/usr/bin/parrec2nii is in python-nibabel 2.0.2-2.
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"""PAR/REC to NIfTI converter
"""
from __future__ import division, print_function, absolute_import
from optparse import OptionParser, Option
import numpy as np
import numpy.linalg as npl
import sys
import os
import nibabel
import nibabel.parrec as pr
from nibabel.parrec import one_line
from nibabel.mriutils import calculate_dwell_time, MRIError
import nibabel.nifti1 as nifti1
from nibabel.filename_parser import splitext_addext
from nibabel.volumeutils import fname_ext_ul_case
from nibabel.orientations import (io_orientation, inv_ornt_aff,
apply_orientation)
from nibabel.affines import apply_affine, from_matvec, to_matvec
def get_opt_parser():
# use module docstring for help output
p = OptionParser(
usage="%s [OPTIONS] <PAR files>\n\n" % sys.argv[0] + __doc__,
version="%prog " + nibabel.__version__)
p.add_option(
Option("-v", "--verbose", action="store_true", dest="verbose",
default=False,
help="""Make some noise."""))
p.add_option(
Option("-o", "--output-dir", action="store", type="string",
dest="outdir", default=None,
help=one_line("""Destination directory for NIfTI files.
Default: current directory.""")))
p.add_option(
Option("-c", "--compressed", action="store_true",
dest="compressed", default=False,
help="Whether to write compressed NIfTI files or not."))
p.add_option(
Option("-p", "--permit-truncated", action="store_true",
dest="permit_truncated", default=False,
help=one_line(
"""Permit conversion of truncated recordings. Support for
this is experimental, and results *must* be checked
afterward for validity.""")))
p.add_option(
Option("-b", "--bvs", action="store_true", dest="bvs", default=False,
help=one_line(
"""Output bvals/bvecs files in addition to NIFTI
image.""")))
p.add_option(
Option("-d", "--dwell-time", action="store_true", default=False,
dest="dwell_time",
help=one_line(
"""Calculate the scan dwell time. If supplied, the magnetic
field strength should also be supplied using
--field-strength (default 3). The field strength must be
supplied because it is not encoded in the PAR/REC
format.""")))
p.add_option(
Option("--field-strength", action="store", type="float",
dest="field_strength",
help=one_line(
"""The magnetic field strength of the recording, only needed
for --dwell-time. The field strength must be supplied
because it is not encoded in the PAR/REC format.""")))
p.add_option(
Option("--origin", action="store", dest="origin", default="scanner",
help=one_line(
"""Reference point of the q-form transformation of the NIfTI
image. If 'scanner' the (0,0,0) coordinates will refer to
the scanner's iso center. If 'fov', this coordinate will be
the center of the recorded volume (field of view). Default:
'scanner'.""")))
p.add_option(
Option("--minmax", action="store", nargs=2, dest="minmax",
help=one_line(
"""Mininum and maximum settings to be stored in the NIfTI
header. If any of them is set to 'parse', the scaled data is
scanned for the actual minimum and maximum. To bypass this
potentially slow and memory intensive step (the data has to
be scaled and fully loaded into memory), fixed values can be
provided as space-separated pair, e.g. '5.4 120.4'. It is
possible to set a fixed minimum as scan for the actual
maximum (and vice versa). Default: 'parse parse'.""")))
p.set_defaults(minmax=('parse', 'parse'))
p.add_option(
Option("--store-header", action="store_true", dest="store_header",
default=False,
help=one_line(
"""If set, all information from the PAR header is stored in
an extension ofthe NIfTI file header. Default: off""")))
p.add_option(
Option("--scaling", action="store", dest="scaling", default='dv',
help=one_line(
"""Choose data scaling setting. The PAR header defines two
different data scaling settings: 'dv' (values displayed on
console) and 'fp' (floating point values). Either one can be
chosen, or scaling can be disabled completely ('off'). Note
that neither method will actually scale the data, but just
store the corresponding settings in the NIfTI header, unless
non-uniform scaling is used, in which case the data is
stored in the file in scaled form. Default: 'dv'""")))
p.add_option(
Option('--keep-trace', action="store_true", dest='keep_trace',
default=False,
help=one_line("""Do not discard the diagnostic Philips DTI
trace volume, if it exists in the data.""")))
p.add_option(
Option("--overwrite", action="store_true", dest="overwrite",
default=False,
help=one_line("""Overwrite file if it exists. Default:
False""")))
return p
def verbose(msg, indent=0):
if verbose.switch:
print("%s%s" % (' ' * indent, msg))
def error(msg, exit_code):
sys.stderr.write(msg + '\n')
sys.exit(exit_code)
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)
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 bvals 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)
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')
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')
# 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)
# done
def main():
parser = get_opt_parser()
(opts, infiles) = parser.parse_args()
verbose.switch = opts.verbose
if opts.origin not in ['scanner', 'fov']:
error("Unrecognized value for --origin: '%s'." % opts.origin, 1)
if opts.dwell_time and opts.field_strength is None:
error('Need --field-strength for dwell time calculation', 1)
# store any exceptions
errs = []
for infile in infiles:
verbose('Processing %s' % infile)
try:
proc_file(infile, opts)
except Exception as e:
errs.append('%s: %s' % (infile, e))
if len(errs):
error('Caught %i exceptions. Dump follows:\n\n %s'
% (len(errs), '\n'.join(errs)), 1)
else:
verbose('Done')
if __name__ == '__main__':
main()
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