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acr_spatial_resolution.py
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acr_spatial_resolution.py
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"""
ACR Spatial Resolution (MTF)
https://www.acraccreditation.org/-/media/acraccreditation/documents/mri/largephantomguidance.pdf
Calculates the effective resolution (MTF50) for slice 1 for the ACR phantom. This is done in accordance with the
methodology described in Section 3 of the following paper:
https://opg.optica.org/oe/fulltext.cfm?uri=oe-22-5-6040&id=281325
WARNING: The phantom must be slanted for valid results to be produced. This test is not within the scope of ACR
guidance.
This script first identifies the rotation angle of the ACR phantom using slice 1. It provides a warning if the
slanted angle is less than 3 degrees.
The location of the ramps within the slice thickness are identified and a square ROI is selected around the anterior
edge of the slice thickness insert.
A rudimentary edge response function is generated based on the edge within the ROI to provide initialisation values for
the 2D normal cumulative distribution fit of the ROI.
The edge is then super-sampled in the direction of the bright-dark transition of the edge and binned at right angles
based on the edge slope determined from the 2D Normal CDF fit of the ROI to obtain the edge response function.
This super-sampled ERF is then fitted using a weighted sigmoid function. The raw data and this fit are then used to
determine the LSF and the subsequent MTF. The MTF50 for both raw and fitted data are reported.
The results are also visualised.
Created by Yassine Azma
22/02/2023
"""
import sys
import traceback
import scipy
import cv2
import os
import numpy as np
import skimage.morphology
import skimage.measure
from hazenlib.HazenTask import HazenTask
from hazenlib.ACRObject import ACRObject
from hazenlib.logger import logger
class ACRSpatialResolution(HazenTask):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.ACR_obj = ACRObject(self.dcm_list)
def run(self) -> dict:
rot_ang = self.ACR_obj.rot_angle
if np.abs(rot_ang) < 3:
logger.warning(f'The estimated rotation angle of the ACR phantom is {np.round(rot_ang, 3)} degrees, which '
f'is less than the recommended 3 degrees. Results will be unreliable!')
# Identify relevant slices
mtf_dcm = self.ACR_obj.dcms[0]
# Initialise results dictionary
results = self.init_result_dict()
results['file'] = self.img_desc(mtf_dcm)
try:
raw_res, fitted_res = self.get_mtf50(mtf_dcm)
results['measurement'] = {
"estimated rotation angle": round(rot_ang, 2),
"raw mtf50": round(raw_res, 2),
"fitted mtf50": round(fitted_res, 2)
}
except Exception as e:
print(f"Could not calculate the spatial resolution for {self.img_desc(mtf_dcm)} because of : {e}")
traceback.print_exc(file=sys.stdout)
# only return reports if requested
if self.report:
results['report_image'] = self.report_files
return results
def y_position_for_ramp(self, res, img, cxy):
investigate_region = int(np.ceil(5.5 / res[1]).item())
if np.mod(investigate_region, 2) == 0:
investigate_region = (investigate_region + 1)
line_profile_y = skimage.measure.profile_line(img, (cxy[1] - 2 * investigate_region, cxy[0]),
(cxy[1] + 2 * investigate_region, cxy[1]),
mode='constant').flatten()
abs_diff_y_profile = np.absolute(np.diff(line_profile_y))
y_peaks = scipy.signal.find_peaks(abs_diff_y_profile, height=1)
pk_heights = y_peaks[1]['peak_heights']
pk_ind = y_peaks[0]
highest_y_peaks = pk_ind[(-pk_heights).argsort()[:2]]
y_locs = highest_y_peaks - 1
height_pts = cxy[1] - 2 * investigate_region - 1 + y_locs
y = np.min(height_pts) + 2
return y
def crop_image(self, img, x, y, width):
crop_x, crop_y = (x - width // 2, x + width // 2), (y - width // 2, y + width // 2)
crop_img = img[crop_y[0]:crop_y[1], crop_x[0]:crop_x[1]]
