def save_results_to_nifti(res, nifti, fitmethod, outname=None):
if nifti.endswith('.nii'):
nifti_base = nifti[:-4]
elif nifti.endswith('.nii.gz'):
nifti_base = nifti[:-7]
if fitmethod == 'two_parameter_monoexp':
t2map = out.take(indices=0, axis=FIT_AXIS)
m0map = out.take(indices=1, axis=FIT_AXIS)
R2map = out.take(indices=2, axis=FIT_AXIS)
output_name_list = []
output_basename_list = ['t2map', 'm0map', 'R2map']
for item in output_basename_list:
output_name_list.append(str(nifti_base) + '_' + item)
output_list = [t2map, m0map, R2map]
for num, item in enumerate(output_name_list):
writer = niftiWriter(procpar, output_list[num])
writer.write(item)
if fitmethod == 'three_parameter_monoexp':
t2map = out.take(indices=0, axis=FIT_AXIS)
m0map = out.take(indices=1, axis=FIT_AXIS)
constmap = out.take(indeices=2, axis=FIT_AXIS)
R2map = out.take(indices=3, axis=FIT_AXIS)
output_name_list = []
output_basename_list = ['t2map', 'm0map', 'constmap', 'R2map']
for item in output_basename_list:
output_name_list.append(str(nifti_base) + '_' + item)
output_list = [t2map, m0map, R2map]
for num, item in enumerate(output_name_list):
writer = niftiWriter(procpar, output_list[num])
writer.write(item)
if fitmethod == 'four_parameter_duoexp':
m0map = out.take(indices=0, axis=FIT_AXIS)
t2smap = out.take(indices=1, axis=FIT_AXIS)
t2lmap = out.take(indices=2, axis=FIT_AXIS)
fractmap = out.take(indeices=3, axis=FIT_AXIS)
R2map = out.take(indices=4, axis=FIT_AXIS)
output_name_list = []
output_basename_list = [
'm0map', 't2smap', 't2lmap', 'fractmap', 'R2map'
]
for item in output_basename_list:
output_name_list.append(str(nifti_base) + '_' + item)
output_list = [t2map, m0map, R2map]
for num, item in enumerate(output_name_list):
writer = niftiWriter(procpar, output_list[num])
writer.write(item)
def main(fid, pp):
fr = fidReader(fid)
fr.print_header()
data, hdr = fr.read()
rec = recon(pp, data)
kspace, imspace = rec.recon()
#rec.recon()
w_im = niftiWriter(pp, imspace)
w_k = niftiWriter(pp, kspace)
w_k.write_from_fid('out_kspace')
w_im.write_from_fid('out_imspace')
def fdf2nii(indir, out, saveprocpar):
if indir.endswith('.img'):
if out == None:
out = str(indir[:-4].split('/')[-1] + '.nii')
out_ppname = '.' + out[:-4] + '.procpar'
else:
out_ppname = '.' + out + '.procpar'
else:
print('Please give .img as input directory')
return
try:
procpar = indir + '/procpar'
except:
print('procpar not found, quitting ...')
return
rdr = fdfReader(indir, out)
(hdr, data) = rdr.read()
writer = niftiWriter(procpar, data)
writer.write(out)
if saveprocpar:
copy2(procpar, out_ppname)
print('procpar saved as hidden: ' + str(out_ppname))
def make_fieldmap(minus_pi_data, plus_pi_data, procpar):
"""
Make phase correction factor from minus Pi shifted and plus Pi shifted data. Does
unwrapping with prelude in two steps: first step is preliminary field map with output
mask, then this output mask is holefilled and the unwrapping is done again
"""
output_name_fieldmap = self.proc_dir + '/fieldmap_wrapped'
output_name_fieldmap_uw = self.proc_dir + '/fieldmap_unwrapped'
output_name_fieldmap_mask = self.proc_dir + '/fieldmap_premask'
output_name_fieldmap_mask_corr = self.proc_dir + '/fieldmap_mask'
magnitude_name_for_prelude_corr = self.proc_dir + '/masked_abs'
data = np.multiply(plus_pi_data, np.conj(minus_pi_data))
fieldmap = np.arctan2(np.imag(data), np.real(data))
niftiwriter = niftiWriter(procpar, fieldmap)
niftiwriter.write(output_name_fieldmap)
print('Unwrapping fieldmap ...')
