def init_func_folder(self, folder_name):
func_folder = MRT_Methods.init_mrt_folder(folder_name, "func")
anat_file, func_file, sbref_file, AP, PA = MRT_Methods.load_initial_files(
folder_name, "func")
func_file, func_mask = self.MotionCorrection_and_Mask(
func_file, func_folder)
tmp_dir = f"{func_folder}/tmp"
if not os.path.isdir(tmp_dir):
os.mkdir(tmp_dir)
files = [anat_file, sbref_file, AP, PA]
for i, file in enumerate(files):
files[i] = shutil.copy(file,
f"{func_folder}/{file.split(os.sep)[-1]}")
anat_file, sbref_file, AP, PA = files
return anat_file, func_file, func_mask, sbref_file, tmp_dir, AP, PA
def fibre_orientation(self, dwi_file: str, dwi_mask: str, wm_resp: str,
gm_resp: str, csf_resp: str):
fod_wm, fod_gm, fod_csf = [
resp.replace("response", "FOD")
for resp in [wm_resp, gm_resp, csf_resp]
]
fod_wm, fod_gm, fod_csf = [
odf.replace("txt", "mif") for odf in [fod_wm, fod_gm, fod_csf]
]
tissues = f"{os.path.dirname(dwi_file)}/tissues.mif"
if not os.path.isfile(tissues):
print("Estimating Fibre Orientation Distributions")
fod_wm, fod_gm, fod_csf = mrt_methods.calc_fibre_orientation(
dwi_file, dwi_mask, wm_resp, gm_resp, csf_resp)
tissues = mrt_methods.gen_tissues_orient(fod_wm, fod_gm, fod_csf)
return fod_wm, fod_gm, fod_csf, tissues
def Gen_FA(self, dwi_file: str, mask_file: str):
dti_file = f"{os.path.dirname(dwi_file)}/dti.mif"
fa_file = f"{os.path.dirname(dwi_file)}/fa.mif"
if not os.path.isfile(dti_file) or not os.path.isfile(fa_file):
print("Generating FA image for group-level analysis")
fa_file = mrt_methods.fit_tensors(dwi_file, mask_file, dti_file)
return fa_file
def gen_5tt(self, t1_registered: str, t1_mask_regisitered: str):
out_vis = f"{os.path.dirname(t1_registered)}/vis.mif"
out_5tt = f"{os.path.dirname(t1_registered)}/5TT.mif"
if not os.path.isfile(out_vis):
print(
"Generating five-tissue-type (5TT) image for Anatomically-Constrained Tractography (ACT)"
)
out_vis, out_5tt = mrt_methods.five_tissue(t1_registered,
t1_mask_regisitered)
return out_vis, out_5tt
def T1_correction(self, anat: str):
anat_dir = f"{os.path.dirname(anat)}/T1.anat"
if not os.path.isdir(anat_dir):
print(
"Performing brain extraction and B1 bias field correction of T1 image"
)
bias_corr_brain, bias_corr_mask = MRT_Methods.T1_correction(anat)
else:
corr_list = glob.glob(f"{anat_dir}/T1_biascorr_")
corr_list.sort()
bias_corr_brain, bias_corr_mask = corr_list
return bias_corr_brain, bias_corr_mask
def gen_dwi_to_T1_contrast(self, dwi_file: str, dwi_mask: str,
t1_file: str, t1_mask: str):
meanbzero = dwi_file.replace(".mif", "_meanbzero.mif")
dwi_pseudoT1 = dwi_file.replace(".mif", "_pseudoT1.mif")
T1_pseudobzero = f"{os.path.dirname(dwi_file)}/T1_pseudobzero.mif"
if not os.path.isfile(T1_pseudobzero):
print(
"Generating contrast-matched images for inter-modal registration between DWIs and T1"
)
meanbzero, dwi_pseudoT1, T1_pseudobzero = mrt_methods.DWI_to_T1_cont(
dwi_file, dwi_mask, t1_file, t1_mask)
return meanbzero, dwi_pseudoT1, T1_pseudobzero
def sort_files(self, folder_name):
connectome, dwi, tractogram, anat = [
f"{folder_name}/{sub_dir}"
for sub_dir in ["connectome", "dwi", "tractogram", "anat"]
]
for sub_dir in [connectome, dwi, tractogram, anat]:
if not os.path.isdir(sub_dir):
os.makedirs(sub_dir)
new_file = None
for file in glob.glob(f"{folder_name}/*"):
if not os.path.isdir(file):
header = file.split(os.sep)[-1]
if ("dwi" in header or "AP" in header or "b0" in header
or "tissues" in header or "bzero" in header):
new_file = f'{dwi}/{header.replace("mif","nii")}'
if "mif" in file and not os.path.isfile(new_file):
new_file = mrt_methods.convert_to_mif(file, new_file)
