def run_betDWI(path_input, path_output):
print(' - running BET with FSL...')
finalFileName = os.path.join(
path_output,
utils.to_extract_filename(path_input) + d.id_bet + '_dwi_masked' +
d.extension)
binaryMaskFileName = os.path.join(
path_output,
utils.to_extract_filename(path_input) + d.id_bet + '_b0_masked_mask' +
d.extension)
b0MaskedFileName = os.path.join(
path_output,
utils.to_extract_filename(path_input) + d.id_bet + '_b0_masked' +
d.extension)
if not (os.path.exists(finalFileName)):
fsl.bet(path_input, b0MaskedFileName, '-m -f .4')
imgMask = nib.load(binaryMaskFileName)
dataMask = imgMask.get_data()
img = nib.load(path_input)
data = img.get_data()
data[dataMask == 0] = 0
nib.save(nib.Nifti1Image(data.astype(np.float32), img.affine),
finalFileName)
# return finalFileName, binaryMaskFileName, b0MaskedFileName
print("Ejecutando")
return finalFileName
def run_to_estimate_dti(path_input,
path_output,
fbval="",
fbvec="",
file_inMask=""):
if fbval == "" or fbvec == "":
folder_sujeto = path_output
for l in os.listdir(folder_sujeto):
if "TENSOR" in l and "bval" in l:
fbval = os.path.join(folder_sujeto, l)
if "TENSOR" in l and "bvec" in l:
fbvec = os.path.join(folder_sujeto, l)
if file_inMask == "":
folder = os.path.dirname(path_input)
for i in os.listdir(folder):
if "masked_mask" in i:
file_inMask = os.path.join(folder, i)
#def to_estimate_dti(file_in, file_inMask, outPath, fbval, fbvec):
print(d.separador + 'building DTI Model...')
ref_name = utils.to_extract_filename(path_input)
file_evecs = os.path.join(path_output, ref_name + d.id_evecs + d.extension)
file_evals = os.path.join(path_output, ref_name + d.id_evals + d.extension)
if (not (os.path.exists(file_evecs))) | (not (os.path.exists(file_evals))):
try:
os.remove(file_evecs)
os.remove(file_evals)
except:
print("Unexpected error:", sys.exc_info()[0])
img = nib.load(path_input)
data = img.get_data()
mask = nib.load(file_inMask)
mask = mask.get_data()
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs)
tensor_model = dti.TensorModel(gtab)
tensor_fitted = tensor_model.fit(data, mask)
nib.save(
nib.Nifti1Image(tensor_fitted.evecs.astype(np.float32),
img.affine), file_evecs)
nib.save(
nib.Nifti1Image(tensor_fitted.evals.astype(np.float32),
img.affine), file_evals)
print(file_evecs)
print(file_evals)
print(path_input)
return path_input
def run_eddy_correction(path_input,path_output,ref_bo):
print(' - running Eddy Correction...')
refNameOnly = utils.to_extract_filename(path_input)
final_name=os.path.join(path_output,refNameOnly + d.id_eddy_correct + d.extension)
if not os.path.exists(final_name):
refName = utils.to_extract_filename_extention(path_input)
path_temporal=os.path.join(path_output,"temp_eddy")
os.mkdir(path_temporal)
os.system('cp ' + path_input + ' ' + path_temporal) # Copiamos archivo de difusion a la carpeta temporal
fsl.eddy_correct(path_temporal +"/"+ refName, path_temporal +"/"+ refNameOnly + d.id_eddy_correct + '.nii', ref_bo)
os.system('cp ' + os.path.join(path_temporal, refNameOnly + d.id_eddy_correct + d.extension) + ' ' + path_output) # Copiamos archivo de difusion desde carpeta temporal
# shutil.rmtree(path_temporal)
return final_name
def run_nonLocalMean(path_input,path_output):
print(' - running NonLocal Mean algoritm...')
