-image minimum version was bumped from 0.12 to 0.13.

• #274 A new MTF module was created to refactor multiple ad hoc implementations.

• #273 The CatPhan HU module detection algorithm was loosened slightly to account for very thin slice scans which have increased noise.

v 2.2.8

General

Although the following changes should really mean a 2.3 release, I consider them small enough that I will keep it a maintenance release.

• An invert parameter was added to the analyze method of the FlatSym module so the user can override the automatic inversion.

• An invert parameter was added to the analyze method of the Starshot module so the user can override the automatic inversion.

Bug Fixes

• #272 An invert parameter was added to the analyze function of the starshot module. This allows the user to force invert the image if pylinac’s auto-inversion algorithm is incorrect.

• #264/265 The results method for the flatsym module would err out when images with 0 flatness were used.

• #191 The flatsym module was not loading non-DICOM images properly, causing processing failures.

• #202 The rotation determination of the QC-3 phantom was often incorrect. This has temporarily been fixed by hardcoding the angle to 45 degrees. This is a correct assumption if the phantom is being used according to the instructions.

• #263 The FlatSym module was sometimes incorrectly inverting images. This was fixed using a better histogram methodology.

• #266 The deviation of a VMAT ROI was not properly detecting failing segments if the value was negative.

• #267 The overall_passed property of the CTP515 module contained an error that would cause an error.

• #271 The line pair/mm values for the CT/CBCT module was inadvertently doubled. I.e. the lines/mm was given, not line pairs.

v 2.2.7

Winston-Lutz

• A small change was made to the Winston-Lutz BB finding algorithm to be more robust and use less custom code. The output from WL analyses should be within 0.1mm of previous values.

• A section was added to the documentation to describe how images are classified and the analysis of output from the .results() method.

Bug Fixes

• #187 Scipy’s imresize function has been deprecated. Functionality was converted to use skimage.transform.resize().

• #185 Winston-Lutz PDF generation had an artifact causing catastrophic failure.

• #183 The Bakai fomula of the gamma calculation had an operational inconsistency such that dose-to-agreement other than 1% would give incorrect values of the gamma value.

• #190 The Catphan module had an inconsistency in the rMTF/spatial resolution determination. Some line pair regions would be detected for some phantoms and not for others. This was caused by the different CatPhan models having slighly different rotations of the CTP528 module. Pylinac now has model-specific boundaries.

• #192 The FlatSym plot would conflate the vertical and horizontal lines shown on the analyzed image. Analysis is unaffected, only the depiction of position.

• #194 The Leeds low contrast ROI color on the analyzed image was not consistent with the contrast plots. ROI color is now based on the pass/fail of the contrast constant, not the contrast.

• #196 Winston-Lutz images with a dense BB and low photon energy could cause BB detection to fail. A better BB-finding algorithm has been implemented.

• #197 EPID RMS deviation would return 0 for the .results() method always. This now calculates correctly.

V 2.2.6

Bug Fixes

• #157 This behavior is revered to pre-2.2.2 behavior to match the DFV and other software.

• #167 Originally, the fix for this was to raise an error and point to a workaround. At the time the fix was to add a parameter to v2.3.

  Behavior was able to be changed internally to handle this case without an API change.

V 2.2.5

General

The watcher function has had several issues. It has been disabled and will be removed in v2.3.

Bug Fixes

• #173 When forcing inversion of picket fence, the inversion came after the orientation determination, causing orientation to be wrong when inversion was needed.

• #171 The load_log function was not working correctly when passing a directory or ZIP archive.

• #172 Calling publish_pdf from log_analyzer without passing a filename would fail.

• #169 VMAT Dynalogs were calculating fluence incorrectly for CCW plans due to the gantry angle replacing the dose.

• #160 While addressing #160 initially, Trajectory logs were unknowningly affected. Behavior has been reverted to pre-2.2.2 behavior and documentation changed.

V 2.2.4

Bug Fixes

• #165 Machine log plots and PDFs showing the Leaf RMS were shown in cm, not in mm, as the axis title indicated.

• #167 Picket fence images where the pickets are too close to the edge perpendicular to the pickets will fail. This adds an explicit error and mentions a workaround. The next major version will include a padding parameter to apply this workaround.

• #168 Picket fence analyses now crop 2 pixels from every edge. This will allow Elekta images to be analyzed since they inexplicably have a column of dead pixels in EPID images. Should not affect Varian images.

V 2.2.3

Bug Fixes

• #158 Catphan roll determination algorithm has slightly widened the air bubble-finding criterion.

V 2.2.2

Bug Fixes

• #157 Dynalog MLC leaf error was calculated incorrectly. Expected positions were off by a row. Error results should be lower on average.

• #160 Dynalog MLC leaf internal pair mapping (1-61 vs 1-120) was different than documentation. Image calculations should not change.

• #162 The LeedsTOR angle_offset in the .analyze() method was not being followed by the high-contrast bubbles.

• #144 The LeedsTOR angle determination is much more robust. Previously, only certain orientations of the phantom would correctly identify.

V 2.2.1

Bug Fixes

• #153 Log analyser PDF publishing fix.

• #155 VMAT PDF report had tolerance listed incorrectly (absolute vs percentage) causing most tolerances to appear as zero due to rounding.

V 2.2.0

General

• #131 Typing has been added to almost every function and class in pylinac.

• F-strings have been incorporated. This bumps the minimum version for Python to 3.6.

• The publish_pdf method of every module has had its signature changed. Before, not all the signatures matched and only included a few parameters like author and unit name. This has been changed to filename: str, notes: str, list of str, open_file: bool, metadata: dict. Filename and open file are straightforward. notes is a string or list of strings that are placed at the bottom of the report (e.g. ‘April monthly redo’). Metadata is a dictionary that will print both the key and value at the top of each page of the report (e.g. physicist and date of measurement)

• The TG-51 module has been placed under a new module: Calibration (TG-51/TRS-398). This is because:

• A TRS-398 calibration module has been created TRS-398.

