from __future__ import annotations
import io
import math
import textwrap
import warnings
import webbrowser
from io import BytesIO
from pathlib import Path
import numpy as np
from matplotlib import pyplot as plt
from plotly import graph_objects as go
from pydantic import BaseModel, ConfigDict, Field
from scipy import ndimage
from skimage import measure
from skimage.filters import threshold_li, threshold_otsu
from .core import pdf
from .core.array_utils import fill_middle_zeros, find_nearest_idx
from .core.contrast import Contrast
from .core.geometry import Line, LineSerialized, Point
from .core.image import DicomImage
from .core.mtf import MTF
from .core.plotly_utils import add_title
from .core.profile import FWXMProfile
from .core.roi import DiskROI, HighContrastDiskROI, LowContrastDiskROI, RectangleROI
from .core.utilities import QuaacDatum, ResultBase, ResultsDataMixin
from .core.warnings import capture_warnings
from .ct import (
CatPhanBase,
CatPhanModule,
Slice,
ThicknessROI,
get_regions,
rois_to_results,
)
# CT
CT_UNIFORMITY_MODULE_OFFSET_MM = 70
CT_SPATIAL_RESOLUTION_MODULE_OFFSET_MM = 100
CT_LOW_CONTRAST_MODULE_OFFSET_MM = 30
# MR
MR_SLICE11_MODULE_OFFSET_MM = 100
MR_GEOMETRIC_DISTORTION_MODULE_OFFSET_MM = 40
MR_UNIFORMITY_MODULE_OFFSET_MM = 60
MR_LOW_CONTRAST_MODULE_OFFSETS_MM = {8: 70, 9: 80, 10: 90, 11: 100}
class CTModule(CatPhanModule):
common_name = "HU Linearity"
attr_name = "ct_calibration_module"
roi_dist_mm = 63
roi_radius_mm = 10
roi_settings = {
"Air": {"angle": 45, "distance": roi_dist_mm, "radius": roi_radius_mm},
"Poly": {"angle": 225, "distance": roi_dist_mm, "radius": roi_radius_mm},
"Acrylic": {"angle": 135, "distance": roi_dist_mm, "radius": roi_radius_mm},
"Bone": {"angle": -45, "distance": roi_dist_mm, "radius": roi_radius_mm},
"Water": {"angle": 180, "distance": roi_dist_mm, "radius": roi_radius_mm},
}
window_min = -200
window_max = 200
[docs]
class CTModuleOutput(BaseModel):
"""This class should not be called directly. It is returned by the ``results_data()`` method.
Use the following attributes as normal class attributes."""
offset: float = Field(
description="The offset of the module slice in mm from the origin slice (z-direction)."
)
roi_distance_from_center_mm: float = Field(
description="The distance of the ROIs from the center of the phantom in mm in the image plane."
)
roi_radius_mm: float = Field(description="The radius of the ROIs in mm.")
roi_settings: dict = Field(
description="The ROI settings. The keys are the material names."
)
rois: dict[str, float] = Field(
description="The analyzed ROIs. The key is the name of the material and the value is the mean HU value. E.g. ``'Air': -987.1``."
)
class UniformityModule(CatPhanModule):
"""Class for analysis of the Uniformity slice of the CTP module. Measures 5 ROIs around the slice that
should all be close to the same value.
"""
attr_name = "uniformity_module"
common_name = "HU Uniformity"
roi_dist_mm = 66
roi_radius_mm = 11
roi_settings = {
"Top": {"angle": -90, "distance": roi_dist_mm, "radius": roi_radius_mm},
"Right": {"angle": 0, "distance": roi_dist_mm, "radius": roi_radius_mm},
"Bottom": {"angle": 90, "distance": roi_dist_mm, "radius": roi_radius_mm},
"Left": {"angle": 180, "distance": roi_dist_mm, "radius": roi_radius_mm},
"Center": {"angle": 0, "distance": 0, "radius": roi_radius_mm},
}
window_min = -50
window_max = 50
class SpatialResolutionModule(CatPhanModule):
"""Class for analysis of the Uniformity slice of the CTP module. Measures 5 ROIs around the slice that
should all be close to the same value.
"""
attr_name = "spatial_resolution_module"
common_name = "Spatial Resolution"
rois: dict[str, HighContrastDiskROI]
roi_dist_mm = 70
roi_radius_mm = 6
roi_settings = {
"10oclock": {
"angle": -135,
"distance": roi_dist_mm,
"radius": roi_radius_mm,
"lp/mm": 0.4,
},
"9oclock": {
"angle": -180,
"distance": roi_dist_mm,
"radius": roi_radius_mm,
"lp/mm": 0.5,
},
"7oclock": {
"angle": 135,
"distance": roi_dist_mm,
"radius": roi_radius_mm,
"lp/mm": 0.6,
},
"6oclock": {
"angle": 90,
"distance": roi_dist_mm,
"radius": roi_radius_mm,
"lp/mm": 0.7,
},
"4oclock": {
"angle": 45,
"distance": roi_dist_mm,
"radius": roi_radius_mm,
"lp/mm": 0.8,
},
"3oclock": {
"angle": 0,
"distance": roi_dist_mm,
"radius": roi_radius_mm,
"lp/mm": 0.9,
},
"2oclock": {
"angle": -45,
"distance": roi_dist_mm,
"radius": roi_radius_mm,
"lp/mm": 1.0,
},
"12oclock": {
"angle": -90,
"distance": roi_dist_mm,
"radius": roi_radius_mm,
"lp/mm": 1.2,
},
}
def _setup_rois(self) -> None:
for name, setting in self.roi_settings.items():
self.rois[name] = HighContrastDiskROI.from_phantom_center(
self.image,
setting["angle_corrected"],
setting["radius_pixels"],
setting["distance_pixels"],
self.phan_center,
contrast_threshold=1.0, # fixed to 1 so everything passes. We aren't evaluating pass/fail here
)
@property
def mtf(self) -> MTF:
spacings = [roi["lp/mm"] for roi in self.roi_settings.values()]
return MTF.from_high_contrast_diskset(
spacings=spacings, diskset=list(self.rois.values())
)
def plotly_rois(self, fig: go.Figure) -> None:
for name, roi in self.rois.items():
roi.plotly(fig, line_color="green", name=name)
def plot_rois(self, axis: plt.Axes) -> None:
"""Plot the ROIs to the axis. Override to set the color"""
for roi, mtf in zip(self.rois.values(), self.mtf.norm_mtfs.values()):
roi.plot2axes(axis, edgecolor="g")
[docs]
class SpatialResolutionModuleOutput(CTModuleOutput):
"""This class should not be called directly. It is returned by the ``results_data()`` method.
Use the following attributes as normal class attributes."""
lpmm_to_rmtf: dict = Field(
description="Line pair to relative modulation transfer mapping. The keys are the line pair values and the values are the relative modulation transfer values.",
title="Line Pair to Relative MTF",
)
class LowContrastModule(CatPhanModule):
"""Class for analysis of the Uniformity slice of the CTP module. Measures 5 ROIs around the slice that
should all be close to the same value.
"""
attr_name = "low_contrast_module"
common_name = "Low Contrast"
roi_dist_mm = 60
roi_radius_mm = 6
nominal_value = 0
roi_settings = {
"ROI": {"angle": -90, "distance": roi_dist_mm, "radius": roi_radius_mm},
}
background_roi_settings = {
"ROI": {"angle": -115, "distance": roi_dist_mm, "radius": roi_radius_mm},
}
window_min = 50
window_max = 150
def cnr(self) -> float:
"""Given in the guidance doc as |A-B|/SD where A is the contrast ROI, B is the background, and SD is stdev of B"""
return (
abs(self.rois["ROI"].pixel_value - self.background_rois["ROI"].pixel_value)
/ self.background_rois["ROI"].std
)
[docs]
class LowContrastModuleOutput(CTModuleOutput):
"""This class should not be called directly. It is returned by the ``results_data()`` method.
It is a dataclass under the hood and thus comes with all the dunder magic.
Use the following attributes as normal class attributes."""
cnr: float = Field(
description="The contrast-to-noise ratio.", title="Contrast to Noise Ratio"
)
[docs]
class ACRCTResult(ResultBase):
"""This class should not be called directly. It is returned by the ``results_data()`` method.
Use the following attributes as normal class attributes."""
phantom_model: str = Field(description="The model of the phantom used.")
phantom_roll_deg: float = Field(
description="The roll of the phantom in degrees.",
title="Phantom roll (\N{DEGREE SIGN})",
)
origin_slice: int = Field(
description="The slice number of the 'origin' slice; for ACR this is Module 1."
)
num_images: int = Field(description="The number of images in the passed dataset.")
ct_module: CTModuleOutput = Field(
description="The results of the CT module.", title="CT Module"
)
uniformity_module: UniformityModuleOutput = Field(
description="The results of the Uniformity module.",
title="HU Uniformity",
)
low_contrast_module: LowContrastModuleOutput = Field(
description="The results of the Low Contrast module.",
title="Low Contrast Resolution",
)
spatial_resolution_module: SpatialResolutionModuleOutput = Field(
description="The results of the Spatial Resolution module.",
title="Spatial Resolution",
)
[docs]
@capture_warnings
class ACRCT(CatPhanBase, ResultsDataMixin[ACRCTResult]):
_model = "ACR CT 464"
catphan_radius_mm = 100
air_bubble_radius_mm = 14
min_num_images = 4
localization_radius = 70
ct_calibration_module = CTModule
low_contrast_module = LowContrastModule
spatial_resolution_module = SpatialResolutionModule
uniformity_module = UniformityModule
clear_borders = False
def _detected_modules(self) -> list[CatPhanModule]:
return [
self.ct_calibration_module,
self.low_contrast_module,
self.spatial_resolution_module,
self.uniformity_module,
]
[docs]
def plot_analyzed_subimage(self, *args, **kwargs):
raise NotImplementedError("Use `plot_images`")
[docs]
def save_analyzed_subimage(self, *args, **kwargs):
raise NotImplementedError("Use `save_images`")
[docs]
def analyze(
self,
x_adjustment: float = 0,
y_adjustment: float = 0,
angle_adjustment: float = 0,
roi_size_factor: float = 1,
scaling_factor: float = 1,
origin_slice: int | None = None,
) -> None:
"""Analyze the ACR CT phantom
Parameters
----------
x_adjustment: float
A fine-tuning adjustment to the detected x-coordinate of the phantom center. This will move the
detected phantom position by this amount in the x-direction in mm. Positive values move the phantom to the right.
y_adjustment: float
A fine-tuning adjustment to the detected y-coordinate of the phantom center. This will move the
detected phantom position by this amount in the y-direction in mm. Positive values move the phantom down.
angle_adjustment: float
A fine-tuning adjustment to the detected angle of the phantom. This will rotate the phantom by this amount in degrees.
