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scripts.py
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#%%
import numpy as np
import nibabel as nib
import os
import scipy.io
import ants
import tools
"""
Ants needs to be installed
https://github.com/ANTsX/ANTs/tree/master
"""
# %%
def transform_scalar_image(mat_path, warp_path, scalar_image_path, reference_image_path, output_path, interpolation="Linear"):
fwdTransform = f"antsApplyTransforms -d 3 -e 0 -i {scalar_image_path} -o {output_path} -t {warp_path} -t {mat_path} -r {reference_image_path} --verbose 1 -n {interpolation}"
os.system(fwdTransform)
print(fwdTransform)
def transform_tensor_image(mat_path, warp_path, tensor_image_path, reference_image_path, output_path):
transformedTensorsSyn = tools.applyTransformForTensor6List(tensor_image_path, reference_image_path, [ warp_path, mat_path], verbose = True)
nib.save(transformedTensorsSyn, output_path)
def reorient_tensor_image(tensor_image_path, warp_path, output_path):
reorientTensor = f"ReorientTensorImage 3 {tensor_image_path} {output_path} {warp_path}"
os.system(reorientTensor)
def save_RGB_from_tensor(tensor_image_path, output_path):
tensor_image = nib.load(tensor_image_path)
tensor_data = tensor_image.get_fdata()[:,:,:,0,:]
simpleRGB = tensor_data[:,:,:,:3]
simpleRGB[:,:,:,0] = tensor_data[:,:,:,0]
simpleRGB[:,:,:,1] = tensor_data[:,:,:,2]
simpleRGB[:,:,:,2] = tensor_data[:,:,:,5]
simpleRGB = simpleRGB / np.max(simpleRGB)
#expand dim
simpleRGB = simpleRGB[:,:,:, np.newaxis,:]
nibImage = nib.Nifti1Image(simpleRGB, tensor_image.affine)
nib.save(nibImage, output_path)
#register all images from atlas, T1, segm, tensor, and wm gm, probability...rgb
def register_atlas_to_patient(mat_path, warp_path, output_path, reference_image_path):
atlasT1Path = "./DTIAtlas/sub-mni152_t1-inside-brain_space-sri.nii.gz"
atlasTissueSegmPath = "./DTIAtlas/sub-mni152_tissue-with-antsN4_space-sri.nii.gz"
atlasWMPath = "./DTIAtlas/sub-mni152_tissue-with-antsN4_wm_space-sri.nii.gz"
atlasGMPath = "./DTIAtlas/sub-mni152_tissue-with-antsN4_gm_space-sri.nii.gz"
atlasCSFPath = "./DTIAtlas/sub-mni152_tissue-with-antsN4_csf_space-sri.nii.gz"
dtiAtlasPath = "./DTIAtlas/FSL_HCP1065_tensor_1mm_space-HPC-AntsIndexSpace_SRI.nii.gz"
os.makedirs(output_path, exist_ok=True)
# transform scalar images
transform_scalar_image(mat_path, warp_path, atlasT1Path, reference_image_path, output_path + "transformed_t1.nii.gz")
transform_scalar_image(mat_path, warp_path, atlasTissueSegmPath, reference_image_path, output_path + "transformed_tissue.nii.gz", interpolation="NearestNeighbor")
transform_scalar_image(mat_path, warp_path, atlasWMPath, reference_image_path, output_path + "transformed_wm.nii.gz")
transform_scalar_image(mat_path, warp_path, atlasGMPath, reference_image_path, output_path + "transformed_gm.nii.gz")
transform_scalar_image(mat_path, warp_path, atlasCSFPath, reference_image_path, output_path + "transformed_csf.nii.gz")
# transform tensor image
transform_tensor_image(mat_path, warp_path, dtiAtlasPath, reference_image_path, output_path + "transformed_tensor.nii.gz")
# save RGB from tensor
save_RGB_from_tensor(output_path + "transformed_tensor.nii.gz", output_path + "transformed_tensor_rgb.nii.gz")
# reorient tensor image
reorient_tensor_image(output_path + "transformed_tensor.nii.gz", warp_path, output_path + "transformed_reoriented_tensor.nii.gz")
save_RGB_from_tensor(output_path + "transformed_reoriented_tensor.nii.gz", output_path + "transformed_reoriented_tensor_rgb.nii.gz")
# %% ran for all patients of brats that are "good"
for patientNumber in range(1, 30000):
try:
pateintString = str(patientNumber).zfill(5)
