def test_pad_constant(self):
reference_shape = (4, 6)
in_shape = (3, 5)
values = np.ones(in_shape[::-1]) # since dimesions are switched in itk
image = sitk.GetImageFromArray(values)
corrector = m.SizeCorrectionFilter(pad_constant=1)
corr_image = corrector.execute(image, params=m.SizeCorrectionParams(reference_shape))
self.assertEqual(corr_image.GetSize(), reference_shape)
np_corr_image = sitk.GetArrayFromImage(corr_image)
self.assertTrue((np_corr_image == 1).all()) # dimensions are switched from itk to numpy
def test_2d(self):
reference_shape = (4, 6)
in_shape = (5, 5)
values = np.random.randint(0, 10, in_shape[::-1]) # since dimesions are switched in itk
image = sitk.GetImageFromArray(values)
corrector = m.SizeCorrectionFilter()
corr_image = corrector.execute(image, params=m.SizeCorrectionParams(reference_shape))
self.assertEqual(corr_image.GetSize(), reference_shape)
np_corr_image = sitk.GetArrayFromImage(corr_image)
self.assertTrue((np_corr_image[:-1] == values[:, :-1]).all()) # dimensions are switched from itk to numpy
def test_one_sided(self):
reference_shape = (4, 4, 3)
in_shape = (2, 4, 5)
values = np.random.randint(0, 10, in_shape[::-1]) # since dimesions are switched in itk
image = sitk.GetImageFromArray(values)
corrector = m.SizeCorrectionFilter(two_sided=False)
corr_image = corrector.execute(image, params=m.SizeCorrectionParams(reference_shape))
self.assertEqual(corr_image.GetSize(), reference_shape)
np_corr_image = sitk.GetArrayFromImage(corr_image)
self.assertTrue((np_corr_image[:, :, 2:] == values[2:, :, :]).all())
def main(imginppath: str, samplerate: float, outputsubdir: str):
samplerate_str = str("{:.2f}".format(samplerate))
print(samplerate_str)
print('------')
outfilename = os.path.split(imginppath)[1].split(
'.nii.gz')[0] + '-subsampled_' + samplerate_str + '.nii.gz'
outfilename = os.path.join(
os.path.split(imginppath)[0], outputsubdir, outfilename)
# load image
img = sitk.ReadImage(imginppath)
print(img.GetSize())
# pad image
padfilter = flt.SizeCorrectionFilter(two_sided=False, pad_constant=0.0)
padparams = flt.SizeCorrectionParams(img.GetSize())
sitkpadflt = sitk.FFTPadImageFilter()
sitkpadflt.SetBoundaryCondition(1)
img_padded = sitkpadflt.Execute(img)
brainmask = sitk.ReadImage(
os.path.join(os.path.split(imginppath)[0], 'brainmask.nii.gz'))
# transform to k-space
print('Unpadded: ' + str(img.GetSize()))
print('Padded: ' + str(img_padded.GetSize()))
img_k = sitk.ForwardFFT(img_padded)
# get size of transformed image
img_k_size = img_k.GetSize()
# random sample
numel = img_k_size[0] * img_k_size[1] * img_k_size[2]
samplearr = np.zeros(numel, dtype=int)
samplearr[:int(numel * samplerate)] = 1
np.random.shuffle(samplearr)
maskarr = np.reshape(samplearr, img_k_size)
maskimg = sitk.GetImageFromArray(np.swapaxes(maskarr, 0, 2))
maskimg.CopyInformation(img_k)
masked_kspace = sitk.Mask(img_k, maskimg)
img_subsampled = sitk.InverseFFT(masked_kspace)
# unpad
unpadparams = flt.SizeCorrectionParams(img.GetSize())
img_subsampled = padfilter.execute(img_subsampled, unpadparams)
img_subsampled.CopyInformation(img)
# mask with brainmask
img_subsampled = sitk.Mask(sitk.Cast(img_subsampled, sitk.sitkInt64),
sitk.Cast(brainmask, sitk.sitkInt64))
img_subsampled = sitk.Cast(img_subsampled, img.GetPixelID())
sitk.WriteImage(img_subsampled, outfilename)