finalFileName = os.path.join(path_output, utils.to_extract_filename(path_input) + d.id_non_local_mean + d.extension)
if not (os.path.exists(finalFileName)):
img = nib.load(path_input)
data = img.get_data()
newData = np.zeros(data.shape)
gradientDirections = data.shape[-1]
for index in range(gradientDirections):
print(index)
sigma = estimate_sigma(data[:, :, :, index], N=8)
newData[:, :, :, index] = nlmeans(data[:, :, :, index], sigma=sigma)
nib.save(nib.Nifti1Image(newData.astype(np.float32), img.affine), finalFileName)
return finalFileName
def run_reslicing(path_input, path_output, vox_sz=d.vox_sz):
print(' - runnning Reslice...')
finalFileName = os.path.join(
path_output,
utils.to_extract_filename(path_input) + d.id_reslice + d.extension)
if not (os.path.exists(finalFileName)):
img = nib.load(path_input)
data = img.get_data()
affine = img.affine
old_vox_sz = img.header.get_zooms()[:3]
new_vox_sz = (vox_sz, vox_sz, vox_sz)
# Si el tamano del voxel es isotropico, no es necesario hacer el reslice
data, affine = reslice(data, affine, old_vox_sz, new_vox_sz)
nib.save(nib.Nifti1Image(data, affine), finalFileName)
return finalFileName
import numpy as np
from dipy.io import read_bvals_bvecs
from dipy.core.gradients import gradient_table
###################
folder_sujeto = path_output
for l in os.listdir(folder_sujeto):
if "bval" in l:
fbval = os.path.join(folder_sujeto, l)
if "bvec" in l:
fbvec = os.path.join(folder_sujeto, l)
#################
# def to_register_dwi_to_mni(path_in, path_out, path_bvec, path_bval):
ref_name = utils.to_extract_filename(path_input)
finalname = os.path.join(path_output, ref_name + '_normalized' + d.extension)
if not os.path.exists(finalname):
img_DWI = nib.load(path_input)
data_DWI = img_DWI.get_data()
affine_DWI = img_DWI.affine
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs)
b0 = data_DWI[..., gtab.b0s_mask]
mean_b0 = np.mean(b0, -1)
import shutil
path_input = sys.argv[1]
path_output = sys.argv[2]
"""
Prueba documental.
In:
file_in: akakakakka
outPath: kakakasjdjdlllf kklkd
ref_bo. kskskdejien skkd dllkd
Out:
"""
ref_bo = '0'
print(' - running Eddy Correction...')
refNameOnly = utils.to_extract_filename(path_input)
final_name = os.path.join(path_output,
refNameOnly + d.id_eddy_correct + d.extension)
if not os.path.exists(final_name):
refName = utils.to_extract_filename_extention(path_input)
path_temporal = os.path.join(path_output, "temp_eddy")
os.mkdir(path_temporal)
os.system(
'cp ' + path_input + ' ' +
path_temporal) # Copiamos archivo de difusion a la carpeta temporal
fsl.eddy_correct(
path_temporal + "/" + refName,
path_temporal + "/" + refNameOnly + d.id_eddy_correct + '.nii', ref_bo)
os.system(
'cp ' + os.path.join(path_temporal, refNameOnly + d.id_eddy_correct +
def run_to_estimate_dti_maps(path_input,
path_output,
file_tensor_fitevals="",
file_tensor_fitevecs="",
fbval="",
fbvec=""):
folder = os.path.dirname(path_input)
if fbval == "" or fbvec == "":
folder_sujeto = path_output
for l in os.listdir(folder_sujeto):
if "TENSOR" in l and "bval" in l:
fbval = os.path.join(folder_sujeto, l)
if "TENSOR" in l and "bvec" in l:
fbvec = os.path.join(folder_sujeto, l)
if file_tensor_fitevals == "" or file_tensor_fitevecs == "":
for i in os.listdir(folder):
if "DTIEvals" in i:
file_tensor_fitevals = os.path.join(folder, i)
for i in os.listdir(folder):
if "DTIEvecs" in i:
file_tensor_fitevecs = os.path.join(folder, i)
if not os.path.exists(os.path.join(folder, "list_maps.txt")):
# def to_estimate_dti_maps(path_dwi_input, path_output, file_tensor_fitevecs, file_tensor_fitevals):
ref_name_only = utils.to_extract_filename(file_tensor_fitevecs)
ref_name_only = ref_name_only[:-9]
list_maps = []
img_tensorFitevecs = nib.load(file_tensor_fitevecs)
img_tensorFitevals = nib.load(file_tensor_fitevals)
evecs = img_tensorFitevecs.get_data()
evals = img_tensorFitevals.get_data()
affine = img_tensorFitevecs.affine
print(d.separador + d.separador + 'computing of FA map')
FA = fractional_anisotropy(evals)
FA[np.isnan(FA)] = 0
nib.save(
nib.Nifti1Image(FA.astype(np.float32), affine),
os.path.join(path_output, ref_name_only + '_FA' + d.extension))
list_maps.append(
os.path.join(path_output, ref_name_only + '_FA' + d.extension))
print(d.separador + d.separador + 'computing of Color FA map')
FA2 = np.clip(FA, 0, 1)
RGB = color_fa(FA2, evecs)
nib.save(
nib.Nifti1Image(np.array(255 * RGB, 'uint8'), affine),
os.path.join(path_output, ref_name_only + '_FA_RGB' + d.extension))
print(d.separador + d.separador + 'computing of MD map')
MD = dti.mean_diffusivity(evals)
nib.save(
nib.Nifti1Image(MD.astype(np.float32), affine),
os.path.join(path_output, ref_name_only + '_MD' + d.extension))
list_maps.append(
os.path.join(path_output, ref_name_only + '_MD' + d.extension))
print(d.separador + d.separador + 'computing of AD map')
AD = dti.axial_diffusivity(evals)
nib.save(
nib.Nifti1Image(AD.astype(np.float32), affine),
os.path.join(path_output, ref_name_only + '_AD' + d.extension))
list_maps.append(
os.path.join(path_output, ref_name_only + '_AD' + d.extension))
print(d.separador + d.separador + 'computing of RD map')
RD = dti.radial_diffusivity(evals)
nib.save(
nib.Nifti1Image(RD.astype(np.float32), affine),
os.path.join(path_output, ref_name_only + '_RD' + d.extension))
list_maps.append(
os.path.join(path_output, ref_name_only + '_RD' + d.extension))
sphere = get_sphere('symmetric724')
peak_indices = quantize_evecs(evecs, sphere.vertices)
eu = EuDX(FA.astype('f8'),
peak_indices,
seeds=300000,
odf_vertices=sphere.vertices,
a_low=0.15)
tensor_streamlines = [streamline for streamline in eu]
hdr = nib.trackvis.empty_header()
hdr['voxel_size'] = nib.load(path_input).header.get_zooms()[:3]
hdr['voxel_order'] = 'LAS'
hdr['dim'] = FA.shape
tensor_streamlines_trk = ((sl, None, None)
for sl in tensor_streamlines)
nib.trackvis.write(os.path.join(
path_output, ref_name_only + '_tractography_EuDx.trk'),
tensor_streamlines_trk,
hdr,
points_space='voxel')
print(list_maps)
with open(os.path.join(path_output, "list_maps.txt"), "w") as f:
for s in list_maps:
f.write(str(s) + "\n")
return path_input
sys.path+=[os.path.dirname(os.path.dirname(os.path.abspath(__file__)))+"/"]
path_input = sys.argv[1]
path_output = sys.argv[2]