• The default colormap for arrays is now Viridis, the matplotlib default.

• A contributor’s guide has been added: Contributing.

• #141 The Pylinac logo has been included in the package so that PDFs can be generated without needing www access.

• A new dependency has been added: argue which handles input parameters.

Flatness & Symmetry

• #130 The flatsym module has been completely rewritten. Documentation has also been updated and should be consulted given the number of changes: Field Analysis.

VMAT

• The overall simplicity of use has been increased by automating & removing several parameters.

• #128 The VMAT class has been split into two classes: DRGS and DRMLC. Although there are now two classes instead of one, the overall simplicity has been increased, such as the following:

  • The test parameter in analyze() is no longer required and has been removed.

  • The type is no longer required in .from_demo_images().

  • The demo method matches the other modules: .run_demo()

  • All naming conventions have been deprecated.

• The x_offset parameter has been removed. The x-position is now based on the FWHM of the DMLC field itself. This means the x-position is dynamic and automatic.

• The delivery_types parameter has been removed. The delivery types of the images are now automatically determined.

• The methods for plotting and saving subimages (each image & the profiles) has been converted to a private method (_plot_subimage(), …). There is little need for a public method to plot individually.

TG-51/Calibration

• #127 A TRS-398 module has been added. There are two main classes: TRS398Photon and TRS398Electron.

• #129 The TG-51 module has been refactored to add a TG51ElectronLegacy and TG51ElectronModern calibration class. The Legacy class uses the classic TG-51 values that require a kecal value and a Pgradient measurement. The Modern class uses the equations from Muir & Rogers 2014 to calculate kQ that updates and incorporates the Pgradient and kecal values. While not strictly TG-51, these values are very likely to be incorporated into the next TG-51 addendum as the kQ values for photons already have.

• Certain parameters have been refactored: volt_high and volt_low have been refactored to voltage_reference and voltage_reduced, m_raw, m_low, and m_opp have been refactored to m_reference, m_reduced, and m_opposite. These parameters are also the same for the TRS-398 classes (see #127).

• The kq function has been separated into three functions: kq_photon_pdd10x, kq_photon_tpr2010, and kq_electron.

• A PDD(20,10) to TPR(20,10) converter function has been added: tpr2010_from_pdd2010.

• Pressure and temperature conversion helper functions have been added: mmHg2kPa, mbar2kPa, fahrenheit2celsius. This can be used in either TG-51 or TRS-398 to get TPR without actually needing to measure it.

• Defaults were removed from most functions to avoid possible miscalibration/miscalculation.

• Most parameters of both TG-51 and TRS-398 were changed to be keyword only. This will prevent accidental miscalculations from simple positional argument mismatches.

Bug Fixes

• #138/#139: Too many arguments when plotting the leaf error subplot for picketfence.

• #133: Trajectory log HDMLC status was reversed. This only affected fluence calculations using the equal_aspect argument.

• #134: Trajectory log fluence array values were not in absolute MU.

V 2.1.0

General

• After reflection, the package seems to have bloated in some respects. Certain behaviors are only helpful in very few circumstances and are hard to maintain w/ proper testing. They are described below or in their respective sections.

• The command line commands have been deprecated. All commands were simply shortcuts that are just as easy to place in a 1-2 line Python script. There was no good use case for it in the context of how typical physicists work.

• The interactive plotting using MPLD3 has been deprecated. Matplotlib figures and PDF reports should be sufficient. This was a testing nightmare and no use cases have been presented.

• The transition of the method return_results() to results() is complete. This was baked-in from the very beginning of the package. It is expected that results would return something, nor is there any other corresponding method prefixed with return_.

• Pip is now the recommended way to install pylinac. Packaging for conda was somewhat cumbersome. Pylinac itself is just Python and was always installable via pip; it is the dependencies that are complicated. The wheels format seems to be changing that.

• Some dependency minimum versions have been bumped.

CatPhan

• The module was refactored to easily alter existing and add new catphan models.

• The CatPhan HU module classifier has been deprecated. Its accuracy was not as high as the original brute force method. Thus, the use_classifier keyword argument is no longer valid.

• CatPhan 604 support was added thanks to contributions and datasets from Alan Chamberlain. More datasets are needed to ensure robust analysis, so please contribute your dataset if it fails analysis.

• The CTP528 slice (High resolution line pairs) behavior was changed to extract the max value from 3 adjacent slices. This was done because sometimes the line pair slice selected was slightly offset from the optimum slice. Using the mean would lower MTF values. While using the max slightly increases the determined MTF from previous versions, the reproducibility was increased across datasets.

Winston-Lutz

• Certain properties have been deprecated such as gantry/coll/couch vector to iso. These are dropped in favor of a cumulative vector.

• A BB shift vector and shift instructions have been added for iterative WL testing. I.e. you can get a BB shift to move the BB to the determined iso easily.

  import pylinac
  
  wl = pylinac.WinstonLutz.from_demo_images()
  print(wl.bb_shift_instructions())
  # output: RIGHT 0.29mm; DOWN 0.04mm; OUT 0.41mm
  # shift BB and run it again...
  

• Images taken at nonzero couch angles are now correctly accounted for in the BB shift.

• Images now do not take into account shifts along the axis of the beam (#116).

• The name of the file will now not automatically be interpreted if it can. This could cause issues for valid DICOM files that had sufficient metadata. If the image was taken at Gantry of 45 and the file name contained “gantry001” due to, e.g., TrueBeam’s default naming convention it would override the DICOM data. (#124)

Picket Fence

• Files can now allow for interpretation by the file name, similar to the WL module. This is helpful for Elekta linacs that may be doing this test (#126).

