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acr_geometric_accuracy.py
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acr_geometric_accuracy.py
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"""
ACR Geometric Accuracy
https://www.acraccreditation.org/-/media/acraccreditation/documents/mri/largephantomguidance.pdf
Calculates geometric accuracy for slices 1 and 5 of the ACR phantom.
This script calculates the horizontal and vertical lengths of the ACR phantom in Slice 1 in accordance with the ACR Guidance.
This script calculates the horizontal, vertical and diagonal lengths of the ACR phantom in Slice 5 in accordance with the ACR Guidance.
The average distance measurement error, maximum distance measurement error and coefficient of variation of all distance
measurements is reported as recommended by IPEM Report 112, "Quality Control and Artefacts in Magnetic Resonance Imaging".
This is done by first producing a binary mask for each respective slice. Line profiles are drawn with aid of rotation
matrices around the centre of the test object to determine each respective length. The results are also visualised.
Created by Yassine Azma
18/11/2022
"""
import sys
import traceback
import os
import numpy as np
import skimage.morphology
import skimage.measure
import skimage.transform
from hazenlib.HazenTask import HazenTask
from hazenlib.ACRObject import ACRObject
class ACRGeometricAccuracy(HazenTask):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.ACR_obj = ACRObject(self.dcm_list)
def run(self) -> dict:
# Identify relevant slices
slice1_dcm = self.ACR_obj.dcms[0]
slice5_dcm = self.ACR_obj.dcms[4]
# Initialise results dictionary
results = self.init_result_dict()
results['file'] = [self.img_desc(slice1_dcm), self.img_desc(slice5_dcm)]
try:
lengths_1 = self.get_geometric_accuracy_slice1(slice1_dcm)
results['measurement'][self.img_desc(slice1_dcm)] = {
"Horizontal distance": round(lengths_1[0], 2),
"Vertical distance": round(lengths_1[1], 2)
}
except Exception as e:
print(f"Could not calculate the geometric accuracy for {self.img_desc(slice1_dcm)} because of : {e}")
traceback.print_exc(file=sys.stdout)
try:
lengths_5 = self.get_geometric_accuracy_slice5(slice5_dcm)
results['measurement'][self.img_desc(slice5_dcm)] = {
"Horizontal distance": round(lengths_5[0], 2),
"Vertical distance": round(lengths_5[1], 2),
"Diagonal distance SW": round(lengths_5[2], 2),
"Diagonal distance SE": round(lengths_5[3], 2)
}
except Exception as e:
print(f"Could not calculate the geometric accuracy for {self.img_desc(slice5_dcm)} because of : {e}")
traceback.print_exc(file=sys.stdout)
L = lengths_1 + lengths_5
mean_err, max_err, cov_l = self.distortion_metric(L)
results['measurement']['distortion'] = {
"Mean relative measurement error": round(mean_err, 2),
"Max absolute measurement error": round(max_err, 2),
"Coefficient of variation %": round(cov_l, 2)
}
# only return reports if requested
if self.report:
results['report_image'] = self.report_files
return results
def get_geometric_accuracy_slice1(self, dcm):
img = dcm.pixel_array
mask = self.ACR_obj.get_mask_image(self.ACR_obj.images[6])
cxy = self.ACR_obj.centre
length_dict = self.ACR_obj.measure_orthogonal_lengths(mask)
if self.report:
import matplotlib.pyplot as plt
fig, axes = plt.subplots(3, 1)
fig.set_size_inches(8, 24)
fig.tight_layout(pad=4)
axes[0].imshow(img)
axes[0].scatter(cxy[0], cxy[1], c='red')
axes[0].set_title('Centroid Location')
axes[1].imshow(mask)
axes[1].set_title('Thresholding Result')
axes[2].imshow(img)
axes[2].arrow(length_dict['Horizontal Extent'][0], cxy[1],
length_dict['Horizontal Extent'][-1] - length_dict['Horizontal Extent'][0], 1, color='blue',
length_includes_head=True, head_width=5)
axes[2].arrow(cxy[0], length_dict['Vertical Extent'][0], 1, length_dict['Vertical Extent'][-1] -
