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Improve acr_slice_thickness #442

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Improve acr_slice_thickness #442

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94d9b49
Replace find_phantom_center method with get_mask_image
mollybuckley Aug 2, 2024
33c31d3
get centre coordinates of detected circle
mollybuckley Aug 2, 2024
a5b566c
testing a larger vertical range of different line profiles
mollybuckley Aug 2, 2024
dfb1abe
Calculation update
mollybuckley Sep 11, 2024
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33 changes: 27 additions & 6 deletions hazenlib/tasks/acr_slice_thickness.py
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Original file line number Diff line number Diff line change
Expand Up @@ -122,6 +122,18 @@ def find_ramps(self, img, centre):

y = np.round([np.max(y_locs) - 0.25 * height, np.min(y_locs) + 0.25 * height])

y_profiles = [skimage.measure.profile_line(img, (1, x_coord), (img.shape[0], x_coord), mode="constant")for x_coord in x]

# Calculate the angle of the ramps
angles = []
for profile in y_profiles:
profile_diff = np.diff(profile)
ramp_indices = np.where(profile_diff != 0)[0] # Simplistic approach to find ramps
if len(ramp_indices) >= 2:
y_diff = ramp_indices[-1] - ramp_indices[0]
x_diff = np.diff(x)[0] # The difference between x coordinates
angle = np.arctan2(y_diff, x_diff)
angles.append(np.degrees(angle))
return x, y

def FWHM(self, data):
Expand Down Expand Up @@ -188,15 +200,17 @@ def get_slice_thickness(self, dcm):
float: measured slice thickness.
"""
img = dcm.pixel_array
cxy, _ = self.ACR_obj.find_phantom_center(img, self.ACR_obj.dx, self.ACR_obj.dy)
mask = self.ACR_obj.get_mask_image(img)
cx, cy = mask.shape[1] // 2, mask.shape[0] // 2
cxy=(cx,cy)
x_pts, y_pts = self.find_ramps(img, cxy)

interp_factor = 1 / 5
interp_factor_dx = interp_factor * self.ACR_obj.dx
sample = np.arange(1, x_pts[1] - x_pts[0] + 2)
new_sample = np.arange(1, x_pts[1] - x_pts[0] + interp_factor, interp_factor)
offsets = np.arange(-3, 4)
ramp_length = np.zeros((2, 7))
offsets = np.arange(-10, 11)
ramp_length = np.zeros((2, 21))

line_store = []
fwhm_store = []
Expand Down Expand Up @@ -227,9 +241,16 @@ def get_slice_thickness(self, dcm):

line_store.append(interp_lines)
fwhm_store.append(fwhm)

with np.errstate(divide="ignore", invalid="ignore"):
dz = 0.2 * (np.prod(ramp_length, axis=0)) / np.sum(ramp_length, axis=0)
expected_ramp_length = 10 * 5 / self.ACR_obj.dx
tol_20percent = expected_ramp_length * 0.2 #trying to only include results where the line profile is actually over the ramp ie. filtering out completely unrealistic results - is this approach valid?
upper_tol = expected_ramp_length + tol_20percent
lower_tol = expected_ramp_length - tol_20percent
ramp_length_filtered_top = ramp_length[0][(ramp_length[0] >= lower_tol) & (ramp_length[0] <= upper_tol)]
ramp_length_filtered_bottom = ramp_length[1][(ramp_length[1] >= lower_tol) & (ramp_length[1] <= upper_tol)]
ramp_length_filtered_top_median = np.median(ramp_length_filtered_top)
ramp_length_filtered_bottom_median = np.median(ramp_length_filtered_bottom)
#calculation from ACR guidance:
dz = 0.2 * (ramp_length_filtered_top_median * ramp_length_filtered_bottom_median) / (ramp_length_filtered_top_median + ramp_length_filtered_bottom_median)

dz = dz[~np.isnan(dz)]
# TODO check this - if it's taking the value closest to the DICOM slice thickness this is potentially not accurate?
Expand Down
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