Core Modules

• is_dicom and is_dicom_image were moved from the utilites module to the io module.

• field_edges() had the parameter interpolation added so that field edges could be computed more accurately (#123)

• A new class was created called LinacDicomImage. This is a subclass of DicomImage and currently adds smart gantry/coll/couch angle interpretation but may be extended further in the future.

V 2.0.0

General

• Version 2.0 is here! It may or may not be a real major version update worthy of ‘2.0’, but ‘1.10’ just didn’t sound as good =)

• A GUI has been added! Most major modules have been added to the GUI. The GUI is a very simple interface that will load files and publish a PDF/process files. To start the gui run the gui() function like so:

  import pylinac
  
  pylinac.gui()
  

  You may also start the GUI from the command line:

  pylinac gui
  

  The GUI is a result of a few causes. Many physicists don’t know how to code; this should remove that barrier and allow Pylinac to get even more exposure. I have always felt the web was the future, and it likely is, but pylinac should be able to run on it’s own, and because a rudimentary GUI is relatively easy, I’ve finally made it. The GUI is also free to use and has no hosting costs (unlike assuranceQA.com). Also, due to other ventures, a new job, and a newborn, I couldn’t devote further time to the assuranceQA site–A native GUI is much easier albeit much more primitive.

• Some module PDF methods now don’t require filenames. If one is not passed it will default to the name of the file analyzed. E.g. “abc123.dcm” would become “abc123.pdf”. Modules where multiple images may be passed (e.g. a CBCT directory) still requires a filename.

• PDF methods now have a boolean parameter to open the file after publishing: open_file.

• A number of dependencies have been bumped. Some were for specific reasons and others were just out of good practice.

Watcher

• Closes #84 Which would overwrite the resulting zip and PDF of initially unzipped CBCTs performed on the same day. I.e. multiple CBCTs would result in only 1 zip/PDF. The image timestamp has been edited so that it will include the hour-minute-second of the CBCT to avoid conflict.

• Closes #86 - Which had a discrepancy between the YAML config setting of the file source directories and what the watcher was looking for.

CatPhan

• Closes #85 Which displayed the nominal CBCT slice width on PDF reports, not the detected width for the CatPhan504 & CatPhan600.

• Closes #89 which had variables swapped in the CatPhan503 PDF.

• The contrast_threshold parameter has been renamed to cnr_threshold. The meaning and values are the same, but has been renamed to be consistent with other changes to the roi module.

• Due to various problems with the SVM classifier, the default setting of the classifier has been set to False.

Planar Phantoms

• The Las Vegas phantom has been added to the planar imaging module. It’s use case is very similar to the existing planar phantoms:

  from pylinac import LasVegas
  
  lv = LasVegas("myfile.dcm")
  lv.analyze()
  lv.publish_pdf()
  ...
  

• The pylinac.planar_imaging.LeedsTOR.analyze() method has an additional parameter: angle_offset. From analyzing multiple Leeds images, it has become apparent that the low contrast ROIs are not always perfectly set relative to the phantom. This parameter will allow the user to fine-tune the analysis to perfectly overlay the low contrast ROIs by adding an additional angle offset to the analysis.

Winston-Lutz

• Closes enhancement #63 Files can now have the axis settings interpreted via the file name. E.g: “myWL_gantry90_coll0_couch340.dcm”. See Passing in Axis values for further info.

• The x/y/z_offset properties of the WLImages which were deprecated many versions ago have finally been removed.

• The collimator/gantry_sag and associated plot_gantry_sag methods have been deprecated. A similar method has been implemented that utilizes the RMS deviation. To achieve the “gantry sag” using RMS errors use the method axis_rms_deviation with parameter value='range'.

TG-51

• The Electron class has been adjusted to reflect the Muir & Rogers 2014 kecal data which allows the user to calculate kQ from just R50 data.

• The kq function now accepts an r_50 parameter to calculate kQ based on the above data.

Core Modules

• The Image class has been fully deprecated and is no longer available. Use the functions available in the Image Module instead. See the version 1.4.0 release notes for further details.

• The remove_edges method has been deprecated and is now an alias for crop. The crop method should be used instead. Parameters are exactly the same.

V 1.9.0

General Changes

• This release introduces PDF reports for most major modules. All classes with this functionality have been given a publish_pdf method. This method takes an output filename and other optional data like the author, machine/unit, and any custom notes. See e.g. pylinac.starshot.Starshot.publish_pdf() or pylinac.picketfence.PicketFence.publish_pdf().

• The watch/process functions have been tweaked to best work on one unit per run. Multiple units/machines should have their own config files. A new article describes how to use the process function with Windows Task Scheduler to regularly pull and analyze files.

CatPhan

• The CatPhan classes, when passed a directory during instantiation, will search through the DICOM files for Series UIDs and analyze the files of the most numerous UID. E.g. if a folder has 80 DICOM images including one set of 60 CBCT images and a total of 20 VMAT and picket fence images, it will find the CBCT files via UID and analyze those, leaving the other images/files alone. This is useful for when all QA images are simply dumped into one folder.

• Raw, uncompressed CatPhan DICOM files can optionally be compressed to a ZIP file after analysis using the new zip_after argument in the analyze method.

Watcher/Processer

• The watcher/process functions have been reworked to produce PDF files rather than PNG/txt files.

• If upgrading the watch/process function from a previous pylinac version be sure to copy/amend the new default YAML config file as new keywords have been added and using old YAML files will error out.

• Several new configuration keywords have been changed/added. In the general section, use-classifier has been deprecated in favor of individual module keywords of the same name. This allows a user to use a classifier for, say, picket fence images but not for winston lutz images. A unit keyword has been added that specifies which unit the files should be considered to be from. This unit name is passed to the PDF reports that are generated. If you have multiple units, make individual YAML configuration files, one for each unit.