else:
shutil.copy(file, f"{dwi}/{header}")
elif "T1" in file or "vis" in file or "5TT" in file:
new_file = f'{anat}/{file.split(os.sep)[-1].replace("mif", "nii")}'
if "mif" in file and not os.path.isfile(new_file):
new_file = mrt_methods.convert_to_mif(file, new_file)
else:
shutil.copy(file, f"{anat}/{file.split(os.sep)[-1]}")
else:
new_file = (
f'{tractogram}/{file.split(os.sep)[-1].replace("mif", "nii")}'
)
if "mif" in file and not os.path.isfile(new_file):
new_file = mrt_methods.convert_to_mif(file, new_file)
else:
shutil.copy(file,
f"{tractogram}/{file.split(os.sep)[-1]}")
if new_file:
if os.path.isfile(new_file):
print(
f"Moved {file.split(os.sep)[-1]} to {os.sep.join(new_file.split(os.sep)[-2:])}"
)
def init_func_folder(self, folder_name):
mrt_folder = MRT_Methods.init_mrt_folder(folder_name, "func")
anat_file, func, sbref, AP, PA, bvec, bval = MRT_Methods.load_initial_files(
folder_name, "func")
dwi, mask = self.MotionCorrection_and_Mask(AP, mrt_folder)
file_list = [anat_file, func, sbref, AP, PA, mask]
print("Converting files into .mif format...")
for f in file_list:
f_name = f.split(os.sep)[-1]
new_f = f'{mrt_folder}/{f_name.replace(".nii.gz",".mif")}'
if not os.path.isfile(new_f):
if "T1" in f:
print("Importing T1 image into temporary directory")
new_anat = MRT_Methods.convert_to_mif(f, new_f)
elif "mask" in f:
print("Importing mask image into temporary directory")
new_mask = MRT_Methods.convert_to_mif(f, new_f)
else:
if "AP" in f:
print("Importing DWI data into temporary directory")
new_dwi = MRT_Methods.convert_to_mif(
f, new_f, bvec, bval)
elif "PA" in f:
print(
"Importing reversed phased encode data into temporary directory"
)
new_PA = MRT_Methods.convert_to_mif(f, new_f)
elif "rest_bold" in f:
print(
"Importing rs-fMRI data into temporary directory")
new_func = MRT_Methods.convert_to_mif(f, new_f)
elif "rest_sbref" in f:
print("Importing rs reference to temporary directory")
new_sbref = MRT_Methods.convert_to_mif(f, new_f)
else:
if "T1" in f:
new_anat = new_f
elif "mask" in f:
new_mask = new_f
else:
if "AP" in f:
new_dwi = new_f
elif "PA" in f:
new_PA = new_f
elif "rest_bold" in f:
new_func = new_f
elif "rest_sbref" in f:
new_sbref = new_f
return new_anat, new_func, new_sbref, new_dwi, new_mask, new_PA
def gen_Response(self, dwi_file: str, dwi_mask: str):
working_dir = os.path.dirname(dwi_file)
wm_resp, gm_resp, csf_resp = [
f"{working_dir}/response_{tissue}.txt"
for tissue in ["wm", "gm", "csf"]
]
if not os.path.isfile(wm_resp):
print(
"Estimating tissue response functions for spherical deconvolution"
)
wm_resp, gm_resp, csf_resp = mrt_methods.gen_response(
dwi_file, dwi_mask, working_dir)
return wm_resp, gm_resp, csf_resp
def reg_dwi_and_t1(
self,
dwi_file: str,
t1_brain: str,
dwi_pseudoT1: str,
T1_pseudobzero: str,
meanbzero: str,
t1_mask: str,
dwi_mask: str,
):
working_dir = os.path.dirname(dwi_file)
t1_registered = f"{working_dir}/T1_registered.mif"
t1_mask_registered = f"{working_dir}/T1_mask_registered.mif"
if not os.path.isfile(t1_registered) or not os.path.isfile(
t1_mask_registered):
print("Performing registration between DWIs and T1")
t1_registered, t1_mask_registered = mrt_methods.reg_dwi_T1(
dwi_file,
t1_brain,
dwi_pseudoT1,
T1_pseudobzero,
meanbzero,
t1_mask,
dwi_mask,
True,
)
else:
t1_registered, t1_mask_registered = mrt_methods.reg_dwi_T1(
dwi_file,
t1_brain,
dwi_pseudoT1,
T1_pseudobzero,
meanbzero,
t1_mask,
dwi_mask,
False,
)
return t1_registered, t1_mask_registered
def run(self):
if "dwi" in self.data_type:
for i, folder_name in enumerate(self.subjects_dirs):
subj = self.subjects[i]
print(f"Analyzing {subj}...")