patientPath = "/mnt/8tb_slot8/jonas/workingDirDatasets/brats/brats_output_andre/base_line_SyN_s2/BraTS2021_"+pateintString+"/"
matfilePath = patientPath + "affine-simple-SyN_s2-reg.mat"
wrapFilePath = patientPath + "deformation-field-SyN_s2-reg.nii.gz"
nib.load(wrapFilePath)
orginalT1Path = "/mnt/8tb_slot8/jonas/workingDirDatasets/brats/brats_good_t1_and_t1c_smoothed_and_masked/BraTS2021_"+pateintString+"/preop/sub-BraTS2021_"+pateintString+"_ses-preop_space-sri_t1.nii.gz"
transform_t1Path = patientPath + "deformation-field-SyN_s2-reg.nii.gz"
outputpath = "/mnt/8tb_slot8/jonas/workingDirDatasets/brats/brats_good_registerd_atlas/BraTS2021_"+pateintString+"/"
os.makedirs(outputpath, exist_ok=True)
register_atlas_to_patient(matfilePath, wrapFilePath, outputpath, orginalT1Path)
except Exception as e:
print(e)
continue
# %% =====================================================================================================================0
# =====================================================================================================================0
# =====================================================================================================================0
#%%
import nibabel as nib
import ants
import numpy as np
import dipy.reconst.dti as dti
from dipy.reconst.dti import fractional_anisotropy
def applyTransformForTensor6List(tensor6ListPath, fixedImagePath, transformList, verbose = True):
nibArr = nib.load(tensor6ListPath).get_fdata()
antsTensorImageHPC = ants.image_read(tensor6ListPath)
fixedImage = ants.image_read(fixedImagePath)
affineFixedImage = nib.load(fixedImagePath).affine
resultArray = np.zeros(fixedImage.shape[0:3] + (6,))
for i in range(6):
antsImg= ants.from_numpy(nibArr[:,:,:,0,i])
antsImg.set_origin(antsTensorImageHPC.origin)
antsImg.set_spacing(antsTensorImageHPC.spacing)
antsImg.set_direction(antsTensorImageHPC.direction)
affine_transformed_tensors = ants.apply_transforms(fixed=fixedImage, moving=antsImg, transformlist= transformList, verbose = verbose)
resultArray[:,:,:,i] = affine_transformed_tensors.numpy()
# Save the transformed image
resultArray5D = resultArray[..., np.newaxis, :]
nifti_img_tensors_SRI = nib.Nifti1Image(resultArray5D, affineFixedImage)
nifti_img_tensors_SRI.header['intent_code'] = 1005
nifti_img_tensors_SRI.header['intent_name'] = b"SymmetricMatrix"
return nifti_img_tensors_SRI
def get_RGB_from_Tensor(tensor):
#TODO
pass
def get_tensor_from_lower6(lower6):
#[dxx, dxy, dyy, dxz, dyz, dzz]
tensor = np.zeros(lower6.shape[0:3] + (3,3))#.astype(np.string_) for testing
print(tensor.shape)
tensor[..., 0, 0] = lower6[..., 0]
tensor[..., 1, 1] = lower6[..., 2]
tensor[..., 2, 2] = lower6[..., 5]
tensor[..., 0, 1] = lower6[..., 1]
tensor[..., 1, 0] = lower6[..., 1]
tensor[..., 0, 2] = lower6[..., 3]
tensor[..., 2, 0] = lower6[..., 3]
tensor[..., 1, 2] = lower6[..., 4]
tensor[..., 2, 1] = lower6[..., 4]
return tensor
#%%
if __name__ == "__main__":
test = np.array([[[["dxx", "dxy", "dyy", "dxz", "dyz", "dzz"]]]])
test[..., 0]
print(test.shape)
print(get_tensor_from_lower6(test))
#%% test rgb generation
if __name__ == "__main__":
patientNumber= 42
patStr = str(patientNumber).zfill(3)
fapath = "/mnt/8tb_slot8/jonas/workingDirDatasets/tgm/rgbResultsWithMD_FA/tgm"+patStr+"/sub-tgm"+patStr+"_ses-preop_space-sri_dti_fa.nii.gz"
tensorsLower6 = "/mnt/8tb_slot8/jonas/workingDirDatasets/tgm/registerDTIAtlasToPatient/tgm042/tensors.nii.gz"
fapath = nib.load(fapath).get_fdata()
lowerTensor = nib.load(tensorsLower6).get_fdata()
fullTensor = get_tensor_from_lower6(lowerTensor[:,:,:,0,:])
fa = fractional_anisotropy(eigenvalues)
# Compute color FA map
color_FA = dti.color_fa(fa, tenfit.evecs)
pass
# %%