• CatPhan, VMAT, and Winston-Lutz can now take raw, unzipped images as well as the usual ZIP archive. ZIP archives are detected only by keywords as usual. For uncompressed CatPhan images, the analyzer will look for any CatPhan DICOM file groups via UID (see above CatPhan section), analyze them, and then ZIP the images until no further sets can be found. For VMAT and Winston-Lutz if the use-classifier setting is true their respective sections in the YAML configuration then an image classifier is used to group images of the given type and then analyze them.

v 1.8.0

General Changes

• This release focuses solely on the CBCT/CatPhan module.

• Pylinac now has a logo! Check out the readme on github or landing page on ReadTheDocs.

Watcher/Processer

• The cbct analysis section has been renamed to catphan. Thus, the YAML config file needs to look like the following:

  # other sections
  ...
  
  catphan:  # not cbct:
      ...
  
  ...
  

CBCT/CatPhan

• The Python file/module has been renamed to ct from cbct. E.g.:

  from pylinac.ct import ...
  

  Most users import directly from pylinac, so this should affect very few people. This was done to generalize the module to make way for other CT/CBCT phantoms that pylinac may support in the future.

• The CBCT module can now support analysis of the CatPhan 600.

• Automatic detection of the phantom is no longer be performed. Previously, it depended on the manufacturer to determine the phantom (Varian->504, Elekta->503), but that did not consider users scanning the CatPhan in their CT scanners, which would give inconsistent results.

• Due to the above, separate classes have been made for the CatPhan models. I.e. flow looks like this now:

  # old way
  from pylinac import CBCT
  ...
  
  # new way
  from pylinac import CatPhan504, CatPhan600
  cat504 = CatPhan504('my/folder')
  cat600 = CatPhan600.from_zip('my/zip.zip')
  

• A classifier has been generated for each CatPhan. Thus, if loading a 503, a 503 classifier will be used, rather than a general classifier for all phantoms.

• The use_classifier parameter has been moved from the analyze() method to the class instantiation methods like so:

  from pylinac import CatPhan504
  cat504 = CatPhan504('my/folder', use_classifier=True)
  cat504.analyze()  # no classifier argument
  

• MTF is now more consistently calculated. Previously, it would simply look at the first 6 line pair regions. In cases of low mA or very noisy images, finding the last few regions would error out or give inconsistent results. Contrarily, high dose/image quality scans would only give MTF down to ~50% since the resolution was so good. Now, MTF is searched for region-by-region until it cannot find the correct amount of peaks and valleys, meaning it is now lost in the noise. This means high-quality scans will find and calculate MTF over more regions and fewer for low-quality scans. In general, this makes the MTF plot much more consistent and usually always gives the RMTF down to 0-20%.

• Individual modules are now only composed of 1 slice rather than averaging the nearby slices. Previously, for consistency, a given module (e.g. CTP404) would find the correct slice and then average the pixel values of the slices on either side of it to reduce noise and give more consistent results. The drawback of this method is that results that depend on the noise of the image are not accurate, and signal/noise calculations were always higher than reality if only looking at one slice.

v 1.7.2

• Fixed (#78) - Certain CBCT datasets have irregular background values. Additionally, the dead space in the square CT dataset outside the field of view can also be very different from the air background. This fix analyzes the dataset for the air background value and uses that as a baseline value to use as a CatPhan detection threshold.

V 1.7.0

General Changes

• The underlying structure of the watcher script has been changed to use a different framework. This change allows for analysis of existing files within the directory of interest.

• A new module has been introduced: tg51, handling several common equations and data processing for things relating to TG-51 absolute dose calibration such as Kq, PDDx, Dref, pion, ptp, etc. It also comes with classes for doing a full TG-51 calculation for photons and electrons with cylindrical chambers.

Log Analyzer

• The log analyzer has changed from having a main class of MachineLog, to the two distinct log types: Dynalog and TrajectoryLog. These classes are used the same way as machinelog, but obviously is meant for one specific type of log. This allows for cleaner source code as the MachineLog class had large swaths of if/else clauses for the two log types. But don’t worry! If you’re unsure of the log type or need to handle both types then a helper function has been made: load_log. This function will load a log just like the MachineLog did and as the new classes. The difference is it will do automatic log type detection, returning either a Dynalog instance or TrajectoryLog instance. The MachineLogs class remains unchanged.

• More specific errors have been introduced; specifically NogALogError, NotADynalogError, and DynalogMatchError which are self-explanatory and more specific than IOError.

• Fixed (#74) which was causing Dynalogs with patient names containing a “V” to be classified as Trajectory logs.

• Fixed (#75) which was skewing gamma pass percent values.

Planar Imaging

• The PipsProQC3 class/phantom has been refactored to correctly reflect its manufacturer to Standard Imaging, thus the class has been renamed to StandardImagingQC3.

Directory Watching

• The watch command line argument now has a sister function, available in a regular Python program: watch(). With this command you can run the directory watcher programmatically, perfect for continuous log monitoring.

• A new command line argument is available: process. This command is also available in Python as process() which can be called on a directory either through the command line or programmatically and will analyze a folder once and then exit, perfect for analyzing a new monthly dataset.

• The structure of querying for files has been changed significantly. Instead of triggering on file changes (e.g. adding a new file to the directory), the watcher now constantly queries for new files at a specified interval. This means that when started, the watcher will analyze existing files in the folder, not just new ones.

• Information given in the email has been modified for logs, which may potentially contain PHI. Instead of the entire log file name given, only the timestamp is given. Additionally, the logs are no longer attached to the email.

V 1.6.0

General Changes

• Changed the default colormap of dicom/grayscale images to be “normal” gray vs the former inverted gray. Brought up in (#70) .

