def proc_file(infile, opts):
# figure out the output filename, and see if it exists
basefilename = splitext_addext(os.path.basename(infile))[0]
if opts.outdir is not None:
# set output path
basefilename = os.path.join(opts.outdir, basefilename)
# prep a file
if opts.compressed:
verbose('Using gzip compression')
outfilename = basefilename + '.nii.gz'
else:
outfilename = basefilename + '.nii'
if os.path.isfile(outfilename) and not opts.overwrite:
raise IOError('Output file "%s" exists, use --overwrite to '
'overwrite it' % outfilename)
# load the PAR header and data
scaling = 'dv' if opts.scaling == 'off' else opts.scaling
infile = fname_ext_ul_case(infile)
pr_img = pr.load(infile,
permit_truncated=opts.permit_truncated,
scaling=scaling,
strict_sort=opts.strict_sort)
pr_hdr = pr_img.header
affine = pr_hdr.get_affine(origin=opts.origin)
slope, intercept = pr_hdr.get_data_scaling(scaling)
if opts.scaling != 'off':
verbose('Using data scaling "%s"' % opts.scaling)
# get original scaling, and decide if we scale in-place or not
if opts.scaling == 'off':
slope = np.array([1.])
intercept = np.array([0.])
in_data = pr_img.dataobj.get_unscaled()
out_dtype = pr_hdr.get_data_dtype()
elif not np.any(np.diff(slope)) and not np.any(np.diff(intercept)):
# Single scalefactor case
slope = slope.ravel()[0]
intercept = intercept.ravel()[0]
in_data = pr_img.dataobj.get_unscaled()
out_dtype = pr_hdr.get_data_dtype()
else:
# Multi scalefactor case
slope = np.array([1.])
intercept = np.array([0.])
in_data = np.array(pr_img.dataobj)
out_dtype = np.float64
# Reorient data block to LAS+ if necessary
ornt = io_orientation(np.diag([-1, 1, 1, 1]).dot(affine))
if np.all(ornt == [[0, 1], [1, 1], [2, 1]]): # already in LAS+
t_aff = np.eye(4)
else: # Not in LAS+
t_aff = inv_ornt_aff(ornt, pr_img.shape)
affine = np.dot(affine, t_aff)
in_data = apply_orientation(in_data, ornt)
bvals, bvecs = pr_hdr.get_bvals_bvecs()
if not opts.keep_trace: # discard Philips DTI trace if present
if bvecs is not None:
bad_mask = np.logical_and(bvals != 0, (bvecs == 0).all(axis=1))
if bad_mask.sum() > 0:
pl = 's' if bad_mask.sum() != 1 else ''
verbose('Removing %s DTI trace volume%s' %
(bad_mask.sum(), pl))
good_mask = ~bad_mask
in_data = in_data[..., good_mask]
bvals = bvals[good_mask]
bvecs = bvecs[good_mask]
# Make corresponding NIfTI image
nimg = nifti1.Nifti1Image(in_data, affine, pr_hdr)
nhdr = nimg.header
nhdr.set_data_dtype(out_dtype)
nhdr.set_slope_inter(slope, intercept)
nhdr.set_sform(affine, code=1)
nhdr.set_qform(affine, code=1)
if 'parse' in opts.minmax:
# need to get the scaled data
verbose('Loading (and scaling) the data to determine value range')
if opts.minmax[0] == 'parse':
nhdr['cal_min'] = in_data.min() * slope + intercept
else:
nhdr['cal_min'] = float(opts.minmax[0])
if opts.minmax[1] == 'parse':
nhdr['cal_max'] = in_data.max() * slope + intercept
else:
nhdr['cal_max'] = float(opts.minmax[1])
# container for potential NIfTI1 header extensions
if opts.store_header:
# dump the full PAR header content into an extension
with open(infile, 'rb') as fobj: # contents must be bytes
hdr_dump = fobj.read()
dump_ext = nifti1.Nifti1Extension('comment', hdr_dump)
nhdr.extensions.append(dump_ext)
verbose('Writing %s' % outfilename)
nibabel.save(nimg, outfilename)
# write out bvals/bvecs if requested
if opts.bvs:
if bvals is None and bvecs is None:
verbose('No DTI volumes detected, bvals and bvecs not written')
elif bvecs is None:
verbose('DTI volumes detected, but no diffusion direction info was'
'found. Writing .bvals file only.')