# make initial field map
os.system('prelude -p '+output_name_fieldmap+' -a '+magnitude_name_for_prelude+ \
' -u '+output_name_fieldmap_uw+' --savemask='+output_name_fieldmap_mask)
# close gaps in mask
os.system('fslmaths ' + output_name_fieldmap_mask + ' -fillh ' +
output_name_fieldmap_mask_corr)
# make new absolute with mask
os.system('fslmaths '+magnitude_name_for_prelude+' -mul '+output_name_fieldmap_mask_corr+\
' '+magnitude_name_for_prelude_corr)
# make final field map
os.system('prelude -p '+output_name_fieldmap+' -a '+magnitude_name_for_prelude_corr+ \
' -u '+output_name_fieldmap_uw)
print('Fieldmap done!')
return
def fid2nii(indir,
out=None,
saveprocpar=True,
save_kspace=False,
save_imgspace=True):
if not os.path.isdir(indir):
raise (Exception('Please specify input fid directory'))
if out == None:
# if output is not given, take fid as basename for new dir
out = indir[:-4] + '.nifti/'
if not os.path.exists(out):
os.makedirs(out)
fid = indir + '/fid'
procpar = indir + '/procpar'
ppdict = procparReader(procpar).read()
fid_data, fid_header = fidReader(fid, procpar).read()
kspacemaker = kSpaceMaker(fid_data, procpar, fid_header)
kspace = kspacemaker.make()
imgspace = imgSpaceMaker(kspace, procpar).make()
kspace_real = [] # put separate channel data into separate lements of list
kspace_imag = []
for i in range(len(ppdict['rcvrs'])):
kspace_real.append(np.real(kspace[i, ...]))
kspace_imag.append(np.imag(kspace[i, ...]))
writer1 = niftiWriter(procpar, kspace_real[-1])
writer1.write(out + 'kspace_real_ch' + str(i))
writer2 = niftiWriter(procpar, kspace_imag[-1])
writer2.write(out + 'kspace_imag_ch' + str(i))
sumimg = np.sum(np.absolute(imgspace), axis=0)
writer_img = niftiWriter(procpar, sumimg)
writer_img.write(out + 'imgspace_sum')
if saveprocpar == True:
copyfile(procpar, out + '/procpar')
#parser = ArgumentParser()
#parser.add_argument('inputfile')
#args = parser.parse_args()
# test data is kmake_test.nii.gz
TESTFILE_REAL = 'tempdata/kmake_test_real.nii.gz'
TESTFILE_IMAG = 'tempdata/kmake_test_imag.nii.gz'
# TODO
# for testing add another dim to kspace data to axis0
TESTPP = 'tempdata/kmake_test.procpar'
TEST_OUTPUT = 'tempdata/imake_test'
kspace_real = nib.load(TESTFILE_REAL).get_fdata()
kspace_imag = nib.load(TESTFILE_IMAG).get_fdata()
kspace = np.vectorize(complex)(kspace_real, kspace_imag)
imgspacemaker = imgSpaceMaker(kspace, TESTPP)
img = imgspacemaker.make()
print('img shape: ' + str(img.shape))
img_to_write = np.absolute(img[..., 0])
niPlotter(img_to_write).plot_slices()
writer = niftiWriter(TESTPP, img_to_write)
writer.write(TEST_OUTPUT)
str(f.shape))
print('dixon.ideal output shape : ' + str(ro.shape) + ' ; ' + str(y.shape))
abs_ro1 = np.absolute(np.vectorize(complex)(ro[0, ...], ro[1, ...]))
abs_ro2 = np.absolute(np.vectorize(complex)(ro[2, ...], ro[3, ...]))
print('abs_ro shape : ' + str(abs_ro1.shape))
# make ro percentage
rel_ro1 = np.divide(abs_ro1, abs_ro1 + abs_ro2)
rel_ro2 = np.divide(abs_ro2, abs_ro1 + abs_ro2)
# plotting stuff
#fig, axes = plt.subplots(4,1)
#ro_plot = []
# writing to nifti
writer1 = niftiWriter(procpar, rel_ro1)
writer2 = niftiWriter(procpar, rel_ro2)
writer3 = niftiWriter(procpar, f)
writer4 = niftiWriter(procpar, phasemap1)
writer5 = niftiWriter(procpar, phasemap2)
writer6 = niftiWriter(procpar, phasemap3)
writer7 = niftiWriter(procpar, magnitude1)
writer8 = niftiWriter(procpar, magnitude2)
writer9 = niftiWriter(procpar, magnitude3)
writer10 = niftiWriter(procpar, y[0])
writer1.write('water')
writer2.write('fat')
writer3.write('fieldmap')
writer4.write('phase1')
writer5.write('phase2')
return kspace
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument('input_fid_dir') # just give the whole directory
args = parser.parse_args()
fid = args.input_fid_dir + '/fid'
procpar = args.input_fid_dir + '/procpar'
fidrdr = fidReader(fid, procpar)
data, fidheader = fidrdr.read()
fidrdr.print_header()
kspacemaker = kSpaceMaker(data, procpar, fidheader)
#kspacemaker.print_fid_header()
kspace = kspacemaker.make()
kspace_real = np.real(kspace[0, ...])
kspace_imag = np.imag(kspace[0, ...])