• Added a colormap setting that can be changed. See Changing Colormaps

• Added a utility function clear_data_files() to clear demo files and classifier files. This may become useful for classifier updates. I.e. the classifier for a given algorithm can be cleared and updated as need be, without the need for a new package release. More information on this will follow as the use of classifiers becomes normal.

• Added a dependency to the pylinac requirements: scikit-learn. This library will allow for machine learning advancements to be used with pylinac. I am aware of the increasing number of dependencies; pylinac has reached a plateau I believe in terms of advancement and I hope that this is the last major dependency to be added.

Winston-Lutz

• (#69) Added EPID position tracking. Now the EPID location will show up in images and will give an output value when printing the summary. Relevant methods like cax2epid_distance() and epid_sag(), and plot_epid_sag() have been added. The summary plot has also been changed to include two sag plots: one for the gantry and one for the EPID.

• Certain properties of WL images have been deprecated. x_offset has been replaced by bb_x_offset() and respectively for the other axes. Usage of the old properties will raise a deprecation warning and will be removed in v1.7.

  Note

  The deprecation warnings may not show up, depending on your python version and/or warning settings. See the python docs for more info.

CBCT

• Added a Support Vector Machine classifier option for finding the HU slice. The classifier is faster (~30%) than the brute force method. This option is available as a parameter in the analyze() method as use_classifier. In the event the classifier does not find any relevant HU slices, it will gracefully fall back to the brute force method with a runtime warning. Because of the fallback feature, the classifier is now used first by default. Using the classifier requires a one-time download to the demo folder, which happens automatically; just make sure you’re connected to the internet.

Picket Fence

• An orientation keyword argument was added to the analyze() method. This defaults to None, which does an automatic determination (current behavior). In the event that the determined orientation was wrong, this argument can be utilized.

Watcher Service

• A new option has been added to the general section: use-classifier. This option tells pylinac whether to use an SVM image classifier to determine the type of image passed. This allows the user not to worry about the file names; the images can be moved to the monitored folder without regard to naming. The use of the classifier does not exclude file naming conventions. If the classifier does not give a good prediction, the algorithm will gracefully fall back to the file name convention.

  The following image types currently support automatic detection:

  • Picket Fence

  • Starshot

  • Leeds TOR

  • PipsPro QC-3

V 1.5.6

• Adds the dtype keyword to DicomImage’s init method.

• (#66) - Fixed an issue with Winston-Lutz isocenters not calculating correctly.

• (#68) - Fixed the order of the Winston-Lutz images when plotted.

• Many thanks to Michel for noting the WL errors and submitting the first external pull request !

• Fixed several small bugs and runtime errors.

V 1.5.5

• (#65) - Fixed the FlatSym demo file usage.

V 1.5.4

• (#64) - Fixed the Picket Fence offset from CAX value, which previously were all the same value.

V 1.5.1-3

General Changes

• Fixed conda entry points so that the user can use pylinac console scripts.

• Moved demo images outside the package to save space. Files are downloaded when relevant methods are invoked.

V 1.5.0

General Changes

• The pylinac directory watcher service got a nice overhaul. Now, rather than running the watcher script file directly, you can use it via the console like so:

  $ pylinac watch "path/to/dir"
  

  This is accomplished through the use of console scripts in the Python setup file. Once you upgrade to v1.5, this console command immediately becomes available. See the updated docs on Directory Watching. Previously, customizing behavior required changing the watcher script directly. Now, a YAML file can be generated that contains all the analysis configurations. Create and customize your own to change tolerances and even to trigger emails on analyses.

• You can now anonymize logs via console scripts:

  $ pylinac anonymize "path/to/log/dir"
  

  This script is a simple wrapper for the log analyzer’s anonymize function.

• Pylinac is now on anaconda.org – i.e. you can install via conda and forget about dependency & installation issues. This is the recommended way to install pylinac now. To install, add the proper channel to the conda configuration settings.

  $ conda config --add channels jrkerns
  

  Then, installation and upgrading is as simple as:

  $ conda install pylinac
  

  The advantage of saving the channel is that upgrading or installing in other environments is always as easy as conda install pylinac.

• Pylinac’s core modules (image, io, etc) are now available via the root package level.

  # old way
  from pylinac.core import image
  
  # new way
  from pylinac import image
  

Starshot

• Relative analysis is no longer allowed. I.e. you can no longer pass images that do not have a DPI or SID. If the image does not have these values inherently (e.g. jpg), you must pass it explicitly to the Starshot constructor. No changes are required for EPID images since those tags are in the image file.

• Added a .from_zip() class method. This can contain a single image (to save space) or a set of images that will be combined.

Log Analyzer

• The anonymize function received an optimization that boosted anonymization speed by ~3x for Trajectory logs and ~2x for Dynalogs. This function is very fast.

• Trajectory log subbeam fluences are now available. This works the same way as for the entire log:

  log = MachineLog.from_demo_dynalog()
  # calculate & view total actual fluence
  log.fluence.actual.calc_map()
  log.fluence.actual.plot_map()
  # calculate & view the fluence from the first subbeam
  log.subbeams[0].fluence.actual.calc_map()
  log.subbeams[0].fluence.actual.plot_map()
  

• The gamma calculation has been refactored to use the image.gamma() method. Because of this, all threshold parameters have been changed to fractions:

  log = MachineLog.from_demo_trajectorylog()
  # old way
  log.fluence.gamma.calc_map(threshold=10)  # <- this indicates 10% threshold
  # new way
  log.fluence.gamma.calc_map(threshold=0.1)  # <- this also indicates 10% threshold
  

  The gamma threshold parameter requires the value to be between 0 and 1, so any explicit thresholds will raise an error that should be addressed.

• The .pixel_map attribute of the actual, expected, and gamma fluence structures have been renamed to array since they are numpy arrays. This attribute is not normally directly accessed so few users should be affected.