Positive values rotate the phantom clockwise.
roi_size_factor: float
A fine-tuning adjustment to the ROI sizes of the phantom. This will scale the ROIs by this amount.
Positive values increase the ROI sizes. In contrast to the scaling adjustment, this
adjustment effectively makes the ROIs bigger or smaller, but does not adjust their position.
scaling_factor: float
A fine-tuning adjustment to the detected magnification of the phantom. This will zoom the ROIs and phantom outline (if applicable) by this amount.
In contrast to the roi size adjustment, the scaling adjustment effectively moves the phantom and ROIs
closer or further from the phantom center. I.e. this zooms the outline and ROI positions, but not ROI size.
origin_slice: int, None
The slice number of the HU linearity module. If None, will be automatically determined. This is a fallback
method in case the automatic determination fails.
"""
self.x_adjustment = x_adjustment
self.y_adjustment = y_adjustment
self.angle_adjustment = angle_adjustment
self.roi_size_factor = roi_size_factor
self.scaling_factor = scaling_factor
self.localize(origin_slice=origin_slice)
self.ct_calibration_module = self.ct_calibration_module(
self, offset=0, clear_borders=self.clear_borders
)
self.uniformity_module = self.uniformity_module(
self,
offset=CT_UNIFORMITY_MODULE_OFFSET_MM,
clear_borders=self.clear_borders,
)
self.spatial_resolution_module = self.spatial_resolution_module(
self,
offset=CT_SPATIAL_RESOLUTION_MODULE_OFFSET_MM,
clear_borders=self.clear_borders,
)
self.low_contrast_module = self.low_contrast_module(
self,
offset=CT_LOW_CONTRAST_MODULE_OFFSET_MM,
clear_borders=self.clear_borders,
)
[docs]
def plotly_analyzed_images(
self,
show: bool = True,
show_colorbar: bool = True,
show_legend: bool = True,
**kwargs,
) -> dict[str, go.Figure]:
"""Plot the analyzed image using Plotly. Will create multiple figures.
Parameters
----------
show : bool
Whether to show the images. Set to False if doing further processing of the figure.
show_colorbar : bool
Whether to show the colorbar on the images.
show_legend : bool
Whether to show the legend on the images.
kwargs
Additional keyword arguments to pass to the figure.
"""
figs = {}
for module in (
self.ct_calibration_module,
self.uniformity_module,
self.spatial_resolution_module,
self.low_contrast_module,
):
figs[module.common_name] = module.plotly(
show_colorbar=show_colorbar, show_legend=show_legend, **kwargs
)
figs["MTF"] = self.spatial_resolution_module.mtf.plotly(
show_legend=show_legend, **kwargs
)
figs["Side View"] = self.plotly_side_view(show_legend=show_legend)
if show:
for fig in figs.values():
fig.show()
return figs
[docs]
def plot_analyzed_image(self, show: bool = True, **plt_kwargs) -> plt.Figure:
"""Plot the analyzed image
Parameters
----------
show
Whether to show the image.
plt_kwargs
Keywords to pass to matplotlib for figure customization.
"""
# set up grid and axes
fig = plt.figure(**plt_kwargs)
grid_size = (2, 3)
hu_ax = plt.subplot2grid(grid_size, (0, 0))
self.ct_calibration_module.plot(hu_ax)
unif_ax = plt.subplot2grid(grid_size, (0, 1))
self.uniformity_module.plot(unif_ax)
sr_ax = plt.subplot2grid(grid_size, (0, 2))
self.spatial_resolution_module.plot(sr_ax)
locon_ax = plt.subplot2grid(grid_size, (1, 0))
self.low_contrast_module.plot(locon_ax)
spatial_res_graph = plt.subplot2grid(grid_size, (1, 2))
self.spatial_resolution_module.mtf.plot(spatial_res_graph)
side_ax = plt.subplot2grid(grid_size, (1, 1))
self.plot_side_view(side_ax)
# finish up
plt.tight_layout()
if show:
plt.show()
return fig
[docs]
def save_analyzed_image(self, filename: str | Path | BytesIO, **plt_kwargs) -> None:
"""Save the analyzed image to disk or stream
Parameters
----------
filename
Where to save the image to
plt_kwargs
Keywords to pass to matplotlib for figure customization.
"""
fig = self.plot_analyzed_image(show=False, **plt_kwargs)
fig.savefig(filename)
[docs]
def plot_images(self, show: bool = True, **plt_kwargs) -> dict[str, plt.Figure]:
"""Plot all the individual images separately
Parameters
----------
show
Whether to show the images.
plt_kwargs
Keywords to pass to matplotlib for figure customization.
"""
figs = {}
# plot the images
modules = {
"hu": self.ct_calibration_module,
"uniformity": self.uniformity_module,
"spatial resolution": self.spatial_resolution_module,
"low contrast": self.low_contrast_module,
}
for key, module in modules.items():
fig, ax = plt.subplots(**plt_kwargs)
module.plot(ax)
figs[key] = fig
# plot the one-off MTF image
fig, ax = plt.subplots(**plt_kwargs)
figs["mtf"] = fig
self.spatial_resolution_module.mtf.plot(ax)
# plot the side view
fig, ax = plt.subplots(**plt_kwargs)
figs["side"] = fig
self.plot_side_view(ax)
plt.tight_layout()
if show:
plt.show()
return figs
[docs]
def save_images(
self,
directory: Path | str | None = None,
to_stream: bool = False,
**plt_kwargs,
) -> list[Path | BytesIO]:
"""Save separate images to disk or stream.
Parameters
----------
directory
The directory to write the images to. If None, will use current working directory
to_stream
Whether to write to stream or disk. If True, will return streams. Directory is ignored in that scenario.
plt_kwargs
Keywords to pass to matplotlib for figure customization.
"""
figs = self.plot_images(show=False, **plt_kwargs)
paths = []
for name, fig in figs.items():
if to_stream:
path = io.BytesIO()
else:
destination = Path(directory) or Path.cwd()
path = (destination / name).with_suffix(".png").absolute()
fig.savefig(path)
paths.append(path)
return paths
[docs]
def find_phantom_roll(self, func=lambda roi: roi.bbox_area) -> float:
"""Determine the "roll" of the phantom.
Only difference of base method is that we sort the ROIs by size,
not by being in the center since the two we're looking for are both right-sided.
"""
return super().find_phantom_roll(func)
[docs]
def results(self) -> str:
"""Return the results of the analysis as a string. Use with print()."""
string = (
f"\n - ACR CT 464 QA Test - \n"
f"HU ROIs: {self.ct_calibration_module.roi_vals_as_str}\n"
f"Contrast to Noise Ratio: {self.low_contrast_module.cnr():2.2f}\n"
f"Uniformity ROIs: {self.uniformity_module.roi_vals_as_str}\n"
f"Uniformity Center ROI standard deviation: {self.uniformity_module.rois['Center'].std:2.2f}\n"
f"MTF 50% (lp/mm): {self.spatial_resolution_module.mtf.relative_resolution(50):2.2f}\n"
)
return string
def _generate_results_data(self) -> ACRCTResult:
return ACRCTResult(
phantom_model="ACR CT 464",
phantom_roll_deg=self.catphan_roll,
origin_slice=self.origin_slice,
num_images=self.num_images,
ct_module=CTModuleOutput(
offset=0,
roi_distance_from_center_mm=self.ct_calibration_module.roi_dist_mm,
roi_radius_mm=self.ct_calibration_module.roi_radius_mm,
roi_settings=self.ct_calibration_module.roi_settings,
rois={
name: roi.mean
for name, roi in self.ct_calibration_module.rois.items()
},
),
uniformity_module=UniformityModuleOutput(
offset=CT_UNIFORMITY_MODULE_OFFSET_MM,
roi_distance_from_center_mm=self.uniformity_module.roi_dist_mm,
roi_radius_mm=self.uniformity_module.roi_radius_mm,
roi_settings=self.uniformity_module.roi_settings,
rois={
name: roi.mean for name, roi in self.uniformity_module.rois.items()
},
center_roi_stdev=self.uniformity_module.rois["Center"].std,
),
spatial_resolution_module=SpatialResolutionModuleOutput(
offset=CT_SPATIAL_RESOLUTION_MODULE_OFFSET_MM,
roi_distance_from_center_mm=self.spatial_resolution_module.roi_dist_mm,
roi_radius_mm=self.spatial_resolution_module.roi_radius_mm,
roi_settings=self.spatial_resolution_module.roi_settings,
rois={
name: roi.pixel_value
for name, roi in self.spatial_resolution_module.rois.items()
},
lpmm_to_rmtf=self.spatial_resolution_module.mtf.norm_mtfs,
),
low_contrast_module=LowContrastModuleOutput(
offset=CT_LOW_CONTRAST_MODULE_OFFSET_MM,
roi_distance_from_center_mm=self.low_contrast_module.roi_dist_mm,
roi_radius_mm=self.low_contrast_module.roi_radius_mm,
roi_settings=self.low_contrast_module.roi_settings,
rois={
name: roi.pixel_value
for name, roi in self.low_contrast_module.rois.items()
},
cnr=self.low_contrast_module.cnr(),
),
)
def _quaac_datapoints(self) -> dict[str, QuaacDatum]:
results_data = self.results_data(as_dict=True)
data = {}
data["Phantom Roll"] = QuaacDatum(
value=results_data["phantom_roll_deg"],
unit="degrees",
description="The roll of the phantom in the image",
)
for name, value in results_data["ct_module"]["rois"].items():
data[f"{name} HU"] = QuaacDatum(
value=value,
unit="HU",
description=f"The HU value of the {name} ROI",
)
for name, value in results_data["uniformity_module"]["rois"].items():
data[f"{name} Uniformity HU"] = QuaacDatum(
value=value,
unit="HU",
description=f"The HU value of the {name} Uniformity ROI",
)
for name, value in results_data["spatial_resolution_module"][
"lpmm_to_rmtf"
].items():
data[f"{name} lp/mm"] = QuaacDatum(
value=value,
unit="rMTF",
)
for name, value in results_data["low_contrast_module"]["rois"].items():
data[f"{name} CNR"] = QuaacDatum(
value=value,
unit="CNR",
description=f"The CNR value of the {name} ROI",
)
return data
[docs]
def publish_pdf(
self,
filename: str | Path,
notes: str | None = None,
open_file: bool = False,
metadata: dict | None = None,
logo: Path | str | None = None,
) -> None:
"""Publish (print) a PDF containing the analysis and quantitative results.