length_dict['Vertical Extent'][0], color='orange', length_includes_head=True, head_width=5)
axes[2].legend([str(np.round(length_dict['Horizontal Distance'], 2)) + 'mm',
str(np.round(length_dict['Vertical Distance'], 2)) + 'mm'])
axes[2].axis('off')
axes[2].set_title('Geometric Accuracy for Slice 1')
img_path = os.path.realpath(os.path.join(self.report_path, f'{self.img_desc(dcm)}.png'))
fig.savefig(img_path)
self.report_files.append(img_path)
return length_dict['Horizontal Distance'], length_dict['Vertical Distance']
def get_geometric_accuracy_slice5(self, dcm):
img = dcm.pixel_array
mask = self.ACR_obj.get_mask_image(self.ACR_obj.images[6])
cxy = self.ACR_obj.centre
length_dict = self.ACR_obj.measure_orthogonal_lengths(mask)
sw_dict, se_dict = self.diagonal_lengths(mask, cxy)
if self.report:
import matplotlib.pyplot as plt
fig, axes = plt.subplots(3, 1)
fig.set_size_inches(8, 24)
fig.tight_layout(pad=4)
axes[0].imshow(img)
axes[0].scatter(cxy[0], cxy[1], c='red')
axes[0].axis('off')
axes[0].set_title('Centroid Location')
axes[1].imshow(mask)
axes[1].axis('off')
axes[1].set_title('Thresholding Result')
axes[2].imshow(img)
axes[2].arrow(length_dict['Horizontal Extent'][0], cxy[1], length_dict['Horizontal Extent'][-1]
- length_dict['Horizontal Extent'][0], 1, color='blue', length_includes_head=True,
head_width=5)
axes[2].arrow(cxy[0], length_dict['Vertical Extent'][0], 1, length_dict['Vertical Extent'][-1] -
length_dict['Vertical Extent'][0], color='orange', length_includes_head=True, head_width=5)
axes[2].arrow(se_dict['Start'][0], se_dict['Start'][1], se_dict['Extent'][0], se_dict['Extent'][1],
color='purple', length_includes_head=True, head_width=5)
axes[2].arrow(sw_dict['Start'][0], sw_dict['Start'][1], sw_dict['Extent'][0], sw_dict['Extent'][1],
color='yellow', length_includes_head=True, head_width=5)
axes[2].legend([str(np.round(length_dict['Horizontal Distance'], 2)) + 'mm',
str(np.round(length_dict['Vertical Distance'], 2)) + 'mm',
str(np.round(sw_dict['Distance'], 2)) + 'mm',
str(np.round(se_dict['Distance'], 2)) + 'mm'])
axes[2].axis('off')
axes[2].set_title('Geometric Accuracy for Slice 5')
img_path = os.path.realpath(os.path.join(self.report_path, f'{self.img_desc(dcm)}.png'))
fig.savefig(img_path)
self.report_files.append(img_path)
return length_dict['Horizontal Distance'], length_dict['Vertical Distance'], \
sw_dict['Distance'], se_dict['Distance']
def diagonal_lengths(self, img, cxy):
res = self.ACR_obj.pixel_spacing
eff_res = np.sqrt(np.mean(np.square(res)))
img_rotate = skimage.transform.rotate(img, 45, center=(cxy[0], cxy[1]))
length_dict = self.ACR_obj.measure_orthogonal_lengths(img_rotate)
extent_h = length_dict['Horizontal Extent']
origin = (cxy[0], cxy[1])
start = (extent_h[0], cxy[1])
end = (extent_h[-1], cxy[1])
se_x_start, se_y_start = ACRObject.rotate_point(origin, start, 45)
se_x_end, se_y_end = ACRObject.rotate_point(origin, end, 45)
dist_se = np.sqrt(np.sum(np.square([se_x_end - se_x_start, se_y_end - se_y_start]))) * eff_res
se_dict = {
'Start': (se_x_start, se_y_start),
'End': (se_x_end, se_y_end),
'Extent': (se_x_end - se_x_start, se_y_end - se_y_start),
'Distance': dist_se
}
extent_v = length_dict['Vertical Extent']
start = (cxy[0], extent_v[0])
end = (cxy[0], extent_v[-1])
sw_x_start, sw_y_start = ACRObject.rotate_point(origin, start, 45)
sw_x_end, sw_y_end = ACRObject.rotate_point(origin, end, 45)
dist_sw = np.sqrt(np.sum(np.square([sw_x_end - sw_x_start, sw_y_end - sw_y_start]))) * eff_res
sw_dict = {
'Start': (sw_x_start, sw_y_start),
'End': (sw_x_end, sw_y_end),
'Extent': (sw_x_end - sw_x_start, sw_y_end - sw_y_start),
'Distance': dist_sw
}
return sw_dict, se_dict
@staticmethod
def distortion_metric(L):
err = [x - 190 for x in L]
mean_err = np.mean(err)
max_err = np.max(np.absolute(err))
cov_l = 100 * np.std(L) / np.mean(L)
return mean_err, max_err, cov_l