Bug Fixes

• Fixed a bug that would not cause certain imaging machine logs (CBCT setup, kV setups) to be of the “Imaging” treatment type.

V 1.4.1

• (#56) - Fixes a starshot issue where if the SID wasn’t 100 it was corrected for twice.

• (#57) - CR images sometimes have an RTImageSID tag, but isn’t numeric; this caused SID calculation errors.

V 1.4.0

General Changes

• Nearly all instance-based loading methods (e.g. Starshot().load('myfile')) have been deprecated. Essentially, you can no longer do empty constructor calls (PicketFence()). The only way to load data is through the existing class-based methods (e.g. Starshot('myfile'), Starshot.from_url('http...'), etc). The class-based methods have existed for several versions, and they are now the preferred and only way as there is no use case for an empty instance.

• Since v1.2 most URLs were downloaded and then the local (but temporary) files were loaded. This practice has now been standardized for all modules. I.e. any from_url()-style call downloads a temporary file and loads that. Because the downloads are to a temporary directory, then are removed upon exit.

• Loading images using the Image class has been deprecated (but still works) in favor of the new functions in the same module with the same name. Where previously one would do:

  from pylinac.core.image import Image
  
  img = Image.load('my/file.dcm')
  

  One should now do:

  from pylinac.core.image import load
  
  img = load('my/file.dcm')
  

  Functionality is exactly the same, but supports a better abstraction (there is no reason for a class for just behaviors). The same change applies for the other loading methods of the Image class: load_url and load_multiples. The Image class is still available but will be removed in v1.5.

Picket Fence

• PicketFence can now load a machine log along with the image to use the expected fluence to determine error. This means if an MLC bank is systematically shifted it is now detectable, unlike when the pickets are fitted to the MLC peaks. Usage is one extra parameter:

  pf = PicketFence('my/pf.dcm', log='my/pf_log.bin')
  

Winston-Lutz

• A from_url() method has been added.

• Upon loading, all files are searched within the directory, not just the root level. This allows for nested files to be included.

CBCT

• The from_zip_file() class constructor method has been renamed to from_zip() to be consistent with the rest of pylinac’s similar constructors.

Log Analyzer

• A new treatment_type has been added for CBCT and kV logs: Imaging.

• A new function has been added to the module: anonymize(). This function is similar to the .anonymize() method, but doesn’t require you to load the logs manually. The function is also threaded so it’s very fast for mass anonymization:

  from pylinac.log_analyzer import anonymize
  
  anonymize('my/log/folder')
  anonymize('mylog.bin')
  

Starshot

• The starshot minimization algorithm has been changed from differential evolution to the more predictable minimize. Previously, results would often be predictable, but would occasionally give really good or really bad results even though no input was changed. This was due to the algorithm; now that a stable algorithm is being used, results are reproducible.

VMAT

• The VMAT loading scheme got a few changes. The Naming Convention is still the same, but images are always loaded upon instantiation (see General Changes). Also, if the naming convention isn’t used, image delivery types can be passed in during construction; e.g.:

  VMAT(images=(img1, img2), delivery_types=['open', 'dmlc']
  

• Loading from a URL has been renamed from from_urls() to from_url() and assumes it points to a ZIP archive with the images inside.

Bug Fixes

• (#47) - Fixes the trajectory log number of beam holds calculation. Thanks, Anthony.

• (#50) - Fixes RMS calculations for “imaging” trajectory logs. Previously, the RMS calculation would return nan, but now returns 0.

• (#51) - Results of the starshot wobble were sometimes extremely high or low. This has been fixed by using a more stable minimization function.

• (#52) - The starshot wobble diameter was incorrect. A recent change of the point-to-line algorithm from 2D to 3D caused this issue and has been fixed.

• (#53) - The Winston-Lutz BB-finding algorithm would sometimes pick up noise, mis-locating the BB. A size criteria has been added to avoid detecting specks of noise.

• (#54) - Imaging Trajectory logs, besides having no RMS calculation, was producing warnings when calculating the fluence. Since there is no fluence for kV imaging logs, the fluence now simply returns an 0’d fluence array.

• (#55) - Dead pixels outside the field were throwing off the thresholding algorithm and not detecting the field and/or BB.

V 1.3.1

• (#46) - Fixes CBCT analysis where there is a ring artifact outside the phantom. Incidentally, analysis is sped up by ~10%.

V 1.3.0

General Changes

• A new dependency has been added: scikit-image. Given that pylinac is largely an image processing library, this is actually overdue. Several extremely helpful functions exist that are made use of in both the new modules and will slowly be incorporated into the old modules as needed. The package is easily installed via pip (pip install scikit-image) or via conda (conda install scikit-image) if using the Anaconda distribution. Finally, if simply upgrading pylinac scikit-image will automatically install via pip. For the sake of installation speed I’d recommend conda.

• ROI sampling for CBCT and Leeds classes have been sped up ~10x, making analysis moderately to much faster.

• All user-interface dialog functions/methods have been deprecated. E.g. PicketFence.from_UI() is no longer a valid method. To retain similar functionality use Tk to open your own dialog box and then pass in the file name. Specifically, this applies to the VMAT, Starshot, PicketFence, MachineLog(s), FlatSym, and CBCT classes. The original goal of pylinac was to be used for a standalone desktop application. The assuranceqa.com web interface is the successor to that idea and does not need those UI methods.

Planar Imaging

• A new planar imaging class has been added: PipsProQC3. This class analyzes the PipsPro QC-3 MV imaging phantom. The class locates and analyzes low and high contrast ROIs.

• The Leeds phantom utilizes the scikit-image library to do a canny edge search to find the phantom. This will bring more stability for this class.

V 1.2.2

• (#45) Fixes various crashes of Leeds analysis.

V 1.2.1

• (#44) Fixed a stale wheel build causing pip install to install v1.1.

