for i in range(len(path_t1)):
clean_ana(os.path.join(path_t1[i], "T1.nii"),
1000.0,
4095.0,
overwrite=True)
"""
mask t1 and epi
"""
for i in range(len(path_t1)):
mask_ana(os.path.join(path_t1[i], "T1.nii"),
os.path.join(path_t1[i], "mask.nii"),
background_bright=False)
for i in range(len(path_epi)):
mask_epi(os.path.join(path_epi[i], "bepi.nii"),
os.path.join(path_t1[i], "pT1.nii"),
os.path.join(path_t1[i], "mask.nii"), niter_mask, sigma_mask,
file_cmap)
"""
flirt
"""
os.chdir(path_flirt)
flirt = FLIRT()
flirt.inputs.cost_func = "corratio"
flirt.inputs.dof = 6
flirt.inputs.interp = "trilinear" # trilinear, nearestneighbour, sinc or spline
flirt.inputs.in_file = os.path.join(path_epi_target, "pbepi.nii")
flirt.inputs.reference = os.path.join(path_epi_source, "pbepi.nii")
flirt.inputs.output_type = "NIFTI"
flirt.inputs.out_file = os.path.join(path_flirt, "flirt.nii")
flirt.inputs.out_matrix_file = os.path.join(path_flirt, "flirt_matrix.mat")
flirt.run()
n4.inputs.dimension = 3
n4.inputs.input_image = os.path.join(path_epi, "epi.nii")
n4.inputs.bias_image = os.path.join(path_epi, 'n4bias.nii')
n4.inputs.output_image = os.path.join(path_epi, "bepi.nii")
n4.run()
"""
clean ana
"""
clean_ana(os.path.join(path_t1, "T1.nii"), 1000.0, 4095.0, overwrite=True)
"""
mask t1 and epi
"""
mask_ana(os.path.join(path_t1, "T1.nii"),
os.path.join(path_t1, "mask.nii"),
background_bright=False)
mask_epi(os.path.join(path_epi, "bepi.nii"), os.path.join(path_t1, "pT1.nii"),
os.path.join(path_t1, "mask.nii"), niter_mask, sigma_mask)
"""
syn
"""
embedded_antsreg(
os.path.join(path_t1, "pT1.nii"), # source image
os.path.join(path_epi, "pbepi.nii"), # target image
run_rigid, # whether or not to run a rigid registration first
rigid_iterations, # number of iterations in the rigid step
run_affine, # whether or not to run an affine registration first
affine_iterations, # number of iterations in the affine step
run_syn, # whether or not to run a SyN registration
coarse_iterations, # number of iterations at the coarse level
medium_iterations, # number of iterations at the medium level
fine_iterations, # number of iterations at the fine level
cost_function, # CrossCorrelation or MutualInformation
# get brainmask
clean_ana(os.path.join(path_t1, "T1.nii"), 1000.0, 4095.0, overwrite=True)
mask_ana(os.path.join(path_t1, "T1.nii"),
os.path.join(path_t1, "mask.nii"),
background_bright=False)
# bias field correction
n4 = N4BiasFieldCorrection()
n4.inputs.dimension = 3
n4.inputs.input_image = os.path.join(path_mri, "orig.nii")
n4.inputs.bias_image = os.path.join(path_mri, "n4bias.nii")
n4.inputs.output_image = os.path.join(path_mri, "borig.nii")
n4.run()
mask_epi(os.path.join(path_mri, "borig.nii"), os.path.join(path_t1, "pT1.nii"),
os.path.join(path_t1, "mask.nii"), niter_mask, sigma_mask, input_cmap)
multiply_images(os.path.join(path_mri, "orig.nii"),
os.path.join(path_t1, "mask_def-img3.nii.gz"),
os.path.join(path_mri, "brainmask.nii"))
# convert orig and brainmask to mgz
mgh2nii(os.path.join(path_mri, "orig.nii"), path_mri, out_type="mgz")
mgh2nii(os.path.join(path_mri, "brainmask.nii"), path_mri, out_type="mgz")
# choose initialization method
if init_reg == "header":
bbr_var = " --init-header"
elif init_reg == "freesurfer":
bbr_var = " --init-coreg"
elif init_reg == "fsl":
# copy input files into temporary folder
sh.copyfile(input_t1[i], os.path.join(path_temp,"T1"+ext_t1))
sh.copyfile(input_mask[i], os.path.join(path_temp,"mask"+ext_mask))
# get mean time series
get_mean(input_epi[i], path_temp, "epi", type="mean")
# new filenames
file_epi_mean = os.path.join(path_temp, "mean_epi"+ext_epi)
file_t1 = os.path.join(path_temp,"T1"+ext_t1)
file_mask = os.path.join(path_temp,"mask"+ext_mask)
# get mask
clean_ana(file_t1, 1000.0, 4095.0, overwrite=True) # clean ana
mask_ana(file_t1, file_mask, background_bright=False) # mask t1
mask_epi(file_epi_mean, os.path.join(path_temp,"pT1"+ext_t1), file_mask, niter_mask, sigma_mask)
# get outlier count within mask
os.system("3dToutcount " + \
"-mask " + os.path.join(path_temp,"mask_def-img3.nii.gz") + " " + \
"-fraction " + \
"-qthr " +str(qthr) + " " + input_epi[i] + " " + \
" > " + os.path.join(path_output,"outlier_afni.txt"))
# make plot
log_data = np.loadtxt(os.path.join(path_output,"outlier_afni.txt"))
# plots
fig, ax = plt.subplots()
ax.plot(log_data, "r")
ax.set_xlabel("Time in TR")