Parameters
----------
filename : (str, file-like object}
The file to write the results to.
notes : str, list of strings
Text; if str, prints single line.
If list of strings, each list item is printed on its own line.
open_file : bool
Whether to open the file using the default program after creation.
metadata : dict
Extra data to be passed and shown in the PDF. The key and value will be shown with a colon.
E.g. passing {'Author': 'James', 'Unit': 'TrueBeam'} would result in text in the PDF like:
--------------
Author: James
Unit: TrueBeam
--------------
logo: Path, str
A custom logo to use in the PDF report. If nothing is passed, the default pylinac logo is used.
"""
analysis_title = f"{self._model} Analysis"
texts = [
" - ACR CT 464 Results - ",
f"HU Linearity ROIs: {self.ct_calibration_module.roi_vals_as_str}",
f"Low contrast visibility: {self.low_contrast_module.cnr():2.2f}",
f"Uniformity ROIs: {self.uniformity_module.roi_vals_as_str}",
]
analysis_images = self.save_images(to_stream=True)
canvas = pdf.PylinacCanvas(
filename, page_title=analysis_title, metadata=metadata, logo=logo
)
if notes is not None:
canvas.add_text(text="Notes:", location=(1, 4.5), font_size=14)
canvas.add_text(text=notes, location=(1, 4))
for idx, text in enumerate(texts):
canvas.add_text(text=text, location=(1.5, 23 - idx * 0.5))
for page, img in enumerate(analysis_images):
canvas.add_new_page()
canvas.add_image(img, location=(1, 5), dimensions=(18, 18))
canvas.finish()
if open_file:
webbrowser.open(filename)
def _module_offsets(self) -> list[float]:
absolute_origin_position = self.dicom_stack[self.origin_slice].z_position
relative_offsets_mm = [
0,
CT_UNIFORMITY_MODULE_OFFSET_MM,
CT_LOW_CONTRAST_MODULE_OFFSET_MM,
CT_SPATIAL_RESOLUTION_MODULE_OFFSET_MM,
]
return [
absolute_origin_position + offset_mm for offset_mm in relative_offsets_mm
]
class MRSlice11PositionModule(CatPhanModule):
common_name = "Slice Position, Slice 11"
roi_settings = {
"Left": {"width": 2, "height": 25, "distance": 65, "angle": 2.5},
"Right": {"width": 2, "height": 25, "distance": 65, "angle": -2.5},
}
rois: dict = {}
def _setup_rois(self) -> None:
for name, setting in self.roi_settings.items():
# angle is +90 because pointing right is 0, and these rois move downward, not rightward
self.rois[name] = RectangleROI.from_phantom_center(
self.image,
setting["width_pixels"],
setting["height_pixels"],
self.catphan_roll - 90 + setting["angle"],
setting["distance_pixels"],
self.phan_center,
)
@property
def bar_difference_mm(self) -> float:
"""The difference in height between the two angled bars"""
idxs = []
for roi in (self.rois["Right"], self.rois["Left"]):
prof = roi.pixel_array.max(axis=np.argmin(roi.pixel_array.shape))
mid_height = (prof.max() - prof.min()) / 2 + prof.min()
idx = find_nearest_idx(prof, mid_height)
idxs.append(idx)
return (idxs[0] - idxs[1]) * self.mm_per_pixel
@property
def slice_shift_mm(self) -> float:
"""The effective shift in phantom position in the S/I direction. Because bars are at 45 degrees, the shift is half the bar difference"""
return self.bar_difference_mm / 2
def plot_rois(self, axis: plt.Axes) -> None:
"""Plot the ROIs to the axis.
We overload because simple rectangle ROIs don't have a pass/fail color.
"""
for roi in self.rois.values():
roi.plot2axes(axis, edgecolor="blue")
def plotly_rois(self, fig: go.Figure) -> None:
for name, roi in self.rois.items():
roi.plotly(fig, line_color="blue", name=name)
[docs]
class MRSlice11ModuleOutput(BaseModel):
"""This class should not be called directly. It is returned by the ``results_data()`` method.
Use the following attributes as normal class attributes."""
offset: int = Field(
description="The offset of the phantom in mm from the origin slice."
)
roi_settings: dict = Field(
description="The ROI settings. The keys are the ROI names."
)
rois: dict = Field(
description="The results of the left and right bar ROIs. The key is the name of the bar"
)
bar_difference_mm: float = Field(
description="The difference in bar positions in mm.",
title="Bar Difference (mm)",
)
slice_shift_mm: float = Field(
description="The measure shift in slice position compared to nominal.",
title="Slice Shift (mm)",
)
class MRSlice1Module(CatPhanModule):
common_name = "Slice 1 (Thickness, Offset, Resolution)"
slice_lines: dict[str, Line]
thickness_rois: dict[str, ThicknessROI] = {}
thickness_roi_settings = {
"Top": {"width": 100, "height": 3, "distance": -3},
"Bottom": {"width": 100, "height": 3, "distance": 2.5},
}
roi_settings = {
"Row Reference": {"radius": 9, "distance": 58, "angle": 135, "lp/mm": 0},
"Col Reference": {"radius": 9, "distance": 58, "angle": 135, "lp/mm": 0},
"Row 1.1": {"radius": 3, "distance": 40, "angle": 116, "lp/mm": 1 / 1.1},
"Col 1.1": {"radius": 3, "distance": 44, "angle": 104, "lp/mm": 1 / 1.1},
"Row 1.0": {"radius": 3, "distance": 36, "angle": 81, "lp/mm": 1.0},
"Col 1.0": {"radius": 3, "distance": 44, "angle": 74, "lp/mm": 1.0},
"Row 0.9": {"radius": 2, "distance": 46, "angle": 52, "lp/mm": 1 / 0.9},
"Col 0.9": {"radius": 2, "distance": 55, "angle": 51, "lp/mm": 1 / 0.9},
}
position_roi_settings = {
"Left": {"width": 2, "height": 25, "distance": 65, "angle": 2.5},
"Right": {"width": 2, "height": 25, "distance": 65, "angle": -2.5},
}
position_rois: dict = {}
rois: dict[str, HighContrastDiskROI]
spacings = [0, 1 / 1.1, 1, 1 / 0.9]
def _setup_rois(self) -> None:
# thickness
for name, setting in self.thickness_roi_settings.items():
# angle is +90 because pointing right is 0, and these rois move downward, not rightward
self.thickness_rois[name] = ThicknessROI.from_phantom_center(
self.image,
setting["width_pixels"],
setting["height_pixels"],
self.catphan_roll + 90,
setting["distance_pixels"],
self.phan_center,
)
# spatial res
for name, setting in self.roi_settings.items():
self.rois[name] = HighContrastDiskROI.from_phantom_center(
self.image,
setting["angle_corrected"],
setting["radius_pixels"],
setting["distance_pixels"],
self.phan_center,
contrast_threshold=1.0, # fixed to 1 so everything passes. We aren't evaluating pass/fail here
)
# slice position
for name, setting in self.position_roi_settings.items():
# angle is +90 because pointing right is 0, and these rois move downward, not rightward
self.position_rois[name] = ThicknessROI.from_phantom_center(
self.image,
setting["width_pixels"],
setting["height_pixels"],
self.catphan_roll - 90 + setting["angle"],
setting["distance_pixels"],
self.phan_center,
)
def plot_rois(self, axis: plt.Axes) -> None:
for roi in self.position_rois.values():
roi.plot2axes(axis, edgecolor="blue")
for roi in self.thickness_rois.values():
roi.plot2axes(axis, edgecolor="blue")
for roi, mtf in zip(self.rois.values(), self.rois.values()):
roi.plot2axes(axis, edgecolor="g")
def plotly_rois(self, fig: go.Figure) -> None:
for name, roi in self.position_rois.items():
roi.plotly(fig, line_color="blue", name=name)
for name, roi in self.thickness_rois.items():
roi.plotly(fig, line_color="blue", name=name)
for name, roi in self.rois.items():
roi.plotly(fig, line_color="green", name=name)
@property
def bar_difference_mm(self) -> float:
"""The difference in height between the two angled bars"""
left_array = self.position_rois["Left"].long_profile.values
left_mid_height = (left_array.max() - left_array.min()) / 2 + left_array.min()
left_idx = find_nearest_idx(left_array, left_mid_height)
right_array = self.position_rois["Right"].long_profile.values
right_mid_height = (
right_array.max() - right_array.min()
) / 2 + right_array.min()
right_idx = find_nearest_idx(right_array, right_mid_height)
return (right_idx - left_idx) * self.mm_per_pixel
@property
def slice_shift_mm(self) -> float:
"""The effective shift in phantom position in the S/I direction. Because bars are at 45 degrees, the shift is half the bar difference"""
return self.bar_difference_mm / 2
@property
def measured_slice_thickness_mm(self) -> float:
"""The slice thickness as determined by the two angled ROIs in the center of Slice 1"""
top = self.thickness_rois["Top"].wire_fwhm * self.mm_per_pixel
bottom = self.thickness_rois["Bottom"].wire_fwhm * self.mm_per_pixel
return 0.2 * (top * bottom) / (top + bottom)
@property
def row_mtf(self) -> MTF:
"""The MTF of the spatial resolution module looking at the row-wise ROIs"""
return MTF.from_high_contrast_diskset(
spacings=self.spacings,
diskset=list(roi for name, roi in self.rois.items() if "Row" in name),
)
@property
def col_mtf(self) -> MTF:
"""The MTF of the spatial resolution module looking at the column-wise ROIs"""
return MTF.from_high_contrast_diskset(
spacings=self.spacings,
diskset=list(roi for name, roi in self.rois.items() if "Col" in name),
)
[docs]
class MRSlice1ModuleOutput(BaseModel):
"""This class should not be called directly. It is returned by the ``results_data()`` method.