anat, dwi, mask, PA = self.init_mrt_folder(folder_name)
denoised = self.denoise(dwi_file=dwi, dwi_mask=mask)
degibbs = self.unring(denoised)
preprocessed = self.DWI_preproc(degibbs, PA)
dwi_file = self.bias_correct(preprocessed, subj=subj)
bias_corr_brain, bias_corr_mask = MRT_Methods.T1_correction(
anat)
elif "func" in self.data_type:
for i, folder_name in enumerate(self.subjects_dirs):
subj = self.subjects[i]
print(f"Analyzing {subj}...")
anat, func, sbref, AP, mask, PA = self.init_func_folder(
folder_name)
denoised = self.denoise(dwi_file=AP, dwi_mask=mask)
degibbs = self.unring(denoised)
preprocessed = self.DWI_preproc(degibbs, PA, func)
dwi_file = self.bias_correct(preprocessed, subj=subj)
bias_corr_brain, bias_corr_mask = MRT_Methods.T1_correction(
anat)
def DWI_preproc(self, degibbs: str, PA: str, func: str = None):
if not func:
data_type = "dwi"
func = None
out_file = f"{os.path.dirname(degibbs)}/dwi1_preprocessed.mif"
else:
data_type = "func"
func = func
out_file = f"{os.path.dirname(degibbs)}/func_preprocessed.mif"
if not os.path.isfile(out_file):
print("Performing various geometric corrections of DWIs")
out_file = MRT_Methods.DWI_prep(
degibbs=degibbs,
PA=PA,
out_file=out_file,
data_type=self.data_type,
func=func,
)
return out_file
def convert_tck_to_trk(self, tck_file: str, dwi_file: str):
trk_file = tck_file.replace("tck", "trk")
if not os.path.isfile(trk_file):
print("Converting tractography file from .tck format to .trk")
trk_file = mrt_methods.convert_tck_to_trk(tck_file, dwi_file)
return trk_file
def calc_tracts(self, fod_wm: str, seg_5tt: str):
Tracts = f"{os.path.dirname(fod_wm)}/tractogram.tck"
if not os.path.isfile(Tracts):
print("Performing whole-brain fibre-tracking")
Tracts = mrt_methods.generate_tracks(fod_wm, seg_5tt)
return Tracts
def unring(self, denoised: str):
out_file = denoised.replace(".mif", "_degibbs.mif")
if not os.path.isfile(out_file):
print("Performing Gibbs ringing removal for DWI data")
MRT_Methods.Unring(in_file=denoised, out_file=out_file)
return out_file
def bias_correct(self, preprocessed: str, subj: str):
out_file = f"{os.path.dirname(preprocessed)}/{subj}_dwi.mif"
if not os.path.isfile(out_file):
print("Performing initial B1 bias field correction of DWIs")
out_file = MRT_Methods.bias_correct(preprocessed, out_file)
return out_file
def denoise(self, dwi_file: str, dwi_mask: str):
out_file = dwi_file.replace(".mif", "_denoised.mif")
if not os.path.isfile(out_file):
print("Performing MP-PCA denoising of DWI data")
out_file = MRT_Methods.Denoise(dwi_file, dwi_mask, out_file)
return out_file