def iterativeBackPropagation(hrImage, lrImages, lrMasks, transforms, H,
itermax):
y = []
for i in range(len(lrImages)):
y.append(
convert_image_to_vector(lrImages[i]) *
convert_image_to_vector(lrMasks[i]))
x = convert_image_to_vector(hrImage)
outputImage = nibabel.Nifti1Image(hrImage.get_data(), hrImage.affine)
hrMaskSum = np.zeros(hrImage.get_data().shape, dtype=np.float32)
for i in range(len(lrImages)):
tmp1 = apply_affine_itk_transform_on_image(input_image=lrMasks[i],
transform=transforms[i][0],
center=transforms[i][1],
reference_image=hrImage,
order=0)
hrMaskSum += tmp1.get_data()
index = np.nonzero(hrMaskSum)
for j in range(itermax):
#simulation and error computation
hrError = np.zeros(hrImage.get_data().shape, dtype=np.float32)
for i in range(len(lrImages)):
lrError = convert_vector_to_image(H[i].dot(x) - y[i], lrImages[i])
tmp2 = apply_affine_itk_transform_on_image(
input_image=lrError,
transform=transforms[i][0],
center=transforms[i][1],
reference_image=hrImage,
order=1)
hrError += tmp2.get_data()
hrError2 = np.zeros(hrImage.get_data().shape, dtype=np.float32)
hrError2[index] = hrError[index] / hrMaskSum[index]
#update hr image and x
outputImage = nibabel.Nifti1Image(outputImage.get_data() - hrError2,
hrImage.affine)
nibabel.save(outputImage, 'ibp_iter' + str(j) + '.nii.gz')
x = convert_image_to_vector(outputImage)
return outputImage
def ibpComputeError(x, H, y, hrMaskSum, lrImages, transforms, interpOrder):
hrError = np.zeros(hrMaskSum.get_data().shape, dtype=np.float32)
#Loop over LR image to compute the sum of errors
for i in range(len(lrImages)):
lrError = convert_vector_to_image(H[i].dot(x) - y[i], lrImages[i])
tmp2 = apply_affine_itk_transform_on_image(input_image=lrError,
transform=transforms[i][0],
center=transforms[i][1],
reference_image=hrMaskSum,
order=interpOrder)
hrError += tmp2.get_data()
#Normalize the error image
hrErrorNorm = np.zeros(hrMaskSum.get_data().shape, dtype=np.float32)
index = np.nonzero(hrMaskSum.get_data())
hrErrorNorm[index] = hrError[index] / hrMaskSum.get_data()[index]
return hrErrorNorm
def ibpComputeError(x, H, y, hrMaskSum, lrImages, transforms, interpOrder):
hrError = np.zeros(hrMaskSum.get_data().shape, dtype=np.float32)
# Loop over LR image to compute the sum of errors
for i in range(len(lrImages)):
lrError = convert_vector_to_image(H[i].dot(x) - y[i], lrImages[i])
tmp2 = apply_affine_itk_transform_on_image(
input_image=lrError,
transform=transforms[i][0],
center=transforms[i][1],
reference_image=hrMaskSum,
order=interpOrder,
)
hrError += tmp2.get_data()
# Normalize the error image
hrErrorNorm = np.zeros(hrMaskSum.get_data().shape, dtype=np.float32)
index = np.nonzero(hrMaskSum.get_data())
hrErrorNorm[index] = hrError[index] / hrMaskSum.get_data()[index]
return hrErrorNorm
for i in range(len(inputImages)):
HList.append(
compute_H(inputImages[i], initHRImage, inputTransforms[i],
psfList[i], maskImages[i]))
#Intensity correction To do
#N4 on initHR
#local correction
#New init HR
if args.bias == True:
initHRImage_N4 = apply_N4_on_image(initHRImage, shrink_factor=1)
xN4 = convert_image_to_vector(initHRImage_N4)
hrN4Data = np.zeros(initHRImage.get_data().shape)
for i in range(len(inputImages)):
simu = convert_vector_to_image(HList[i].dot(xN4), inputImages[i])
im = gaussian_biais_correction(inputImages[i], simu, 5)
warped = apply_affine_itk_transform_on_image(
input_image=im,
transform=inputTransforms[i][0],
center=inputTransforms[i][1],
reference_image=initHRImage,
order=3)
hrN4Data += (warped.get_data() / np.float32(len(inputImages)))
initHRImage = nibabel.Nifti1Image(hrN4Data, initHRImage.affine)
#Compute x
x = convert_image_to_vector(initHRImage)
maskX = convert_image_to_vector(maskHRImage)
#Let mask the HR image
#Compute H
HList = []
for i in range(len(inputImages)):
HList.append( compute_H(inputImages[i], initHRImage, inputTransforms[i], psfList[i], maskImages[i]) )
#Intensity correction To do
#N4 on initHR
#local correction
#New init HR
if args.bias == True:
initHRImage_N4 = apply_N4_on_image(initHRImage, shrink_factor=1)