Use the following attributes as normal class attributes."""
offset: int = Field(
description="The offset of the phantom in mm from the origin slice."
)
roi_settings: dict = Field(
description="A dictionary of the ROI settings. The keys are the ROI names."
)
rois: dict = Field(
description=" A dictionary of the analyzed MTF ROIs. The key is the name of the ROI; e.g. ``Row 1.1``."
)
bar_difference_mm: float = Field(
description="The difference in bar positions in mm.",
title="Bar Difference (mm)",
)
slice_shift_mm: float = Field(
description="The measured shift in slice position compared to nominal.",
title="Slice Shift (mm)",
)
measured_slice_thickness_mm: float = Field(
description="The measured slice thickness in mm.",
title="Measured Slice Thickness (mm)",
)
row_mtf_50: float = Field(
description="The MTF at 50% for the row-based ROIs.",
title="Row-wise 50% MTF (lp/mm)",
)
col_mtf_50: float = Field(
description="The MTF at 50% for the column-based ROIs.",
title="Column-wise 50% MTF (lp/mm)",
)
row_mtf_lp_mm: dict[int, float] = Field(
description="A key-value pair of the MTF. The key is the relative resolution in % and the value is the lp/mm at that resolution",
title="MTF (lp/mm)",
)
col_mtf_lp_mm: dict[int, float] = Field(
description="A key-value pair of the MTF. The key is the relative resolution in % and the value is the lp/mm at that resolution",
title="MTF (lp/mm)",
)
class MRUniformityModule(CatPhanModule):
"""Class for analysis of the Uniformity slice of the CTP module. Measures 5 ROIs around the slice that
should all be close to the same value.
"""
common_name = "Signal Uniformity"
roi_settings = {
"Center": {
"angle": 90,
"distance": 5,
"radius": 80,
}, # 80 radius ~= 200cm2, per the manual
}
ghost_roi_settings = {
# size of ~900mm2 per the manual
"Top": {"angle": -90, "distance": 110, "width": 60, "height": 15},
"Bottom": {"angle": 90, "distance": 110, "width": 60, "height": 15},
"Left": {"angle": 180, "distance": 110, "width": 15, "height": 60},
"Right": {"angle": 0, "distance": 110, "width": 15, "height": 60},
}
ghost_rois: dict = {}
def __init__(self, catphan, offset):
self.tesla = float(catphan.dicom_stack.metadata.MagneticFieldStrength)
super().__init__(catphan, tolerance=None, offset=offset)
def _setup_rois(self) -> None:
super()._setup_rois()
for name, roi in self.ghost_roi_settings.items():
self.ghost_rois[name] = RectangleROI.from_phantom_center(
self.image,
roi["width_pixels"],
roi["height_pixels"],
roi["angle"] + self.catphan_roll,
roi["distance_pixels"],
self.phan_center,
)
def plot_rois(self, axis: plt.Axes) -> None:
super().plot_rois(axis)
for roi in self.ghost_rois.values():
roi.plot2axes(axis, edgecolor="yellow")
def plotly_rois(self, fig: go.Figure) -> None:
super().plotly_rois(fig)
for name, roi in self.ghost_rois.items():
roi.plotly(fig, line_color="yellow", name=name)
@property
def percent_image_uniformity(self) -> float:
"""PIU value calculated via section 5.3 of the manual"""
piu_high = np.percentile(self.rois["Center"].pixel_values, 99)
piu_low = np.percentile(self.rois["Center"].pixel_values, 1)
return 100 * (1 - ((piu_high - piu_low) / (piu_high + piu_low)))
@property
def piu_passed(self) -> bool:
"""Section 5.4"""
if self.tesla < 3:
return self.percent_image_uniformity > 85
else:
return self.percent_image_uniformity > 80
@property
def ghosting_ratio(self) -> float:
"""Ghosting ratio of section 6.3 of the manual"""
top = self.ghost_rois["Top"].pixel_value
bottom = self.ghost_rois["Bottom"].pixel_value
left = self.ghost_rois["Left"].pixel_value
right = self.ghost_rois["Right"].pixel_value
return abs(
((top + bottom) - (left + right)) / (2 * self.rois["Center"].pixel_value)
)
@property
def psg(self) -> float:
"""Percent Signal Ghosting"""
return self.ghosting_ratio * 100
@property
def psg_passed(self) -> bool:
"""Whether the PSG is within tolerance"""
return self.psg < 3.0
class MRLowContrastModule(CatPhanModule):
"""Class for analysis of the Low Contrast of a single slice of the ACR MRI Large phantom.
It counts complete spokes where all 3 disks are visible.
Each slice has 10 spokes arranged in a circular pattern, with spoke diameters decreasing
clockwise from 7.0 mm (spoke 1) to 1.5 mm (spoke 10).
"""
attr_name = "low_contrast_module"
low_contrast_region_radius = 40 # mm
_distances = [12.75, 25.50, 38.25]
_rsf = 0.8 / 2 # roi_size_factor (/2 to convert diameter to radius)
roi_settings = {
"spoke_1": {"angle": -90, "radius": 7.0 * _rsf, "distances": _distances},
"spoke_2": {"angle": -54, "radius": 6.4 * _rsf, "distances": _distances},
"spoke_3": {"angle": -18, "radius": 5.8 * _rsf, "distances": _distances},
"spoke_4": {"angle": 18, "radius": 5.2 * _rsf, "distances": _distances},
"spoke_5": {"angle": 54, "radius": 4.6 * _rsf, "distances": _distances},
"spoke_6": {"angle": 90, "radius": 3.9 * _rsf, "distances": _distances},
"spoke_7": {"angle": 126, "radius": 3.3 * _rsf, "distances": _distances},
"spoke_8": {"angle": 162, "radius": 2.7 * _rsf, "distances": _distances},
"spoke_9": {"angle": 198, "radius": 2.1 * _rsf, "distances": _distances},
"spoke_10": {"angle": 234, "radius": 1.5 * _rsf, "distances": _distances},
}
_bg_distances = [0, 20, 32]
_bg_roi_radius = 2.5
_angle_offset = 0
background_roi_settings = {
"spoke_1": {
"angle": -90 + _angle_offset,
"radius": _bg_roi_radius,
"distances": _bg_distances,
},
"spoke_2": {
"angle": -54 + _angle_offset,
"radius": _bg_roi_radius,
"distances": _bg_distances,
},
"spoke_3": {
"angle": -18 + _angle_offset,
"radius": _bg_roi_radius,
"distances": _bg_distances,
},
"spoke_4": {
"angle": 18 + _angle_offset,
"radius": _bg_roi_radius,
"distances": _bg_distances,
},
"spoke_5": {
"angle": 54 + _angle_offset,
"radius": _bg_roi_radius,
"distances": _bg_distances,
},
"spoke_6": {
"angle": 90 + _angle_offset,
"radius": _bg_roi_radius,
"distances": _bg_distances,
},
"spoke_7": {
"angle": 126 + _angle_offset,
"radius": _bg_roi_radius,
"distances": _bg_distances,
},
"spoke_8": {
"angle": 162 + _angle_offset,
"radius": _bg_roi_radius,
"distances": _bg_distances,
},
"spoke_9": {
"angle": 198 + _angle_offset,
"radius": _bg_roi_radius,
"distances": _bg_distances,
},
"spoke_10": {
"angle": 234 + _angle_offset,
"radius": _bg_roi_radius,
"distances": _bg_distances,
},
}
def __init__(
self,
catphan,
contrast_method: str,
tolerance: float,
offset: int,
spoke_start_angle: float,
visibility_sanity_multiplier: float,
):
self.contrast_method = contrast_method
self._spoke_start_angle = spoke_start_angle
self.visibility_sanity_multiplier = visibility_sanity_multiplier
super().__init__(catphan, tolerance, offset)
@property
def window_min(self) -> int:
return int(self.low_contrast_region.min)
@property
def window_max(self) -> int:
return int(self.low_contrast_region.max)
def _convert_units_in_settings(self) -> None:
super()._convert_units_in_settings()
for roi, settings in self.roi_settings.items():
settings["distances_pixels"] = [
d * self.scaling_factor / self.mm_per_pixel
for d in settings["distances"]
]
for roi, settings in self.background_roi_settings.items():
settings["distances_pixels"] = [
d * self.scaling_factor / self.mm_per_pixel
for d in settings["distances"]
]
def _setup_rois(self) -> None:
"""Set up ROIs for all spokes and disks.
Creates low contrast and background ROIs for each of the 3 disks in each of the 10 spokes.
It assumes ``roi_settings`` and ``background_roi_settings`` have the same dictionary structure.
