def run_feat(bold_file, bold_folder, brainmask_file, feat_gen):
from nipype.interfaces.fsl import ImageStats, FEAT, Info
# from bm_functions import gen_default_feat_config
from numpy import shape
from textwrap import dedent
fslFilename = bold_folder + 'feat.fsf'
# Get the number of voxels in the 4D file
statComp = ImageStats()
statComp.inputs.in_file = bold_file
statComp.inputs.op_string = '-v'
numVox = int(statComp.run().outputs.out_stat[0])
# Get the number of raw volumes
statComp.inputs.split_4d = True
numVol = shape(statComp.run().outputs.out_stat)[0]
# Generate the file
standard_T1_brain = Info.standard_image('MNI152_T1_2mm_brain')
theString = feat_gen(bold_folder, bold_file, brainmask_file, standard_T1_brain, numVox, numVol)
with open(fslFilename,'w') as out_file:
out_file.write(dedent(theString))
out_file.close()
# Run feat using the previously manipulated config
runFeat = FEAT(fsf_file = fslFilename)
# Run and pass back the foldername
return runFeat.run().outputs.feat_dir
def run_feat(bold_file, bold_folder, brainmask_file, feat_gen):
from nipype.interfaces.fsl import ImageStats, FEAT, Info
# from bm_functions import gen_default_feat_config
from numpy import shape
from textwrap import dedent
fslFilename = bold_folder + 'feat.fsf'
# Get the number of voxels in the 4D file
statComp = ImageStats()
statComp.inputs.in_file = bold_file
statComp.inputs.op_string = '-v'
numVox = int(statComp.run().outputs.out_stat[0])
# Get the number of raw volumes
statComp.inputs.split_4d = True
numVol = shape(statComp.run().outputs.out_stat)[0]
# Generate the file
standard_T1_brain = Info.standard_image('MNI152_T1_2mm_brain')
theString = feat_gen(bold_folder, bold_file, brainmask_file,
standard_T1_brain, numVox, numVol)
with open(fslFilename, 'w') as out_file:
out_file.write(dedent(theString))
out_file.close()
# Run feat using the previously manipulated config
runFeat = FEAT(fsf_file=fslFilename)
# Run and pass back the foldername
return runFeat.run().outputs.feat_dir
def run_invert_T1(subj, T1_inv_path, betfMRI_folder, betMRI_folder):
print("\n 5.Calculating inverse T1")
stats = ImageStats(in_file=opj(betMRI_folder, subj, 'T1_brain.nii.gz'),
op_string='-R')
T1minmax = stats.run()
stats = ImageStats(in_file=opj(betfMRI_folder, subj, 'mean_brain.nii.gz'),
op_string='-R')
EPIminmax = stats.run()
factor = (T1minmax.outputs.out_stat[1] - T1minmax.outputs.out_stat[0]) / (
EPIminmax.outputs.out_stat[1] - EPIminmax.outputs.out_stat[0])
os.system("fslmaths " + opj(betMRI_folder, subj, 'T1_brain.nii.gz') +
" -mul -1" + " -add " + str(T1minmax.outputs.out_stat[1]) +
" -mul " + str(factor) + " " +
opj(betMRI_folder, subj, 'T1_brain_inv.nii.gz'))
def fslstats(infile, mask):
stats = ImageStats()
stats.inputs.in_file = infile
stats.inputs.op_string = '-M'
stats.inputs.mask_file = mask
cout = stats.run()
return cout.outputs.out_stat
def fslstats(infile, mask):
stats = ImageStats()
stats.inputs.in_file = infile
stats.inputs.op_string = '-M'
stats.inputs.mask_file = mask
cout = stats.run()
return cout.outputs.out_stat
def fslmath_imagestats(input_file, operation, output_prefix):
from nipype.interfaces.fsl import ImageStats
import os
statsM = ImageStats(in_file=input_file, op_string= operation)
print "ImageStats [" + os.path.basename(input_file) + "]:" + statsM.cmdline
res = statsM.run()
return res.outputs.out_stat
def get_volume(mask, thresh, out_file):
Mask = MathsCommand(in_file = mask,
args = "-thr {0} -bin".format(thresh),
out_file=out_file)
Mout = Mask.run()
Volume = ImageStats(in_file = mask, op_string = '-l {0} -V'.format(thresh))
Vout=Volume.run()
outstat = Vout.outputs.out_stat
return outstat[1]
def get_volume(mask):
"""
Fslstats -V returns volume [voxels mm3]
Fslstats -M returns mean value of non-zero voxels
Multiplying these gives the partial volume estimate
"""
Volume = ImageStats(in_file = mask, op_string = '-V -M')
Vout=Volume.run()
outstat = Vout.outputs.out_stat
return outstat[1] * outstat[2]
def fslstats(infile, mask):
"""
Wrapper for fslstats. Takes input file and extract mean from specified ROI mask
"""
stats = ImageStats()
stats.inputs.in_file = infile
stats.inputs.op_string = '-k %s -M'
stats.inputs.mask_file = mask
cout = stats.run()
return cout.outputs.out_stat