writer_re = niftiWriter(procpar, kspace_real)
writer_re.write('tempdata/kmake_test_real')
writer_im = niftiWriter(procpar, kspace_imag)
writer_im.write('tempdata/kmake_test_imag')
plotter = niPlotter(kspace_real)
plotter.plot_slices()
os.system('cp ' + str(procpar) + ' tempdata/kmake_test.procpar')
def run(self):
def make_fieldmap(minus_pi_data, plus_pi_data, procpar):
"""
Make phase correction factor from minus Pi shifted and plus Pi shifted data. Does
unwrapping with prelude in two steps: first step is preliminary field map with output
mask, then this output mask is holefilled and the unwrapping is done again
"""
output_name_fieldmap = self.proc_dir + '/fieldmap_wrapped'
output_name_fieldmap_uw = self.proc_dir + '/fieldmap_unwrapped'
output_name_fieldmap_mask = self.proc_dir + '/fieldmap_premask'
output_name_fieldmap_mask_corr = self.proc_dir + '/fieldmap_mask'
magnitude_name_for_prelude_corr = self.proc_dir + '/masked_abs'
data = np.multiply(plus_pi_data, np.conj(minus_pi_data))
fieldmap = np.arctan2(np.imag(data), np.real(data))
niftiwriter = niftiWriter(procpar, fieldmap)
niftiwriter.write(output_name_fieldmap)
print('Unwrapping fieldmap ...')
# make initial field map
os.system('prelude -p '+output_name_fieldmap+' -a '+magnitude_name_for_prelude+ \
' -u '+output_name_fieldmap_uw+' --savemask='+output_name_fieldmap_mask)
# close gaps in mask
os.system('fslmaths ' + output_name_fieldmap_mask + ' -fillh ' +
output_name_fieldmap_mask_corr)
# make new absolute with mask
os.system('fslmaths '+magnitude_name_for_prelude+' -mul '+output_name_fieldmap_mask_corr+\
' '+magnitude_name_for_prelude_corr)
# make final field map
os.system('prelude -p '+output_name_fieldmap+' -a '+magnitude_name_for_prelude_corr+ \
' -u '+output_name_fieldmap_uw)
print('Fieldmap done!')
return
def inverse_fourier_transform2D(kspace_data):
# changed axes 2,3
print('pproc kdata shape {}'.format(kspace_data.shape))
kspace_data = np.fft.fftshift(kspace_data, axes=(0, 1))
imgspace_data = np.fft.ifft2(kspace_data,
axes=(0, 1),
norm='ortho')
imgspace_data = np.fft.ifftshift(imgspace_data, axes=(0, 1))
return imgspace_data
if not os.path.exists(self.proc_dir):
os.makedirs(self.proc_dir)
if not self.preproc_from_fid:
open(self.preproc_data, 'w').close()
procpar = []
roshift = []
for item in self.combined_names:
# combined_names is a list of lists containing real and imaginary parts
procpar.append(item[0] + '/procpar')
ppr = procparReader(item[0] + '/procpar')
shift = int(float(ppr.read()['roshift']) *
1000000) # write it in microsec for naming
roshift.append(float(ppr.read()['roshift']))
hdr, data_re = fdfReader(item[0], 'out').read()
hdr, data_im = fdfReader(item[1], 'out').read()
kspace_data = np.vectorize(complex)(data_re, data_im)
imgspace_data = inverse_fourier_transform2D(kspace_data)
magnitude_data = np.absolute(imgspace_data)
phase_data = np.arctan2(np.imag(imgspace_data),
np.real(imgspace_data))
niftiwriter = niftiWriter(item[0] + '/procpar', magnitude_data)
output_name_magnitude = self.proc_dir + '/' + self.pslabel + '_' + str(
shift) + 'us_mag'
niftiwriter.write(output_name_magnitude)
niftiwriter = niftiWriter(item[1] + '/procpar', phase_data)
output_name_phase = self.proc_dir + '/' + self.pslabel + '_' + str(
shift) + 'us_ph'
niftiwriter.write(output_name_phase)
output_name_unwrapped = self.proc_dir + '/' + self.pslabel + '_' + str(
shift) + 'us_unwrapped_ph'
if float(ppr.read()['roshift']) == 0.00037:
plus_pi_data = imgspace_data
if float(ppr.read()['roshift']) == -0.00037:
minus_pi_data = imgspace_data
if float(ppr.read()['roshift']) == 0:
magnitude_name_for_prelude = output_name_magnitude
copyfile(item[0] + '/procpar', self.proc_dir + '/procpar')
if self.unwrap_all:
print('Unwrapping phasemaps ...')
os.system('prelude -p '+output_name_phase+' -a '+output_name_magnitude+ \
' -u '+output_name_unwrapped)
with open(self.preproc_data, 'a') as openfile:
line = str(float(ppr.read()['roshift']))+','\
+output_name_magnitude+','\
+output_name_phase+'\n'
openfile.write(line)
if self.preproc_from_fid:
pass
if self.make_fieldmap:
make_fieldmap(minus_pi_data, plus_pi_data, procpar[0])
return