ROIs are organized as nested structure: rois[spoke_num][disk_num]
"""
self.common_name = f"Low Contrast - {self.slice_num + 1}"
self.rois: dict[str, list[LowContrastDiskROI]] = {}
self.background_rois: dict[str, list[LowContrastDiskROI]] = {}
### Detect the low contrast region containing the objects
rad_pix = self.low_contrast_region_radius / self.mm_per_pixel
nominal_area = rad_pix * rad_pix * np.pi
larr, _, _ = get_regions(self)
larr = measure.label(~larr)
prop = measure.regionprops(larr)
lc_region = min(prop, key=lambda x: np.abs(x.area - nominal_area))
actual_area = lc_region.area
is_proper_size = abs(actual_area / nominal_area - 1) < 0.3
if not is_proper_size:
raise ValueError("Unable to find the Low Contrast region.")
centroid = lc_region.centroid
lc_center = Point(centroid[1], centroid[0])
lc_region_radius = rad_pix
self.low_contrast_region = DiskROI(self.image, lc_region_radius, lc_center)
### Setup objects (it assumes low contrast and background ROIs have the same dictionary structure.)
if len(self.roi_settings) != len(self.background_roi_settings):
msg = "There should be the same number of ROIs for low contrast and background."
raise ValueError(msg)
if len(self.roi_settings.keys()) != len(self.background_roi_settings.keys()):
msg = "Low contrast and background dictionaries must have the same keys."
raise ValueError(msg)
for spoke_name in self.roi_settings.keys():
lc_rois: list[LowContrastDiskROI] = [] # low contrast
bg_rois: list[LowContrastDiskROI] = [] # background
for idx in range(len(self.roi_settings[spoke_name]["distances_pixels"])):
bg_setting = self.background_roi_settings[spoke_name]
bg_roi = LowContrastDiskROI.from_phantom_center(
self.image,
bg_setting["angle_corrected"] + self._spoke_start_angle,
bg_setting["radius_pixels"],
bg_setting["distances_pixels"][idx],
lc_center,
)
bg_rois.append(bg_roi)
lc_setting = self.roi_settings[spoke_name]
lc_roi = LowContrastDiskROI.from_phantom_center(
self.image,
lc_setting["angle_corrected"] + self._spoke_start_angle,
max(lc_setting["radius_pixels"], 1), # ensure 2 pix to avoid std=0#
lc_setting["distances_pixels"][idx],
lc_center,
contrast_reference=bg_roi.mean,
contrast_method=self.contrast_method,
visibility_threshold=self.tolerance,
)
lc_rois.append(lc_roi)
self.rois[spoke_name] = lc_rois
self.background_rois[spoke_name] = bg_rois
@property
def score(self) -> int:
"""The number of complete spokes for this slice.
A spoke is complete only if all 3 disks are visible (visibility > threshold).
Stop counting at the first incomplete spoke.
"""
spoke1 = self.rois[list(self.roi_settings.keys())[0]]
max_visibility = max(r.visibility for r in spoke1)
sanity_visibility = max_visibility * self.visibility_sanity_multiplier
is_visible = [
all(self.roi_is_visible(r, sanity_visibility) for r in s)
for s in self.rois.values()
]
return len(is_visible) if all(is_visible) else np.argmin(is_visible)
@staticmethod
def roi_is_visible(roi: LowContrastDiskROI, sanity_visibility: float) -> bool:
"""Helper method to determine if a roi is visible, including sanity check"""
return roi.passed_visibility and roi.visibility < sanity_visibility
def as_dict(self) -> dict:
"""Dump important data as a dictionary."""
return {
spoke_name: [roi.as_dict() for roi in spoke_rois]
for spoke_name, spoke_rois in self.rois.items()
}
def plot_rois(self, axis: plt.Axes) -> None:
"""Plot the ROIs to the axis."""
spoke1 = self.rois[list(self.roi_settings.keys())[0]]
max_visibility = max(r.visibility for r in spoke1)
sanity_visibility = max_visibility * self.visibility_sanity_multiplier
# low contrast region
self.low_contrast_region.plot2axes(axis, edgecolor="blue")
# low contrast rois
for spoke in self.rois.values():
for roi in spoke:
roi_is_visible = self.roi_is_visible(roi, sanity_visibility)
color = "green" if roi_is_visible else "red"
roi.plot2axes(axis, edgecolor=color)
# background rois
for spoke in self.background_rois.values():
for roi in spoke:
roi.plot2axes(axis, edgecolor="blue")
def plotly_rois(self, fig: go.Figure) -> None:
spoke1 = self.rois[list(self.roi_settings.keys())[0]]
max_visibility = max(r.visibility for r in spoke1)
sanity_visibility = max_visibility * self.visibility_sanity_multiplier
# low contrast region
self.low_contrast_region.plotly(fig, line_color="blue")
# low contrast rois
for spoke in self.rois.values():
for roi in spoke:
roi_is_visible = self.roi_is_visible(roi, sanity_visibility)
color = "green" if roi_is_visible else "red"
roi.plotly(fig, line_color=color)
# background rois
for spoke in self.background_rois.values():
for roi in spoke:
roi.plotly(fig, line_color="blue")
[docs]
class MRLowContrastModuleOutput(BaseModel):
"""This class should not be called directly. It is returned by the ``results_data()`` method.
Use the following attributes as normal class attributes."""
offset: float = Field(
description="The offset of the slice in mm from the origin slice."
)
slice_num: int = Field(description="The slice number.")
spoke_settings: dict = Field(description="A dictionary of the spoke settings.")
background_settings: dict = Field(
description="A dictionary of the background roi settings."
)
spokes: dict = Field(description="A dictionary of the spokes.")
class MRLowContrastMultiSliceModule:
"""Class for managing Low Contrast analysis across multiple slices."""
roi_settings = {
"slice_8": {
"offset": MR_LOW_CONTRAST_MODULE_OFFSETS_MM[8],
"spoke_start_angle": 0,
},
"slice_9": {
"offset": MR_LOW_CONTRAST_MODULE_OFFSETS_MM[9],
"spoke_start_angle": 9,
},
"slice_10": {
"offset": MR_LOW_CONTRAST_MODULE_OFFSETS_MM[10],
"spoke_start_angle": 18,
},
"slice_11": {
"offset": MR_LOW_CONTRAST_MODULE_OFFSETS_MM[11],
"spoke_start_angle": 27,
},
}
def __init__(
self,
catphan,
contrast_method: str,
visibility_threshold: float,
visibility_sanity_multiplier: float,
):
"""Initialize the multi-slice Low Contrast Detectability module.
Parameters
----------
catphan : CatPhanBase
The ACR MRI Large phantom instance.
contrast_method : str
The contrast equation to use. See :ref:`low_contrast_topic`.
visibility_threshold : float
The visibility threshold for determining if a disk is visible.
visibility_sanity_multiplier: float
This applies to the smallest low-contrast ROIs. Due to the low standard deviation in small ROIs,
the calculated visibility can become significantly larger than is physically reasonable.
This sanity threshold ensures that a small ROI is considered not visible if its visibility
exceeds that of the first spoke by this multiplier.
"""
self.slices: dict[str, MRLowContrastModule] = {}
for key, value in self.roi_settings.items():
self.slices[key] = MRLowContrastModule(
catphan=catphan,
contrast_method=contrast_method,
tolerance=visibility_threshold,
offset=value["offset"],
spoke_start_angle=value["spoke_start_angle"],
visibility_sanity_multiplier=visibility_sanity_multiplier,
)
@property
def score(self) -> int:
"""Get the total low contrast detectability score as the sum of the number of complete spokes."""
return sum(s.score for s in self.slices.values())
[docs]
class MRLowContrastMultiSliceModuleOutput(BaseModel):
"""This class should not be called directly. It is returned by the ``results_data()`` method.
Use the following attributes as normal class attributes."""
score: int = Field(
description="The total score across all slices (sum of all slice scores).",
title="Total Score",
)
low_contrast_rois: dict = Field(description="Low contrast outputs.")
class GeometricDistortionModule(CatPhanModule):
"""Class for analysis of the Uniformity slice of the CTP module. Measures 5 ROIs around the slice that
should all be close to the same value.
"""
common_name = "Geometric Distortion"
profiles: dict
def _setup_rois(self) -> None:
"""This is mostly for plotting purposes. This is why we use FWXMProfile
instead of FWXMProfilePhysical. The lines to plot should be in pixel coordinates, not physical.
We convert to physical just for the field width calculation."""