with open(basefilename + '.bvals', 'w') as fid:
# np.savetxt could do this, but it's just a loop anyway
for val in bvals:
fid.write('%s ' % val)
fid.write('\n')
else:
verbose('Writing .bvals and .bvecs files')
# Transform bvecs with reorientation affine
orig2new = npl.inv(t_aff)
bv_reorient = from_matvec(to_matvec(orig2new)[0], [0, 0, 0])
bvecs = apply_affine(bv_reorient, bvecs)
with open(basefilename + '.bvals', 'w') as fid:
# np.savetxt could do this, but it's just a loop anyway
for val in bvals:
fid.write('%s ' % val)
fid.write('\n')
with open(basefilename + '.bvecs', 'w') as fid:
for row in bvecs.T:
for val in row:
fid.write('%s ' % val)
fid.write('\n')
# export data labels varying along the 4th dimensions if requested
if opts.vol_info:
labels = pr_img.header.get_volume_labels()
if len(labels) > 0:
vol_keys = list(labels.keys())
with open(basefilename + '.ordering.csv', 'w') as csvfile:
csvwriter = csv.writer(csvfile, delimiter=',')
csvwriter.writerow(vol_keys)
for vals in zip(*[labels[k] for k in vol_keys]):
csvwriter.writerow(vals)
# write out dwell time if requested
if opts.dwell_time:
try:
dwell_time = calculate_dwell_time(pr_hdr.get_water_fat_shift(),
pr_hdr.get_echo_train_length(),
opts.field_strength)
except MRIError:
verbose('No EPI factors, dwell time not written')
else:
verbose('Writing dwell time (%r sec) calculated assuming %sT '
'magnet' % (dwell_time, opts.field_strength))
with open(basefilename + '.dwell_time', 'w') as fid:
fid.write('%r\n' % dwell_time)
def proc_file(infile, opts):
# figure out the output filename, and see if it exists
basefilename = splitext_addext(os.path.basename(infile))[0]
if opts.outdir is not None:
# set output path
basefilename = os.path.join(opts.outdir, basefilename)
# prep a file
if opts.compressed:
verbose("Using gzip compression")
outfilename = basefilename + ".nii.gz"
else:
outfilename = basefilename + ".nii"
if os.path.isfile(outfilename) and not opts.overwrite:
raise IOError('Output file "%s" exists, use --overwrite to ' "overwrite it" % outfilename)
# load the PAR header and data
scaling = "dv" if opts.scaling == "off" else opts.scaling
infile = fname_ext_ul_case(infile)
pr_img = pr.load(infile, permit_truncated=opts.permit_truncated, scaling=scaling, strict_sort=opts.strict_sort)
pr_hdr = pr_img.header
affine = pr_hdr.get_affine(origin=opts.origin)
slope, intercept = pr_hdr.get_data_scaling(scaling)
if opts.scaling != "off":
verbose('Using data scaling "%s"' % opts.scaling)
# get original scaling, and decide if we scale in-place or not
if opts.scaling == "off":
slope = np.array([1.0])
intercept = np.array([0.0])
in_data = pr_img.dataobj.get_unscaled()
out_dtype = pr_hdr.get_data_dtype()
elif not np.any(np.diff(slope)) and not np.any(np.diff(intercept)):
# Single scalefactor case
slope = slope.ravel()[0]
intercept = intercept.ravel()[0]
in_data = pr_img.dataobj.get_unscaled()
out_dtype = pr_hdr.get_data_dtype()
else:
# Multi scalefactor case
slope = np.array([1.0])
intercept = np.array([0.0])
in_data = np.array(pr_img.dataobj)
out_dtype = np.float64
# Reorient data block to LAS+ if necessary
ornt = io_orientation(np.diag([-1, 1, 1, 1]).dot(affine))
if np.all(ornt == [[0, 1], [1, 1], [2, 1]]): # already in LAS+
t_aff = np.eye(4)
else: # Not in LAS+
t_aff = inv_ornt_aff(ornt, pr_img.shape)
affine = np.dot(affine, t_aff)
in_data = apply_orientation(in_data, ornt)
bvals, bvecs = pr_hdr.get_bvals_bvecs()
if not opts.keep_trace: # discard Philips DTI trace if present
if bvecs is not None:
bad_mask = np.logical_and(bvals != 0, (bvecs == 0).all(axis=1))
if bad_mask.sum() > 0:
pl = "s" if bad_mask.sum() != 1 else ""
verbose("Removing %s DTI trace volume%s" % (bad_mask.sum(), pl))
good_mask = ~bad_mask
in_data = in_data[..., good_mask]
bvals = bvals[good_mask]
bvecs = bvecs[good_mask]
# Make corresponding NIfTI image
nimg = nifti1.Nifti1Image(in_data, affine, pr_hdr)
nhdr = nimg.header
nhdr.set_data_dtype(out_dtype)
nhdr.set_slope_inter(slope, intercept)
nhdr.set_sform(affine, code=1)
nhdr.set_qform(affine, code=1)
if "parse" in opts.minmax:
# need to get the scaled data
verbose("Loading (and scaling) the data to determine value range")
if opts.minmax[0] == "parse":
nhdr["cal_min"] = in_data.min() * slope + intercept
else:
nhdr["cal_min"] = float(opts.minmax[0])
if opts.minmax[1] == "parse":
nhdr["cal_max"] = in_data.max() * slope + intercept
else:
nhdr["cal_max"] = float(opts.minmax[1])
# container for potential NIfTI1 header extensions
if opts.store_header:
# dump the full PAR header content into an extension
with open(infile, "rb") as fobj: # contents must be bytes
hdr_dump = fobj.read()
dump_ext = nifti1.Nifti1Extension("comment", hdr_dump)
nhdr.extensions.append(dump_ext)
verbose("Writing %s" % outfilename)
nibabel.save(nimg, outfilename)
# write out bvals/bvecs if requested
if opts.bvs:
if bvals is None and bvecs is None:
verbose("No DTI volumes detected, bvals and bvecs not written")
elif bvecs is None:
verbose("DTI volumes detected, but no diffusion direction info was" "found. Writing .bvals file only.")