xN4 = convert_image_to_vector(initHRImage_N4)
hrN4Data = np.zeros(initHRImage.get_data().shape)
for i in range(len(inputImages)):
simu = convert_vector_to_image(HList[i].dot(xN4),inputImages[i])
im = gaussian_biais_correction(inputImages[i],simu, 5)
warped = apply_affine_itk_transform_on_image(input_image=im,transform=inputTransforms[i][0], center=inputTransforms[i][1], reference_image=initHRImage, order=3)
hrN4Data += (warped.get_data() / np.float32(len(inputImages)) )
initHRImage = nibabel.Nifti1Image(hrN4Data, initHRImage.affine)
#Compute x
x = convert_image_to_vector(initHRImage)
maskX = convert_image_to_vector(maskHRImage)
#Let mask the HR image
x = x*maskX
#loop over LR images and stack y and masks
maskList = []
yList = []
m = np.identity(4)
c = np.array([0, 0, 0, 1])
inputTransform = (m, c)
print('Creating mask image using the following padding value:' +
str(args.padding))
data = np.zeros(HRimage.get_data().shape)
data[HRimage.get_data() > args.padding] = 1
maskHRImage = nibabel.Nifti1Image(data, HRimage.affine)
print('Percentage of HR masked values : %.2f ' %
(np.size(np.nonzero(
(data))) / (1.0 * np.size(data.shape)) * 100.0 / np.size(data)))
data = np.zeros(LRimage.get_data().shape)
data[LRimage.get_data() > args.padding] = 1
maskLRImage = nibabel.Nifti1Image(data, LRimage.affine)
print('Percentage of LR masked values : %.2f ' %
(np.size(np.nonzero(
(data))) / (1.0 * np.size(data.shape)) * 100.0 / np.size(data)))
HRSpacing = np.float32(np.array(HRimage.header['pixdim'][1:4]))
LRSpacing = np.float32(np.array(LRimage.header['pixdim'][1:4]))
psf = compute_psf(LRSpacing, HRSpacing, args.psf)
H = compute_H(LRimage, HRimage, inputTransform, psf, maskLRImage)
x = convert_image_to_vector(HRimage)
maskX = convert_image_to_vector(maskHRImage)
#Let mask the HR image
x = x * maskX
nibabel.save(convert_vector_to_image(H.dot(x), LRimage), args.output)
else:
#no transform provided : use identity as transform and zero as center
m = np.identity(4)
c = np.array([0, 0, 0, 1])
inputTransform = (m,c)
print('Creating mask image using the following padding value:'+str(args.padding))
data = np.zeros(HRimage.get_data().shape)
data[HRimage.get_data() > args.padding] = 1
maskHRImage = nibabel.Nifti1Image(data, HRimage.affine)
print('Percentage of HR masked values : %.2f '%( np.size(np.nonzero((data))) / (1.0*np.size(data.shape)) * 100.0 / np.size(data) ) )
data = np.zeros(LRimage.get_data().shape)
data[LRimage.get_data() > args.padding] = 1
maskLRImage = nibabel.Nifti1Image(data, LRimage.affine)
print('Percentage of LR masked values : %.2f '%( np.size(np.nonzero((data))) / (1.0*np.size(data.shape)) * 100.0 / np.size(data) ) )
HRSpacing = np.float32(np.array(HRimage.header['pixdim'][1:4]))
LRSpacing = np.float32(np.array(LRimage.header['pixdim'][1:4]))
psf = compute_psf(LRSpacing, HRSpacing, args.psf)
H = compute_H(LRimage, HRimage, inputTransform, psf, maskLRImage)
x = convert_image_to_vector(HRimage)
maskX = convert_image_to_vector(maskHRImage)
#Let mask the HR image
x = x*maskX
nibabel.save(convert_vector_to_image(H.dot(x),LRimage),args.output)
# Compute H
HList = []
for i in range(len(inputImages)):
HList.append(compute_H(inputImages[i], initHRImage, inputTransforms[i], psfList[i], maskImages[i]))
# Intensity correction To do
# N4 on initHR
# local correction
# New init HR
if args.bias == True:
initHRImage_N4 = apply_N4_on_image(initHRImage, shrink_factor=1)
xN4 = convert_image_to_vector(initHRImage_N4)
hrN4Data = np.zeros(initHRImage.get_data().shape)
for i in range(len(inputImages)):
simu = convert_vector_to_image(HList[i].dot(xN4), inputImages[i])
im = gaussian_biais_correction(inputImages[i], simu, 5)
warped = apply_affine_itk_transform_on_image(
input_image=im,
transform=inputTransforms[i][0],
center=inputTransforms[i][1],
reference_image=initHRImage,
order=3,
)
hrN4Data += warped.get_data() / np.float32(len(inputImages))
initHRImage = nibabel.Nifti1Image(hrN4Data, initHRImage.affine)
# Compute x
x = convert_image_to_vector(initHRImage)
maskX = convert_image_to_vector(maskHRImage)