px_to_cut_off = int(round(5 / self.mm_per_pixel))
self.profiles = {}
threshold = threshold_otsu(image=self.image.array)
bin_image = self.image.as_binary(threshold=threshold)
bin_image = ndimage.binary_fill_holes(bin_image).astype(float)
# calculate horizontal
data = bin_image[int(self.phan_center.y), :]
# cutoff 3mm from the search area
f_data = fill_middle_zeros(data, cutoff_px=px_to_cut_off)
prof = FWXMProfile(values=f_data)
line = Line(
Point(prof.field_edge_idx(side="left"), self.phan_center.y),
Point(prof.field_edge_idx(side="right"), self.phan_center.y),
)
self.profiles["horizontal"] = {
"width (mm)": prof.field_width_px * self.mm_per_pixel,
"line": line,
}
# calculate vertical
data = bin_image[:, int(self.phan_center.x)]
f_data = fill_middle_zeros(data, cutoff_px=px_to_cut_off)
prof = FWXMProfile(values=f_data)
line = Line(
Point(self.phan_center.x, prof.field_edge_idx(side="left")),
Point(self.phan_center.x, prof.field_edge_idx(side="right")),
)
self.profiles["vertical"] = {
"width (mm)": prof.field_width_px * self.mm_per_pixel,
"line": line,
}
# calculate negative diagonal
# calculate slope equation intercept
# b = y - (+1)x
b = self.phan_center.y - self.phan_center.x
xs = np.arange(0, self.image.shape[1])
ys = xs + b
coords = ndimage.map_coordinates(bin_image, [ys, xs], order=1, mode="mirror")
f_data = fill_middle_zeros(coords, cutoff_px=px_to_cut_off)
prof = FWXMProfile(values=f_data)
line = Line(
Point(
xs[int(round(prof.field_edge_idx(side="left")))],
ys[int(round(prof.field_edge_idx(side="left")))],
),
Point(
xs[int(round(prof.field_edge_idx(side="right")))],
ys[int(round(prof.field_edge_idx(side="right")))],
),
)
# pixels are now diagonal and thus spacing between pixels is now the hypotenuse
# We don't have to fix the line above because that's in pixels. The issue is the
# geometric distance and thus we only have to correct here.
self.profiles["negative diagonal"] = {
"width (mm)": prof.field_width_px * self.mm_per_pixel * math.sqrt(2),
"line": line,
}
# calculate positive diagonal
# calculate slope equation intercept
# b = y - (-1)x
b = self.phan_center.y + self.phan_center.x
ys = -xs + b
coords = ndimage.map_coordinates(bin_image, [ys, xs], order=1, mode="mirror")
f_data = fill_middle_zeros(coords, cutoff_px=px_to_cut_off)
prof = FWXMProfile(values=f_data)
line = Line(
Point(
xs[int(round(prof.field_edge_idx(side="left")))],
ys[int(round(prof.field_edge_idx(side="left")))],
),
Point(
xs[int(round(prof.field_edge_idx(side="right")))],
ys[int(round(prof.field_edge_idx(side="right")))],
),
)
self.profiles["positive diagonal"] = {
"width (mm)": prof.field_width_px * self.mm_per_pixel * math.sqrt(2),
"line": line,
}
def plotly_rois(self, fig: go.Figure) -> None:
for name, profile_data in self.profiles.items():
profile_data["line"].plotly(fig, line_width=2, color="blue", name=name)
def plot_rois(self, axis: plt.Axes):
for name, profile_data in self.profiles.items():
profile_data["line"].plot2axes(axis, width=2, color="blue")
def distances(self) -> dict:
"""The measurements of the phantom size for all 4 lines in mm"""
return {name: f"{p['width (mm)']:2.2f}mm" for name, p in self.profiles.items()}
[docs]
class MRGeometricDistortionModuleOutput(BaseModel):
"""This class should not be called directly. It is returned by the ``results_data()`` method.
Use the following attributes as normal class attributes."""
model_config = ConfigDict(arbitrary_types_allowed=True)
offset: int = Field(
description="The offset of the phantom in mm from the origin slice."
)
profiles: dict[str, dict[str, float | LineSerialized]] = Field(
description="A dictionary of the profiles used to measure the geometric distortion. The key is the name of the profile.",
title="Profile widths (mm)",
)
distances: dict = Field(
description="The lines measuring the ROI size. The key is the name of the line direction and the value is a string of the line length.",
title="Distance measurements (mm)",
)
class SagittalLocalizationModule:
"""Class for analysis of the Sagittal slice of the ACR Phantom."""
"""This class shares some similarities with CatphanModule but also has key differences.
The options considered were: inherit to override, refactor into a common abstraction,
or duplicate plot code for a standalone implementation.
By design choice, the latter was taken."""
common_name = "Sagittal Distortion"
roi_settings: dict[str, dict[str, float]] = {
"ROI1": {"offset": -75},
"ROI2": {"offset": -25},
"ROI3": {"offset": 25},
"ROI4": {"offset": 75},
} # offsets in mm left/right from phantom centroid
rois: dict[str, Line] = {}
profiles: dict = {}
image: DicomImage
window_min: int | None = None # plt visualization
window_max: int | None = None # plt visualization
def __init__(self, image: DicomImage | None):
if image is None:
return
self.image = image
# The processing is consistent with the geometric distortion module
threshold = round(threshold_li(image.array))
bin_image = image.as_binary(threshold=threshold)
bin_image = ndimage.binary_fill_holes(bin_image).astype(float)
centroid = np.argwhere(bin_image).mean(axis=0)
pixel_size = 1 / image.dpmm
for key, val in self.roi_settings.items():
offset_px = val["offset"] * pixel_size
col = round(centroid[1] + offset_px)
data = bin_image[:, col]
prof = FWXMProfile(values=data)
line = Line(
Point(col, prof.field_edge_idx(side="left")),
Point(col, prof.field_edge_idx(side="right")),
)
self.profiles[key] = {
"width (mm)": prof.field_width_px * pixel_size,
"line": line,
}
self.rois[key] = line
def distances(self) -> dict:
"""The measurements of the phantom size in mm"""
return {name: f"{p['width (mm)']:2.2f}mm" for name, p in self.profiles.items()}
def plot(self, axis: plt.Axes):
"""Plot the image along with ROIs to an axis"""
self.image.plot(ax=axis, show=False, vmin=self.window_min, vmax=self.window_max)
self.plot_rois(axis)
axis.autoscale(tight=True)
axis.set_title(f"{self.common_name}")
axis.axis("off")
def plotly(self, **kwargs) -> go.Figure:
"""Plot the image along with the ROIs to a plotly figure."""
fig = go.Figure()
self.image.plotly(
fig, show=False, zmin=self.window_min, zmax=self.window_max, **kwargs
)
self.plotly_rois(fig)
add_title(fig, f"{self.common_name}")
return fig
def plotly_rois(self, fig: go.Figure) -> None:
for name, profile_data in self.profiles.items():
profile_data["line"].plotly(fig, line_width=2, color="blue", name=name)
def plot_rois(self, axis: plt.Axes):
for name, profile_data in self.profiles.items():
profile_data["line"].plot2axes(axis, width=2, color="blue")
[docs]
class MRSagittalLocalizationModuleOutput(BaseModel):
"""This class should not be called directly. It is returned by the ``results_data()`` method.
Use the following attributes as normal class attributes."""
model_config = ConfigDict(arbitrary_types_allowed=True)
profiles: dict[str, dict[str, float | LineSerialized]] = Field(
description="A dictionary of the profiles used to measure the geometric distortion. The key is the name of the profile.",
title="Profile widths (mm)",
)
distances: dict = Field(
description="The lines measuring the ROI size. The key is the name of the line direction and the value is a string of the line length.",
title="Distance measurements (mm)",
)
[docs]
class ACRMRIResult(ResultBase):
"""This class should not be called directly. It is returned by the ``results_data()`` method.
Use the following attributes as normal class attributes."""
phantom_model: str = Field(description="The model of the phantom used.")
phantom_roll_deg: float = Field(description="The roll of the phantom in degrees.")
origin_slice: int = Field(
description="The slice number of the 'origin' slice; for ACR this is Slice 1."
)
num_images: int = Field(description="The number of images in the passed dataset.")
slice1: MRSlice1ModuleOutput = Field(
description="The results for the 'Slice 1' module", title="Slice 1 Module"
)
slice11: MRSlice11ModuleOutput = Field(
description="The results for the 'Slice 11' module", title="Slice 11 Module"
)
uniformity_module: MRUniformityModuleOutput = Field(
description="Results from the uniformity module", title="Uniformity Module"
)
geometric_distortion_module: MRGeometricDistortionModuleOutput = Field(
description="Results from the geometric distortion module",
title="Geometric Distortion Module",
)
sagittal_localizer_module: MRSagittalLocalizationModuleOutput = Field(
description="Results from the sagittal localizer module",
title="Sagittal Localization Module",
)
low_contrast_multi_slice_module: MRLowContrastMultiSliceModuleOutput = Field(
description="Results from the low contrast modules",
title="Low Contrast Multi Slice",
)
[docs]
@capture_warnings
class ACRMRILarge(CatPhanBase, ResultsDataMixin[ACRMRIResult]):
_model = "ACR MRI Large"
catphan_radius_mm = 100
min_num_images = 4
air_bubble_radius_mm = 20
slice1 = MRSlice1Module
geometric_distortion = GeometricDistortionModule
uniformity_module = MRUniformityModule
slice11 = MRSlice11PositionModule
sagittal_localization = SagittalLocalizationModule
low_contrast_multi_slice = MRLowContrastMultiSliceModule
has_sagittal_module: bool = False
clip_in_localization = False
low_contrast_visibility_sanity_multiplier: float
[docs]
def plot_analyzed_subimage(self, *args, **kwargs):
raise NotImplementedError("Use `plot_images`")
[docs]
def save_analyzed_subimage(self, *args, **kwargs):
raise NotImplementedError("Use `save_images`")
[docs]
def localize(self) -> None:
self._phantom_center_func = self.find_phantom_axis()
self.catphan_roll = self.find_phantom_roll() + self.angle_adjustment
# now that we have the origin slice, ensure we have scanned all linked modules
if not self._ensure_physical_scan_extent():
raise ValueError(
"The physical scan extent does not cover the extent of module configuration. "
"This means not all modules were included in the scan. Rescan the phantom to include all "
"relevant modules, or change the offset values."
)
def _module_offsets(self) -> list[float]:
absolute_origin_position = self.dicom_stack[self.origin_slice].z_position
relative_offsets_mm = [
0,
MR_GEOMETRIC_DISTORTION_MODULE_OFFSET_MM,
MR_UNIFORMITY_MODULE_OFFSET_MM,
MR_SLICE11_MODULE_OFFSET_MM,
]
relative_offsets_mm.extend(MR_LOW_CONTRAST_MODULE_OFFSETS_MM.values())
return [
absolute_origin_position + offset_mm for offset_mm in relative_offsets_mm
]
[docs]
def find_phantom_roll(self) -> float:
"""Determine the "roll" of the phantom. This algorithm uses the circular left-upper hole on slice 1 as the reference
Returns
-------
float : the angle of the phantom in **degrees**.
