"""
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
yassine.azma@rmh.nhs.uk
22/02/2023
"""
import os
import sys
import traceback
import numpy as np
import cv2
import scipy
import skimage.morphology
import skimage.measure
from hazenlib.HazenTask import HazenTask
from hazenlib.ACRObject import ACRObject
from hazenlib.logger import logger
[docs]class ACRSpatialResolution(HazenTask):
"""Spatial resolution measurement class for DICOM images of the ACR phantom
Inherits from HazenTask class
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.ACR_obj = ACRObject(self.dcm_list)
[docs] def run(self) -> dict:
"""Main function for performing spatial resolution measurement
using slice 1 from the ACR phantom image set
Returns:
dict: results are returned in a standardised dictionary structure specifying the task name, input DICOM Series Description + SeriesNumber + InstanceNumber, task measurement key-value pairs, optionally path to the generated images for visualisation
"""
# Identify relevant slices
mtf_dcm = self.ACR_obj.slice_stack[0]
rot_ang = self.ACR_obj.determine_rotation(mtf_dcm.pixel_array)
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!"
)
# 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
[docs] def y_position_for_ramp(self, img, cxy):
"""Identify the y coordinate of the ramp
Args:
img (np.ndarray): dcm.pixelarray
cxy (tuple): x,y coordinates of the object centre
Returns:
float: y coordinate of the ramp min
"""
investigate_region = int(np.ceil(5.5 / self.ACR_obj.dy).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
[docs] def crop_image(self, img, x, y, width):
"""Return a rectangular subset of a pixel array
Args:
img (np.ndarray): dcm.pixelarray
x (int): x coordinate of centre
y (int): y coordinate of centre
width (int): size of the array top subset
Returns:
np.ndarray: subset of a pixel array with given 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
[docs] def get_edge_type(self, crop_img):
"""Determine direction of ramp edge
Args:
crop_img (np.ndarray): cropped pixel array ~ subset of the image
Returns:
tuple of string: vertical/horizontal and up/down or left/rigtward
"""
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
[docs] def edge_location_for_plot(self, crop_img, edge_type):
"""Determine the location of the edge so it can be visualised
Args:
crop_img (np.array): cropped pixel array ~ subset of the image
edge_type (tuple): vertical/horizontal and up/down or left/rigtward
Returns:
np.array: mask array for edge location
"""
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
[docs] def fit_normcdf_surface(self, crop_img, edge_type, direction):
"""Fit normalised CDF? to surface
Args:
crop_img (np.array): cropped pixel array ~ subset of the image
edge_type (string): vertical/horizontal
direction (string): up/down or left/rigtward
Returns:
tuple of floats: slope, surface
"""
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):
"""Maths function
Args:
x (_type_): _description_
slope (_type_): _description_
mu (_type_): _description_
bright (_type_): _description_
dark (_type_): _description_
Returns:
_type_: _description_
"""
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
[docs] def sample_erf(self, crop_img, slope, edge_type):
"""_summary_
Args:
crop_img (np.array): cropped pixel array ~ subset of the image
slope (float): value of slope of edge
edge_type (string): vertical/horizontal
Returns:
np.array: _description_
"""
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
[docs] def fit_erf(self, erf):
"""Fit ERF
Args:
erf (np.array): _description_
Returns:
_type_: _description_
"""
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):
"""Maths function for sigmoid curve equation
Args:
x (_type_): _description_
a (_type_): _description_
b (_type_): _description_
c (_type_): _description_
d (_type_): _description_
e (_type_): _description_
Returns:
_type_: _description_
"""
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
[docs] def calculate_MTF(self, erf):
"""Calculate MTF
Args:
erf (np.array): array of ?
Returns:
tuple: freq, lsf, MTF
"""
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((self.ACR_obj.dx, self.ACR_obj.dy))))
* (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
[docs] def identify_MTF50(self, freq, MTF):
"""Calculate effective resolution
Args:
freq (float or int): _description_
MTF (float or int): _description_
Returns:
float: _description_
"""
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
[docs] def get_mtf50(self, dcm):
"""_summary_
Args:
dcm (pydicom.Dataset): DICOM image object
Returns:
tuple: _description_
"""
img = dcm.pixel_array
cxy, _ = self.ACR_obj.find_phantom_center(img, self.ACR_obj.dx, self.ACR_obj.dy)
ramp_x = int(cxy[0])
ramp_y = self.y_position_for_ramp(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)
_, lsf_fit, MTF_fit = self.calculate_MTF(erf_fit)
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