return crop_img
def get_edge_type(self, crop_img):
edge_sum_rows = np.sum(crop_img, axis=1).astype(np.int_)
edge_sum_cols = np.sum(crop_img, axis=0).astype(np.int_)
_, pk_rows_height = self.ACR_obj.find_n_highest_peaks(np.abs(np.diff(edge_sum_rows)), 1)
_, pk_cols_height = self.ACR_obj.find_n_highest_peaks(np.abs(np.diff(edge_sum_cols)), 1)
edge_type = 'vertical' if pk_rows_height > pk_cols_height else 'horizontal'
thresh_roi_crop = crop_img > 0.6 * np.max(crop_img)
edge_dir = np.sum(thresh_roi_crop, axis=0) if edge_type == 'vertical' else np.sum(thresh_roi_crop, axis=1)
if edge_type == 'vertical':
direction = 'downward' if edge_dir[-1] > edge_dir[0] else 'upward'
else:
direction = 'leftward' if edge_dir[-1] > edge_dir[0] else 'rightward'
return edge_type, direction
def edge_location_for_plot(self, crop_img, edge_type):
thresh_roi_crop = crop_img > 0.6 * np.max(crop_img)
naive_lsf = np.abs(np.diff(np.sum(thresh_roi_crop, 1))) > 1 if edge_type == 'vertical' else np.abs(
np.diff(np.sum(thresh_roi_crop, 0)))
edge_test = np.diff(np.where(naive_lsf == 0))[0]
edge_begin = np.where(edge_test > 1)
edge_loc = np.array([edge_begin, edge_begin + edge_test[edge_begin] - 1]).flatten()
return edge_loc
def fit_normcdf_surface(self, crop_img, edge_type, direction):
thresh_roi_crop = crop_img > 0.6 * np.max(crop_img)
temp_x = np.linspace(1, thresh_roi_crop.shape[1], thresh_roi_crop.shape[1])
temp_y = np.linspace(1, thresh_roi_crop.shape[0], thresh_roi_crop.shape[0])
x, y = np.meshgrid(temp_x, temp_y)
bright = max(crop_img[thresh_roi_crop])
dark = 20 + np.min(crop_img[~thresh_roi_crop])
def func(x, slope, mu, bright, dark):
norm_cdf = (bright - dark) * scipy.stats.norm.cdf(x[0], mu + slope * x[1], 0.5) + dark
return norm_cdf
sign = 1 if direction in ('downward', 'leftward') else -1
x_data = np.vstack((sign * x.ravel(), y.ravel())) if edge_type == 'vertical' else np.vstack(
(sign * y.ravel(), x.ravel()))
popt, pcov = scipy.optimize.curve_fit(func, x_data, crop_img.ravel(), p0=[0, 0, bright, dark], maxfev=1000)
surface = func(x_data, popt[0], popt[1], popt[2], popt[3]).reshape(crop_img.shape)
slope = 1 / popt[0] if direction in ('leftward', 'upward') else -1 / popt[0]
return slope, surface
def sample_erf(self, crop_img, slope, edge_type):
resamp_factor = 8
if edge_type == 'horizontal':
resample_crop_img = cv2.resize(crop_img, (crop_img.shape[0] * resamp_factor, crop_img.shape[1]))
else:
resample_crop_img = cv2.resize(crop_img, (crop_img.shape[0], crop_img.shape[1] * resamp_factor))
mid_loc = [i / 2 for i in resample_crop_img.shape]
temp_x = np.linspace(1, resample_crop_img.shape[1], resample_crop_img.shape[1])
temp_y = np.linspace(1, resample_crop_img.shape[0], resample_crop_img.shape[0])
x_resample, y_resample = np.meshgrid(temp_x, temp_y)
erf = []
n_inside_roi = []
if edge_type == 'horizontal':
diffY = (y_resample - 1) - mid_loc[0]
x_prime = x_resample + resamp_factor * diffY * slope
x_min, x_max = np.min(x_prime).astype(int), np.max(x_prime).astype(int)
for k in range(x_min, x_max):
erf_val = np.mean(resample_crop_img[(x_prime >= k) & (x_prime < k + 1)])
erf.append(erf_val)
number_nonzero = np.count_nonzero(resample_crop_img[(x_prime >= k) & (x_prime < k + 1)])
n_inside_roi.append(number_nonzero)
else:
diffX = (x_resample.shape[0] - 1) - x_resample - mid_loc[1]
y_prime = np.flipud(y_resample) + resamp_factor * diffX * slope
y_min, y_max = np.min(y_prime).astype(int), np.max(y_prime).astype(int)
for k in range(y_min, y_max):
erf_val = np.mean(resample_crop_img[(y_prime >= k) & (y_prime < k + 1)])
erf.append(erf_val)
number_nonzero = np.count_nonzero(resample_crop_img[(y_prime >= k) & (y_prime < k + 1)])
n_inside_roi.append(number_nonzero)
erf = np.array(erf)
n_inside_roi = np.array(n_inside_roi)
erf = erf[n_inside_roi == np.max(n_inside_roi)]
return erf
def fit_erf(self, erf):