from tareas.dependencias import utils
from tareas.dependencias import definitions as d
from dipy.segment.mask import median_otsu
import nibabel as nib
import numpy as np
median_radius = 4
num_pass = 4
# print(' - running Median Otsu algoritm...')
finalFileName = path_output + utils.to_extract_filename(path_input) + d.id_median_otsu + '_maskedVolume' + d.extension
binaryMaskFileName = path_output + utils.to_extract_filename(path_input) + d.id_median_otsu + '_binaryMask' + d.extension
b0MaskedFileName = path_output + utils.to_extract_filename(path_input) + d.id_median_otsu + '_b0Masked' + d.extension
if not (os.path.exists(finalFileName)):
img = nib.load(path_input)
data = img.get_data()
maskedvolume, mask = median_otsu(data, median_radius, num_pass)
nib.save(nib.Nifti1Image(maskedvolume.astype(np.float16), img.affine), finalFileName)
nib.save(nib.Nifti1Image(mask.astype(np.float16), img.affine), binaryMaskFileName)
nib.save(nib.Nifti1Image(maskedvolume[:, :, :, d.default_b0_ref].astype(np.float16), img.affine),
b0MaskedFileName)
# return finalFileName, binaryMaskFileName
print(finalFileName)
path_input = sys.argv[1]
path_output = sys.argv[2]
from tareas.dependencias import utils
from tareas.dependencias import definitions as d
import nibabel as nib
import numpy as np
from dipy.denoise.noise_estimate import estimate_sigma
from dipy.denoise.nlmeans import nlmeans
print(' - running NonLocal Mean algoritm...')
finalFileName = os.path.join(
path_output,
utils.to_extract_filename(path_input) + d.id_non_local_mean + d.extension)
if not (os.path.exists(finalFileName)):
img = nib.load(path_input)
data = img.get_data()
newData = np.zeros(data.shape)
gradientDirections = data.shape[-1]
for index in range(gradientDirections):
print(index)
sigma = estimate_sigma(data[:, :, :, index], N=8)
newData[:, :, :, index] = nlmeans(data[:, :, :, index], sigma=sigma)
nib.save(nib.Nifti1Image(newData.astype(np.float32), img.affine),
finalFileName)
path_input = sys.argv[1]
path_output = sys.argv[2]
from tareas.dependencias import utils
from tareas.dependencias import definitions as d
from tareas.dependencias import fsl_wrapper as fsl
import nibabel as nib
import numpy as np
print(' - running BET with FSL...')
finalFileName = os.path.join(path_output,utils.to_extract_filename(path_input) + d.id_bet + '_dwi_masked' + d.extension)
binaryMaskFileName = os.path.join(path_output,utils.to_extract_filename(path_input) + d.id_bet + '_b0_masked_mask' + d.extension)
b0MaskedFileName = os.path.join(path_output,utils.to_extract_filename(path_input) + d.id_bet + '_b0_masked' + d.extension)
if not (os.path.exists(finalFileName)):
fsl.bet(path_input, b0MaskedFileName, '-m -f .4')
imgMask = nib.load(binaryMaskFileName)
dataMask = imgMask.get_data()
img = nib.load(path_input)
data = img.get_data()
data[dataMask == 0] = 0
nib.save(nib.Nifti1Image(data.astype(np.float32), img.affine), finalFileName)
if "DTIEvecs" in i:
file_tensor_fitevecs = os.path.join(folder, i)
folder_sujeto = path_output
for l in os.listdir(folder_sujeto):
if "bval" in l:
fbval = os.path.join(folder_sujeto, l)
if "bvec" in l:
fbvec = os.path.join(folder_sujeto, l)
#################
if not os.path.exists(os.path.join(folder, "list_maps.txt")):
# def to_estimate_dti_maps(path_dwi_input, path_output, file_tensor_fitevecs, file_tensor_fitevals):
ref_name_only = utils.to_extract_filename(file_tensor_fitevecs)
ref_name_only = ref_name_only[:-9]
list_maps = []
img_tensorFitevecs = nib.load(file_tensor_fitevecs)
img_tensorFitevals = nib.load(file_tensor_fitevals)
evecs = img_tensorFitevecs.get_data()
evals = img_tensorFitevals.get_data()
affine = img_tensorFitevecs.affine