V 1.2.0

General Changes

• CatPhan 503 (Elekta) analysis is now supported.

• A new planar imaging module has been added for 2D phantom analysis; currently the Leeds TOR phantom is available.

• The requests package is no longer needed for downloading URLs; the urllib stdlib module is now used instead.

• Requirements were fixed in the docs and setup.py; a numpy function was being used that was introduced in v1.9 even though v1.8 was stated as the minimum; the new requirement is v1.9.

• Demonstration methods for the main classes have been fully converted to static methods. This means, for example, the following are equivalent: CBCT().run_demo() and CBCT.run_demo().

Core Modules

• A tutorial on the use of the core modules is now available.

• A new mask core module was created for binary array operations.

• (#42) The Image classes now have a gamma method available.

• The Image classes’ median_filter() method has been renamed to filter(), which allows for different types of filters to be passed in.

• The Image class can now load directly from a URL: load_url().

CBCT

• CatPhan 503 (Elekta) is now supported. Usage is exactly the same except for the low-contrast module, which is not present in the 503.

• The low contrast measurements now use two background bubbles on either side of each contrast ROI. The default contrast threshold has been bumped to 15, which is still arbitrary but fits most eyeball values.

Starshot

• (#43) Keyword arguments can be passed to the init and class methods regarding the image info. For example, if a .tif file is loaded but the DPI is not in the image header it can be passed in like so:

  star = Starshot("mystar.tif", dpi=100, sid=1000)
  

Planar Imaging

• 2D analysis of the Leeds TOR phantom is available. Tests low and high contrast. A new Planar Imaging doc page has been created.

Winston-Lutz

• A save_summary() method has been added for saving the plot to file.

V 1.1.1

• Winston-Lutz demo images were not included in the pypi package.

V 1.1.0

General Changes

• This release debuts the new Winston-Lutz module, which easily loads any number of EPID images, finds the field CAX and the BB, and can plot various metrics.

Log Analyzer

• Logs can now be anonymized using the .anonymize() method for both MachineLog and MachineLogs.

• The .to_csv() methods for MachineLog and MachineLogs returns a list of the newly created files.

• MachineLogs can now load from a zip archive using .from_zip().

V 1.0.3

• Fixes #39. MachineLog fluence was inverted in the left-right direction.

• Fixes #40. MachineLog fluence calculations from dynalogs were dependent on the load order (A-file vs. B-file).

V 1.0.2

• Fixes #38. MachineLog fluence calculations would crash if there was no beam-on snapshots (e.g. kV images).

V 1.0.1

• Fixes #37. Reading in a trajectory log txt file with a blank line caused a crash.

V 1.0.0

General Changes

• This release debuts the new interactive plotting for certain figures. Quickly, matplotlib line/bar plots (althouth not yet images/arrays) can be plotted and saved in HTML using the MPLD3 library. This is less of interest to users doing interactive work, but this adds the ability to embed HTML plots in web pages.

• Several numpy array indexing calls were converted to ints from floats to avoid the new 1.9 numpy type-casting warnings. This also speeds up indexing calls slightly.

Picket Fence

• The analyzed image now has the option of showing a leaf error subplot beside the image. The image is aligned to the image such that the leaves align with the image.

Starshot

• Plotting the analyzed starshot image now shows both the zoomed-out image and a second, zoomed-in view of the wobble.

• Each subplot can be plotted and saved individually.

VMAT

• Plotting the analyzed image now shows the open and dmlc images and the segment outlines as well as a profile comparison between the two images. Each subplot can also be plotted and saved individually.

• MLCS is no longer a test option; DRMLC should be used instead.

V 0.9.1

• Fixed a bug with the log analyzer treatment type property.

V 0.9.0

General Changes

• This release has a few new features for the CBCT class, but is mostly an internal improvement. If you only use the main classes (CBCT, PicketFence, Starshot, etc), there should be no changes needed.

CBCT

• The CBCT analysis now examines low contrast ROIs and slice thickness.

• CBCT components have been renamed. E.g. the HU linearity attr has been renamed hu from HU.

Starshot

• Fixes #32 which was causing FWHM peaks on starshots to sometimes be erroneous for uint8/uint16 images.

PicketFence

• Adds #31, a method for loading multiple images into PicketFence.

Log Analyzer

• Fixes a bug which sometimes caused the parsing of the associated .txt log file for trajectory logs to crash.

V 0.8.2

• Fixed a bug with the picket fence overlay for left-right picket patterns.

• Plots for starshot, vmat, and picketfence now have a larger DPI, which should mean some more detail for saved images.

V 0.8.1

• Fixed an import bug

V 0.8.0

General Changes

• An upgrade for the robustness of the package. A LOT of test images were added for the Starshot, CBCT, PicketFence, and VMAT modules and numerous bugs were caught and fixed in the process.

• The debut of the “directory watcher”. Run this script to tell pylinac to watch a directory; if a file with certain keywords is placed in the directory, pylinac will analyze the image and output the analyzed image and text file of results in the same directory.

• A generic troubleshooting section has been added to the documentation, and several modules have specific troubleshooting sections to help identify common errors and how to fix them.

VMAT

• Added a from_zip() and load_zip() method to load a set of images that are in a zip file.

• Added an x_offset parameter to analyze() to make shifting segments easier.

PicketFence

• Fixed #30, which wasn’t catching errors on one side of the pickets, due to a signed error that should’ve been absolute.

• Two new parameters have been added to analyze(): num_pickets and sag_adjustment, which are somewhat self-explanatory. Consult the docs for more info.

Starshot

• Fixed #29, which was causing analysis to fail for images with a pin prick.

CBCT

• Fixed #28, which was applying the phantom roll adjustment the wrong direction.

V 0.7.1

General Changes

• Added .from_url() class method and .load_url() methods to most modules.