true_erf = np.diff(erf) > 0.2 * np.max(np.diff(erf))
turning_points = np.where(true_erf)[0][0], np.where(true_erf)[0][-1]
weights = 0.5 * np.ones((len(true_erf) + 1))
weights[turning_points[0]:turning_points[1]] = 1
def func(x, a, b, c, d, e):
sigmoid = a + b / (1 + np.exp(c * (x - d))) ** e
return sigmoid
popt, pcov = scipy.optimize.curve_fit(func, np.arange(1, len(erf) + 1), erf, sigma=(1 / weights),
p0=[np.min(erf), np.max(erf), 0, sum(turning_points) / 2, 1], maxfev=5000)
erf_fit = func(np.arange(1, len(erf) + 1), popt[0], popt[1], popt[2], popt[3], popt[4])
return erf_fit
def calculate_MTF(self, erf, res):
lsf = np.diff(erf)
N = len(lsf)
n = np.arange(-N / 2, N / 2) if N % 2 == 0 else np.arange(-(N - 1) / 2, (N + 1) / 2)
resamp_factor = 8
Fs = 1 / (np.sqrt(np.mean(np.square(res))) * (1 / resamp_factor))
freq = n * Fs / N
MTF = np.abs(np.fft.fftshift(np.fft.fft(lsf)))
MTF = MTF / np.max(MTF)
zero_freq = np.where(freq == 0)[0][0]
freq = freq[zero_freq:]
MTF = MTF[zero_freq:]
return freq, lsf, MTF
def identify_MTF50(self, freq, MTF):
freq_interp = np.arange(0, 1.005, 0.005)
MTF_interp = np.interp(freq_interp, freq, MTF, left=None, right=None, period=None)
equivalent_linepairs = freq_interp[np.argmin(np.abs(MTF_interp - 0.5))]
eff_res = 1 / (equivalent_linepairs * 2)
return eff_res
def get_mtf50(self, dcm):
img = dcm.pixel_array
res = dcm.PixelSpacing
cxy = self.ACR_obj.centre
ramp_x, ramp_y = int(cxy[0]), self.y_position_for_ramp(res, img, cxy)
width = int(13 * img.shape[0] / 256)
crop_img = self.crop_image(img, ramp_x, ramp_y, width)
edge_type, direction = self.get_edge_type(crop_img)
slope, surface = self.fit_normcdf_surface(crop_img, edge_type, direction)
erf = self.sample_erf(crop_img, slope, edge_type)
erf_fit = self.fit_erf(erf)
freq, lsf_raw, MTF_raw = self.calculate_MTF(erf, res)
_, lsf_fit, MTF_fit = self.calculate_MTF(erf_fit, res)
eff_raw_res = self.identify_MTF50(freq, MTF_raw)
eff_fit_res = self.identify_MTF50(freq, MTF_fit)
if self.report:
edge_loc = self.edge_location_for_plot(crop_img, edge_type)
import matplotlib.pyplot as plt
import matplotlib.patches as patches
fig, axes = plt.subplots(5, 1)
fig.set_size_inches(8, 40)
fig.tight_layout(pad=4)
axes[0].imshow(img, interpolation='none')
rect = patches.Rectangle((ramp_x - width // 2 - 1, ramp_y - width // 2 - 1), width, width, linewidth=1,
edgecolor='w', facecolor='none')
axes[0].add_patch(rect)
axes[0].axis('off')
axes[0].set_title('Segmented Edge')
axes[1].imshow(crop_img)
if edge_type == 'vertical':
axes[1].plot(np.arange(0, width - 1), np.mean(edge_loc) - slope * np.arange(0, width - 1), color='r')
else:
axes[1].plot(np.mean(edge_loc) + slope * np.arange(0, width - 1), np.arange(0, width - 1), color='r')
axes[1].axis('off')
axes[1].set_title('Cropped Edge', fontsize=14)
axes[2].plot(erf, 'rx', ms=5, label='Raw Data')
axes[2].plot(erf_fit, 'k', lw=3, label='Fitted Data')
axes[2].set_ylabel('Signal Intensity')
axes[2].set_xlabel('Pixel')
axes[2].grid()
axes[2].legend(fancybox='true')
axes[2].set_title('ERF', fontsize=14)
axes[3].plot(lsf_raw, 'rx', ms=5, label='Raw Data')
axes[3].plot(lsf_fit, 'k', lw=3, label='Fitted Data')
axes[3].set_ylabel(r'$\Delta$' + ' Signal Intensity')
axes[3].set_xlabel('Pixel')
axes[3].grid()
axes[3].legend(fancybox='true')
axes[3].set_title('LSF', fontsize=14)
axes[4].plot(freq, MTF_raw, 'rx', ms=8, label=f'Raw Data - {round(eff_raw_res, 2)}mm @ 50%')
axes[4].plot(freq, MTF_fit, 'k', lw=3, label=f'Weighted Sigmoid Fit of ERF - {round(eff_fit_res, 2)}mm @ 50%')
axes[4].set_xlabel('Spatial Frequency (lp/mm)')
axes[4].set_ylabel('Modulation Transfer Ratio')
axes[4].set_xlim([-0.05, 1])
axes[4].set_ylim([0, 1.05])
axes[4].grid()
axes[4].legend(fancybox='true')
axes[4].set_title('MTF', fontsize=14)
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 eff_raw_res, eff_fit_res