"""
# get edges and make ROIs from it
slice = Slice(self, self.origin_slice)
larr, regions, _ = get_regions(slice)
try:
# find appropriate ROIs and grab the two most centrally positioned ones
circle_bubbles = [
r
for r in regions
if (self._is_right_area(r) and self._is_right_eccentricity(r))
]
exact_size = np.pi * ((self.air_bubble_radius_mm / self.mm_per_pixel) ** 2)
most_similar_bubble = sorted(
circle_bubbles, key=lambda r: abs(r.filled_area - exact_size)
)[0]
y_dist = most_similar_bubble.centroid[0] - slice.phan_center.y
x_dist = most_similar_bubble.centroid[1] - slice.phan_center.x
phan_roll = np.arctan2(y_dist, x_dist)
corrected_roll = (
np.rad2deg(phan_roll) + 135
) # bubble is at top-left. perfect placement is -135
return corrected_roll
except Exception:
raise RuntimeError(
"Could not determine the roll of the phantom. Ensure the 20mm top-left circle is visible on Slice 1"
)
[docs]
def analyze(
self,
echo_number: int | None = None,
x_adjustment: float = 0,
y_adjustment: float = 0,
angle_adjustment: float = 0,
roi_size_factor: float = 1,
scaling_factor: float = 1,
low_contrast_method: str = Contrast.WEBER,
low_contrast_visibility_threshold: float = 0.001,
low_contrast_visibility_sanity_multiplier: float = 3,
) -> None:
"""Analyze the ACR CT phantom
Parameters
----------
echo_number:
The echo to analyze. If not passed, uses the minimum echo number found.
x_adjustment: float
A fine-tuning adjustment to the detected x-coordinate of the phantom center. This will move the
detected phantom position by this amount in the x-direction in mm. Positive values move the phantom to the right.
y_adjustment: float
A fine-tuning adjustment to the detected y-coordinate of the phantom center. This will move the
detected phantom position by this amount in the y-direction in mm. Positive values move the phantom down.
angle_adjustment: float
A fine-tuning adjustment to the detected angle of the phantom. This will rotate the phantom by this amount in degrees.
Positive values rotate the phantom clockwise.
roi_size_factor: float
A fine-tuning adjustment to the ROI sizes of the phantom. This will scale the ROIs by this amount.
Positive values increase the ROI sizes. In contrast to the scaling adjustment, this
adjustment effectively makes the ROIs bigger or smaller, but does not adjust their position.
scaling_factor: float
A fine-tuning adjustment to the detected magnification of the phantom. This will zoom the ROIs and phantom outline (if applicable) by this amount.
In contrast to the roi size adjustment, the scaling adjustment effectively moves the phantom and ROIs
closer or further from the phantom center. I.e. this zooms the outline and ROI positions, but not ROI size.
low_contrast_method: str
The contrast equation to use in the low contrast modules. See
:ref:`low_contrast_topic`.
low_contrast_visibility_threshold: float
The visibility threshold for determining if an object is visible in the low contrast modules.
low_contrast_visibility_sanity_multiplier: float
This applies to the smallest low-contrast ROIs. Due to the low standard deviation in small ROIs,
the calculated visibility can become significantly larger than is physically reasonable.
This sanity threshold ensures that a small ROI is considered not visible if its visibility
exceeds that of the first spoke by this multiplier.
"""
self.x_adjustment = x_adjustment
self.y_adjustment = y_adjustment
self.angle_adjustment = angle_adjustment
self.roi_size_factor = roi_size_factor
self.scaling_factor = scaling_factor
self.low_contrast_visibility_sanity_multiplier = (
low_contrast_visibility_sanity_multiplier
)
self._select_echo_images(echo_number)
sagittal_image = self._select_sagittal_image()
self.has_sagittal_module = sagittal_image is not None
self.localize()
self.slice1 = self.slice1(self, offset=0)
self.geometric_distortion = self.geometric_distortion(
self, offset=MR_GEOMETRIC_DISTORTION_MODULE_OFFSET_MM
)
self.uniformity_module = self.uniformity_module(
self, offset=MR_UNIFORMITY_MODULE_OFFSET_MM
)
self.slice11 = self.slice11(self, offset=MR_SLICE11_MODULE_OFFSET_MM)
self.sagittal_localization = self.sagittal_localization(sagittal_image)
self.low_contrast_multi_slice = self.low_contrast_multi_slice(
self,
contrast_method=low_contrast_method,
visibility_threshold=low_contrast_visibility_threshold,
visibility_sanity_multiplier=low_contrast_visibility_sanity_multiplier,
)
def _select_echo_images(self, echo_number: int | None) -> None:
"""Select out the images that match the given echo number"""
# we check for multiple echos. We only pick the first echo found.
# this is probably not the best logic but we somehow have to pick
# Echo Numbers is an int; https://dicom.innolitics.com/ciods/mr-image/mr-image/00180086
# in case EchoNumbers isn't there, use all
try:
all_echos = {int(i.metadata.EchoNumbers) for i in self.dicom_stack}
except AttributeError:
# no manipulation; use all images
return
if echo_number is None:
echo_number = min(all_echos)
if len(all_echos) > 1:
warnings.warn(
f"Multiple echoes found ({all_echos}) and no echo number was passed. Using echo # {echo_number}"
)
if echo_number not in all_echos:
raise ValueError(
f"Echo number {echo_number} was passed but not found in the dataset. Found echo numbers: {all_echos}. Remove the echo_number parameter or pick a valid echo number."
)
# drop images that don't have the same echo number
to_pop = []
for idx, img in enumerate([i for i in self.dicom_stack].copy()):
if int(img.metadata.EchoNumbers) != echo_number:
to_pop.append(idx)
for idx in sorted(to_pop, reverse=True):
del self.dicom_stack[idx]
del self.dicom_stack.metadatas[idx]
def _select_sagittal_image(self, max_dist: float = 0.01) -> DicomImage | None:
"""This function return the sagittal image from the dicom stack.
Parameters
----------
max_dist: float
Max vectorial distance from the nominal to the actual image orientation.
"""
# It uses the table in this article to find the sagittal image
# https://www.kaggle.com/code/rickandjoe/mri-orientation-axial-sagittal-or-coronal
nominal_sagittal_image_orientation = np.array([0, 1, 0, 0, 0, -1])
metadatas = self.dicom_stack.metadatas
image_orientation = [m.ImageOrientationPatient for m in metadatas]
diff = np.array(image_orientation) - nominal_sagittal_image_orientation
dist = np.linalg.norm(diff, axis=1)
if np.sum(dist < max_dist) > 1:
raise ValueError("There are too many sagittal images in the dataset.")
min_value = dist.min()
min_index = dist.argmin()
if min_value >= max_dist:
return None
image = self.dicom_stack[min_index]
# Remove from the stack since localize() assumes only axial slices are present
del self.dicom_stack[min_index]
del self.dicom_stack.metadatas[min_index]
return image
[docs]
def plotly_analyzed_images(
self,
show: bool = True,
show_colorbar: bool = True,
show_legend: bool = True,
**kwargs,
) -> dict[str, go.Figure]:
"""Plot the analyzed set of images to Plotly figures.
Parameters
----------
show : bool
Whether to show the plot.
show_colorbar : bool
Whether to show the colorbar on the plot.
show_legend : bool
Whether to show the legend on the plot.
kwargs
Additional keyword arguments to pass to the plot.
Returns
-------
dict
A dictionary of the Plotly figures where the key is the name of the
image and the value is the figure.
"""
figs = {}
# plot the images
modules: list = [
self.slice1,
self.geometric_distortion,
self.uniformity_module,
self.slice11,
]
modules.extend(list(self.low_contrast_multi_slice.slices.values()))
if self.has_sagittal_module:
modules.append(self.sagittal_localization)
for module in modules:
figs[module.common_name] = module.plotly(
show_colorbar=show_colorbar, show_legend=show_legend, **kwargs
)
# side view
figs["Side View"] = self.plotly_side_view(show_legend=show_legend)
# mtf
fig = go.Figure()
self.slice1.row_mtf.plotly(fig=fig, name="Row-wise rMTF")
figs["MTF"] = self.slice1.col_mtf.plotly(
show_legend=show_legend,
fig=fig,
name="Column-wise rMTF",
marker_color="orange",
)
if show:
for fig in figs.values():
fig.show()
return figs
[docs]
def plot_analyzed_image(self, show: bool = True, **plt_kwargs) -> plt.Figure:
"""Plot the analyzed image
Parameters
----------
show
Whether to show the image.
plt_kwargs
Keywords to pass to matplotlib for figure customization.
"""
modules: list = [
self.slice1,
self.geometric_distortion,
self.uniformity_module,
self.slice11,
]
modules.extend(list(self.low_contrast_multi_slice.slices.values()))
if self.has_sagittal_module:
modules.append(self.sagittal_localization)
# set up grid and axes
fig, axs = plt.subplots(3, 4, **plt_kwargs)
axes = axs.ravel()
ax_idx = -1
for module in modules:
ax_idx += 1
module.plot(axes[ax_idx])
ax_idx += 1
self.plot_side_view(axes[ax_idx])
ax_idx += 1
self.slice1.row_mtf.plot(axes[ax_idx], label="Row-wise rMTF")
self.slice1.col_mtf.plot(axes[ax_idx], label="Column-wise rMTF")
axes[ax_idx].legend()
for i in range(ax_idx + 1, len(axes)):
axes[i].set_visible(False)
# finish up
plt.tight_layout()
if show:
plt.show()
return fig
[docs]
def plot_images(self, show: bool = True, **plt_kwargs) -> dict[str, plt.Figure]:
"""Plot all the individual images separately
Parameters
----------
show
Whether to show the images.
plt_kwargs
Keywords to pass to matplotlib for figure customization.