PicketFence

• Fixed #23, which was not properly detecting pickets for picket patterns that covered less than half the image.

• Fixed #24, which was failing analysis from small but very large noise. A small median filter is now applied to images upon loading.

V 0.7.0

General Changes

• The scipy dependency has been bumped to v0.15 to accommodate the new differential evolution function using in the Starshot module.

CBCT

• Whereas v0.6 attempted to fix an issue where if the phantom was not centered in the scan it would error out by adding a z-offset, v0.7 is a move away from this idea. If the offset given was not correct then analysis would error disgracefully. It is the point of automation to automatically detect things like where the phantom is in the dataset. Thus, v0.7 is a move towards this goal. Briefly, upon loading all the images are scanned and the HU linearity slice is searched for. Of the detected slices, the median value is taken. Other slices are known relative to this position.

• As per above, the z-offset idea is no longer used or allowed.

• Plots are now all shown in grayscale.

• If the phantom was not completely scanned (at least the 4 modules of analysis) analysis will now error out more gracefully.

V 0.6.0

General Changes

• Pylinac now has a wheel variation. Installation should thus be quicker for users with Python 3.4.

• Most main module classes now have a save method to save the image that is plotted by the plot method.

Class-based Constructors

• This release presents a normalized and new way of loading and initializing classes for the PicketFence, Starshot, VMAT and CBCT classes. Those classes all now accept the image path (folder path for CBCT) in the initialization method. Loading other types of data should be delegated to class-based constructors (e.g. to load a zip file into the CBCT class, one would use cbct = CBCT.from_zip_file('zfiles.zip')). This allows the user to both initialize and load the images/data in one step. Also prevents user from using methods before initialization (i.e. safer). See ReadTheDocs page for more info.

Dependencies

• Because the VMAT module was reworked and is now based on Varian specs, the pandas package will no longer be required. FutureWarnings have been removed.

CBCT

• Bug #18 is fixed. This bug did not account for slice thickness when determining the slice positions of the relevant slices.

• Bug #19 is fixed. This bug allowed the loading of images that did not belong to the same study. An error is now raised if such behavior is observed.

• Demo files are now read from the zipfile, rather than being extracted and then potentially cleaning up afterward. Behavior is now quicker and cleaner.

• Individual plots of certain module/slices can now be done. Additionally, the MTF can be plotted.

• The user can now adjust the relative position of the slice locations in the event the phantom is not set up to calibration conditions.

Log Analyzer

• Keys in the txt attr dict weren’t stripped and could have trailing spaces. Keys are now stripped.

VMAT

• Ability to offset the segments has been added.

  Complete overhaul to conform to new Varian RapidArc QA specs. This includes the following:

• Rather than individual samples, 4 or 7 segments are created, 5x100mm each.

• Deviation is now calculated for each segment, based on the average segment value.

• The DRMLC test has changed name to MLCS. E.g. passing a test should be: myvmat.analyze('mlcs'), not myvmat.analyze('drmlc'); the latter will still work but raises a future warning.

Starshot

• Fixed a bug where an image that did not have pixels/mm information would error out.

• Added a tolerance parameter to the analyze method.

V 0.5.1

Log Analyzer

• Axis limits are now tightened to the data when plotting log_analyzer.Axis data.

• Gamma map plot luminescence is now normalized to 1 and a colorbar was added.

• Bug #14 fixed, where Tlogs v3 were not loading couch information properly.

• Trajectory log .txt files now also load along with the .bin file if one is around.

Starshot

• Multiple images can now be superimposed to form one image for analysis.

VMAT

• load_demo_image() parameter changed from test_type to type

V 0.5.0

• A new flatness & symmetry module allows for film and EPID image analysis.

• The log_analyzer module now supports writing trajectory logs to CSV.

• A FutureWarning that pandas will be a dependency in later versions if it’s not installed.

V 0.4.1

• Batch processing of logs added via a new class.

• ~4x speedup of fluence calculations.

V 0.4.0

• A Varian MLC picket fence analysis module was added; this will analyze EPID PF images of any size and either orientation.

V 0.3.0

• Log Analyzer module added; this module reads Dynalogs and Trajectory logs from Varian linear accelerators.

Starshot

• The profile circle now aligns with the lines found.

• Recursive option added to analyze for recursive searching of a reasonable wobble.

• Image now has a cleaner interface and properties

V 0.2.1

• Demo files were not included when installed from pip

V 0.2.0

• Python 2.7 support dropped. Python 3 has a number of features that Python 2 does not, and because this project is just getting started, I didn’t want to support Python 2, and then eventually drop it as Python 3 becomes more and more mainstream.

• Internal overhaul. Modules are now in the root folder. A core module with specialized submodules was created with a number of various tools.

• Demo files were assimilated into one directory with respective subdirectories.

• VMAT module can now handle HDMLC images.

• CBCT module was restructured and is much more reliable now.

• method names normalized, specifically the return_results method, which had different names in different modules.

• Lots of tests added; coverage increased dramatically.

V 0.1.3

Overall

A module for analyzing CBCT DICOM acquisitions of a CatPhan 504 (Varian) has been added. The starshot demo files have been compressed to zip files to save space. A value decorator was added for certain functions to enforce, e.g., ranges of values that are acceptable. The “Files” directory was moved outside the source directory. -Starshot now reports the diameter instead of radius

V 0.1.2

A PyPI setup.py bug was not properly installing pylinac nor including demo files. Both of these have been fixed.

V 0.1.1

Several small bugs were fixed and small optimizations made. A few methods were refactored for similarity between modules.

V 0.1.0

This is the initial release of Pylinac. It includes two modules for doing TG-142-related tasks: Starshot & VMAT QA

Versioning mostly follows standard semantic revisioning. However, each new module will result in a bump in minor release, while bug fixes will bump patch number.