print(d.separador + d.separador + 'computing of FA map')
FA = fractional_anisotropy(evals)
FA[np.isnan(FA)] = 0
def run_to_register_dwi_to_mni(path_input,path_output,fbval="",fbvec=""):
if fbval == "" or fbvec == "":
folder_sujeto=path_output
for l in os.listdir(folder_sujeto):
if "TENSOR" in l and "bval" in l:
fbval=os.path.join(folder_sujeto,l)
if "TENSOR" in l and "bvec" in l:
fbvec=os.path.join(folder_sujeto,l)
# def to_register_dwi_to_mni(path_in, path_out, path_bvec, path_bval):
ref_name = utils.to_extract_filename(path_input)
finalname=os.path.join(path_output,ref_name + '_normalized' + d.extension)
if not os.path.exists(finalname ):
img_DWI = nib.load(path_input)
data_DWI = img_DWI.get_data()
affine_DWI = img_DWI.affine
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs)
b0 = data_DWI[..., gtab.b0s_mask]
mean_b0 = np.mean(b0, -1)
try:
mni_t2 = nib.load(d.standard_t2)
except:
mni_t2 = nib.load(d.standard_t1)
mni_t2_data = mni_t2.get_data()
MNI_T2_affine = mni_t2.affine
directionWarped = np.zeros(
(mni_t2_data.shape[0], mni_t2_data.shape[1], mni_t2_data.shape[2], data_DWI.shape[-1]),dtype=np.float32)
rangos = range(data_DWI.shape[-1])
affine, starting_affine = tools.affine_registration(mean_b0, mni_t2_data, moving_grid2world=affine_DWI,
static_grid2world=MNI_T2_affine)
print("terminado affine registration")
warped_moving, mapping = tools.syn_registration(mean_b0, mni_t2_data,
moving_grid2world=affine_DWI,
static_grid2world=MNI_T2_affine,
# step_length=0.1,
# sigma_diff=2.0,
metric='CC',
dim=3, level_iters=[10, 10, 5],
# prealign=affine.affine)
prealign=starting_affine)
print("terminado syn registration")
for gradientDirection in rangos:
print(gradientDirection)
directionWarped[:, :, :, gradientDirection] = mapping.transform(
data_DWI[:, :, :, gradientDirection].astype(int), interpolation='nearest')
nib.save(nib.Nifti1Image(directionWarped, MNI_T2_affine), finalname)
affine = nib.load(finalname).affine
list_path_atlas_1 = utils.registration_atlas_to(d.aan_atlas, path_output, affine, mapping)
with open(os.path.join(path_output,"list_path_atlas_1.txt"), "w") as f:
for s in list_path_atlas_1:
f.write(str(s) +"\n")
list_path_atlas_2 = utils.registration_atlas_to(d.morel_atlas, path_output, affine, mapping)
with open(os.path.join(path_output,"list_path_atlas_2.txt"), "w") as f:
for s in list_path_atlas_2:
f.write(str(s) +"\n")
list_path_atlas_3 = utils.registration_atlas_to(d.hypothalamus_atlas, path_output, affine, mapping)
with open(os.path.join(path_output,"list_path_atlas_3.txt"), "w") as f:
for s in list_path_atlas_3:
f.write(str(s) +"\n")
list_path_atlas_4 = utils.registration_atlas_to(d.harvard_oxford_cort_atlas, path_output, affine, mapping)
with open(os.path.join(path_output,"list_path_atlas_4.txt"), "w") as f:
for s in list_path_atlas_4:
f.write(str(s) +"\n")
print(finalname)
return path_input
sys.path += [os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + "/"]
from tareas.dependencias import utils
from tareas.dependencias import definitions as d
import nibabel as nib
from dipy.align.reslice import reslice
path_input = sys.argv[1]
path_output = sys.argv[2]
vox_sz = d.vox_sz
print(' - runnning Reslice...')
finalFileName = os.path.join(
path_output,
utils.to_extract_filename(path_input) + d.id_reslice + d.extension)
if not (os.path.exists(finalFileName)):
img = nib.load(path_input)
data = img.get_data()
affine = img.affine
old_vox_sz = img.header.get_zooms()[:3]
new_vox_sz = (vox_sz, vox_sz, vox_sz)
# Si el tamano del voxel es isotropico, no es necesario hacer el reslice
data, affine = reslice(data, affine, old_vox_sz, new_vox_sz)
nib.save(nib.Nifti1Image(data, affine), finalFileName)
print(finalFileName)