with open(basefilename + ".bvals", "w") as fid:
# np.savetxt could do this, but it's just a loop anyway
for val in bvals:
fid.write("%s " % val)
fid.write("\n")
else:
verbose("Writing .bvals and .bvecs files")
# Transform bvecs with reorientation affine
orig2new = npl.inv(t_aff)
bv_reorient = from_matvec(to_matvec(orig2new)[0], [0, 0, 0])
bvecs = apply_affine(bv_reorient, bvecs)
with open(basefilename + ".bvals", "w") as fid:
# np.savetxt could do this, but it's just a loop anyway
for val in bvals:
fid.write("%s " % val)
fid.write("\n")
with open(basefilename + ".bvecs", "w") as fid:
for row in bvecs.T:
for val in row:
fid.write("%s " % val)
fid.write("\n")
# export data labels varying along the 4th dimensions if requested
if opts.vol_info:
labels = pr_img.header.get_volume_labels()
if len(labels) > 0:
vol_keys = list(labels.keys())
with open(basefilename + ".ordering.csv", "w") as csvfile:
csvwriter = csv.writer(csvfile, delimiter=",")
csvwriter.writerow(vol_keys)
for vals in zip(*[labels[k] for k in vol_keys]):
csvwriter.writerow(vals)
# write out dwell time if requested
if opts.dwell_time:
try:
dwell_time = calculate_dwell_time(
pr_hdr.get_water_fat_shift(), pr_hdr.get_echo_train_length(), opts.field_strength
)
except MRIError:
verbose("No EPI factors, dwell time not written")
else:
verbose("Writing dwell time (%r sec) calculated assuming %sT " "magnet" % (dwell_time, opts.field_strength))
with open(basefilename + ".dwell_time", "w") as fid:
fid.write("%r\n" % dwell_time)
trans,
img_to.shape,
order=order)
return out_class(data, img_to.affine)
def gmean_norm(data):
in_data = data > np.mean(data) * 0.8
gmean = np.mean(data[in_data])
return data / gmean
if __name__ == '__main__':
np.set_printoptions(suppress=True, precision=4)
normal_fname = "Phantom_EPI_3mm_tra_SENSE_6_1.PAR"
normal_img = parrec.load(normal_fname)
normal_data = normal_img.get_data()
normal_normed = gmean_norm(normal_data)
print("RMS of standard image {:<44}: {}".format(
normal_fname,
np.sqrt(np.sum(normal_normed ** 2))))
for parfile in glob.glob("*.PAR"):
if parfile == normal_fname:
continue
funny_img = parrec.load(parfile)
fixed_img = resample_img2img(normal_img, funny_img)
fixed_data = fixed_img.get_data()
difference_data = normal_normed - gmean_norm(fixed_data)
print('RMS resliced {:<52} : {}'.format(
trans,
img_to.shape,
order=order)
return out_class(data, img_to.affine)
def gmean_norm(data):
in_data = data > np.mean(data) * 0.8
gmean = np.mean(data[in_data])
return data / gmean
if __name__ == '__main__':
np.set_printoptions(suppress=True, precision=4)
normal_fname = "Phantom_EPI_3mm_tra_SENSE_6_1.PAR"
normal_img = parrec.load(normal_fname)
normal_data = normal_img.get_data()
normal_normed = gmean_norm(normal_data)
print("RMS of standard image {:<44}: {}".format(
normal_fname, np.sqrt(np.sum(normal_normed**2))))
for parfile in glob.glob("*.PAR"):
if parfile == normal_fname:
continue
funny_img = parrec.load(parfile)
fixed_img = resample_img2img(normal_img, funny_img)
fixed_data = fixed_img.get_data()
difference_data = normal_normed - gmean_norm(fixed_data)
print('RMS resliced {:<52} : {}'.format(
parfile, np.sqrt(np.sum(difference_data**2))))
def loader(self, fname):
return parrec.load(fname)