"""
figs = {}
# plot the images
modules: dict = {
"geometric": self.geometric_distortion,
"slice 1": self.slice1,
"signal uniformity": self.uniformity_module,
"slice 11": self.slice11,
}
modules |= self.low_contrast_multi_slice.slices
if self.has_sagittal_module:
modules["sagittal"] = self.sagittal_localization
for key, module in modules.items():
fig, ax = plt.subplots(**plt_kwargs)
module.plot(ax)
figs[key] = fig
# plot rMTF
fig, ax = plt.subplots(**plt_kwargs)
self.slice1.row_mtf.plot(ax, label="Row-wise rMTF")
self.slice1.col_mtf.plot(ax, label="Column-wise rMTF")
ax.legend()
figs["rMTF"] = fig
# plot the side view
fig, ax = plt.subplots(**plt_kwargs)
figs["side"] = fig
self.plot_side_view(ax)
if show:
plt.show()
return figs
def _detected_modules(self) -> list[CatPhanModule]:
return [
self.slice1,
self.slice11,
self.uniformity_module,
self.geometric_distortion,
]
[docs]
def save_images(
self,
directory: Path | str | None = None,
to_stream: bool = False,
**plt_kwargs,
) -> list[Path | BytesIO]:
"""Save separate images to disk or stream.
Parameters
----------
directory
The directory to write the images to. If None, will use current working directory
to_stream
Whether to write to stream or disk. If True, will return streams. Directory is ignored in that scenario.
plt_kwargs
Keywords to pass to matplotlib for figure customization.
"""
figs = self.plot_images(show=False, **plt_kwargs)
paths = []
for name, fig in figs.items():
if to_stream:
path = io.BytesIO()
else:
destination = Path(directory) or Path.cwd()
path = (destination / name).with_suffix(".png").absolute()
fig.savefig(path)
paths.append(path)
return paths
def _quaac_datapoints(self) -> dict[str, QuaacDatum]:
results_data = self.results_data(as_dict=True)
data = {}
data["Phantom Roll"] = QuaacDatum(
value=results_data["phantom_roll_deg"],
unit="degrees",
description="The roll of the phantom in the image",
)
slice1_keys = (
("bar_difference_mm", "Bar Difference", "mm"),
("slice_shift_mm", "Slice Shift", "mm"),
("measured_slice_thickness_mm", "Measured Slice Thickness", "mm"),
("row_mtf_50", "Row-wise MTF 50%", "lp/mm"),
("col_mtf_50", "Column-wise MTF 50%", "lp/mm"),
)
for key, name, unit in slice1_keys:
data[name] = QuaacDatum(
value=results_data["slice1"][key],
unit=unit,
)
for name, roi in results_data["slice11"]["rois"].items():
data[f"Slice 11 {name} ROI"] = QuaacDatum(
value=roi["value"],
unit="HU",
)
data["Slice 11 Bar Difference"] = QuaacDatum(
value=results_data["slice11"]["bar_difference_mm"],
unit="mm",
)
data["Slice 11 Slice Shift"] = QuaacDatum(
value=results_data["slice11"]["slice_shift_mm"],
unit="mm",
)
for name, roi in results_data["uniformity_module"]["rois"].items():
data[f"Uniformity {name} ROI"] = QuaacDatum(
value=roi["value"],
unit="HU",
)
for name, roi in results_data["uniformity_module"]["ghost_rois"].items():
data[f"Uniformity {name} Ghost ROI"] = QuaacDatum(
value=roi["value"],
unit="HU",
)
data["Percent Signal Ghosting"] = QuaacDatum(
value=results_data["uniformity_module"]["psg"],
unit="%",
)
data["Ghosting Ratio"] = QuaacDatum(
value=results_data["uniformity_module"]["ghosting_ratio"],
unit="",
)
data["Percent Integral Uniformity"] = QuaacDatum(
value=results_data["uniformity_module"]["piu"],
unit="%",
)
for name, line in results_data["geometric_distortion_module"][
"profiles"
].items():
data[f"Geometric Distortion {name} line length"] = QuaacDatum(
value=line["width (mm)"],
unit="mm",
)
for name, line in results_data["sagittal_localizer_module"]["profiles"].items():
data[f"Localizer Distortion {name} line length"] = QuaacDatum(
value=line["width (mm)"],
unit="mm",
)
return data
[docs]
def publish_pdf(
self,
filename: str | Path,
notes: str | None = None,
open_file: bool = False,
metadata: dict | None = None,
logo: Path | str | None = None,
) -> None:
"""Publish (print) a PDF containing the analysis and quantitative results.
Parameters
----------
filename : (str, file-like object}
The file to write the results to.
notes : str, list of strings
Text; if str, prints single line.
If list of strings, each list item is printed on its own line.
open_file : bool
Whether to open the file using the default program after creation.
metadata : dict
Extra data to be passed and shown in the PDF. The key and value will be shown with a colon.
E.g. passing {'Author': 'James', 'Unit': 'TrueBeam'} would result in text in the PDF like:
--------------
Author: James
Unit: TrueBeam
--------------
logo: Path, str
A custom logo to use in the PDF report. If nothing is passed, the default pylinac logo is used.
"""
analysis_title = f"{self._model} Analysis"
analysis_images = self.save_images(to_stream=True)
canvas = pdf.PylinacCanvas(
filename, page_title=analysis_title, metadata=metadata
)
if notes is not None:
canvas.add_text(text="Notes:", location=(1, 4.5), font_size=14)
canvas.add_text(text=notes, location=(1, 4))
shortened_texts = [
textwrap.wrap(r, width=110) for r in self.results(as_str=False)
]
idx = 0
for items in enumerate(shortened_texts):
for text in items:
canvas.add_text(text=text, location=(1.5, 25 - idx * 0.5))
idx += 1
for page, img in enumerate(analysis_images):
canvas.add_new_page()
canvas.add_image(img, location=(1, 5), dimensions=(18, 18))
canvas.finish()
if open_file:
webbrowser.open(filename)
[docs]
def results(self, as_str: bool = True) -> str | tuple:
"""Return the results of the analysis as a string. Use with print()."""
string = (
f" - {self._model} Results - ",
f"Geometric Distortions: {self.geometric_distortion.distances()}",
f"Slice Thickness: {self.slice1.measured_slice_thickness_mm:2.2f}mm",
f"Slice 1 S/I Position shift: {self.slice1.slice_shift_mm:2.2f}mm",
f"Slice 11 S/I Position shift: {self.slice11.slice_shift_mm:2.2f}mm",
f"Uniformity PIU: {self.uniformity_module.percent_image_uniformity:2.2f}",
f"Percent-signal ghosting: {self.uniformity_module.psg:2.2f}%",
f"Uniformity Center ROI standard deviation: {self.uniformity_module.rois['Center'].std:2.2f}",
f"Row-wise MTF 50% (lp/mm): {self.slice1.row_mtf.relative_resolution(50):2.2f}",
f"Column-wise MTF 50% (lp/mm): {self.slice1.col_mtf.relative_resolution(50):2.2f}",
f"Sagittal Distortions: {self.sagittal_localization.distances()}",
f"Low Contrast Score: {self.low_contrast_multi_slice.score}",
)
if as_str:
return "\n".join(string)
else:
return string
def _generate_results_data(self) -> ACRMRIResult:
resolutions = range(10, 91, 10) # 10-90% in 10% increments
row_mtfs = {
resolution: self.slice1.row_mtf.relative_resolution(resolution)
for resolution in resolutions
}
col_mtfs = {
resolution: self.slice1.col_mtf.relative_resolution(resolution)
for resolution in resolutions
}
low_contrast_rois = {}
for k, v in self.low_contrast_multi_slice.slices.items():
low_contrast_rois[k] = MRLowContrastModuleOutput(
offset=MR_LOW_CONTRAST_MODULE_OFFSETS_MM[v.slice_num + 1],
slice_num=v.slice_num + 1,
spoke_settings=v.roi_settings,
background_settings=v.background_roi_settings,
spokes=v.as_dict(),
)
return ACRMRIResult(
phantom_model=self._model,
phantom_roll_deg=self.catphan_roll,
origin_slice=self.origin_slice,
num_images=self.num_images,
slice1=MRSlice1ModuleOutput(
offset=0,
roi_settings=self.slice1.roi_settings,
rois=rois_to_results(self.slice1.rois),
bar_difference_mm=self.slice1.bar_difference_mm,
slice_shift_mm=self.slice1.slice_shift_mm,
measured_slice_thickness_mm=self.slice1.measured_slice_thickness_mm,
row_mtf_50=self.slice1.row_mtf.relative_resolution(50),
col_mtf_50=self.slice1.col_mtf.relative_resolution(50),
row_mtf_lp_mm=row_mtfs,
col_mtf_lp_mm=col_mtfs,
),
slice11=MRSlice11ModuleOutput(
offset=MR_SLICE11_MODULE_OFFSET_MM,
bar_difference_mm=self.slice11.bar_difference_mm,
slice_shift_mm=self.slice11.slice_shift_mm,
rois=rois_to_results(self.slice11.rois),
roi_settings=self.slice11.roi_settings,
),
geometric_distortion_module=MRGeometricDistortionModuleOutput(
offset=MR_GEOMETRIC_DISTORTION_MODULE_OFFSET_MM,
profiles=self.geometric_distortion.profiles,
distances=self.geometric_distortion.distances(),
),
uniformity_module=MRUniformityModuleOutput(
offset=0,
roi_settings=self.uniformity_module.roi_settings,
rois=rois_to_results(self.uniformity_module.rois),
ghost_roi_settings=self.uniformity_module.ghost_roi_settings,
ghost_rois=rois_to_results(self.uniformity_module.ghost_rois),
psg=self.uniformity_module.psg,
ghosting_ratio=self.uniformity_module.ghosting_ratio,
piu=self.uniformity_module.percent_image_uniformity,
piu_passed=self.uniformity_module.piu_passed,
),
sagittal_localizer_module=MRSagittalLocalizationModuleOutput(
profiles=self.sagittal_localization.profiles,
distances=self.sagittal_localization.distances(),
),
low_contrast_multi_slice_module=MRLowContrastMultiSliceModuleOutput(
score=self.low_contrast_multi_slice.score,
low_contrast_rois=low_contrast_rois,
),
)