/usr/bin/mvpa-prep-fmri is in python-mvpa 0.4.8-3.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 | #!/usr/bin/python2.7
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
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#
# See COPYING file distributed along with the PyMVPA package for the
# copyright and license terms.
#
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"""Tiny tool to prepare a directory for a typical analysis of fMRI data with
PyMVPA. Tools from the FSL suite will be used for preprocessing. It takes a 4D
fMRI timeseries as input and performs the following steps:
- extract an example volume
- perform motion correction using the example volume as reference
- conservative skull-stripping and brain mask generation
- masking of the motion-corrected timeseries with the brain mask
All results will be stored either in the current directory, or in a
subdirectory with the subject ID (if specified)."""
import sys
import os
from subprocess import call
import numpy as N
from mvpa.misc.cmdline import parser, opt
from mvpa.base import verbose, externals, error
import mvpa
if __debug__:
from mvpa.base import debug
_EXFUNC_CONV_DICT = {'last' : lambda x: x-1,
'first': lambda x: 0,
'middle': lambda x: int(x/2)}
"""Dictionary to get exemplar volume given a literal string"""
def prepParser(parser):
# use module docstring for help output
parser.usage = "%s [OPTIONS] <fmri-data>\n\n" % sys.argv[0] + __doc__
parser.version = "%prog " + mvpa.__version__
parser.add_option(opt.verbose)
parser.add_option(opt.help)
parser.add_option("-s", "--subject-id",
action="store", type="string", dest="subj",
default=None,
help="Subject ID used as output path")
parser.add_option("-e", "--example-func-vol",
action="store", type="string", dest="exfunc",
default='middle',
help="Volume (numeric ID or 'last', 'first', 'middle') "
"to be used as an example functional image. "
"Default: 10")
parser.add_option("-m", "--mcflirt-options",
action="store", type="string", dest="mcflirt_opts",
default='',
help="Options for MCFLIRT. '-plots' is auto-added ")
parser.add_option("-p", "--mcflirt-plots",
action="store_true", dest="mcflirt_plots",
help="Create a .pdf with plots of motion parameters")
parser.add_option("-b", "--bet-options",
action="store", type="string", dest="bet_opts",
default='-f 0.3',
help="Options for BET. '-m' is auto-added. "
"Default: '-f 0.3' for a safe guess of the brain "
"outline")
def main():
"""
"""
prepParser(parser)
(options, infiles) = parser.parse_args()
# late import of pynifti to be able to get help output without a big
# external dep.
externals.exists('nifti', raiseException=True)
from nifti import NiftiImage
if len(infiles) > 1 or not len(infiles):
error("%s needs exactly one input fMRI image as argument. "
"Got %s" % (sys.argv[0], str(infiles)))
func_fname = infiles[0]
# compressed or uncompressed? decide by input image
# XXX maybe add override option
if func_fname.lower().endswith('nii.gz'):
nii_ext = '.nii.gz'
verbose(2, "Output files will be compressed NIfTI images")
else:
nii_ext = '.nii'
verbose(2, "Output files will be uncompressed NIfTI images")
# determine output path
if not options.subj is None:
opath = options.subj
else:
opath = os.path.curdir
if not os.path.exists(opath):
verbose(1, "Create output directory '%s'" % opath)
os.makedirs(opath)
else:
verbose(2, "Using output path '%s'" % opath)
verbose(2, "Load image file from '%s'" % func_fname)
func_nim = NiftiImage(func_fname, load=True)
# process exfunc option
exfunc = options.exfunc.lower()
timepoints = func_nim.timepoints
if exfunc in _EXFUNC_CONV_DICT.keys():
exfuncid = _EXFUNC_CONV_DICT[exfunc](timepoints)
else:
try:
exfuncid = int(exfunc)
except ValueError, e:
error("Failed to convert '%s' into numerical id of "
"volume." % (exfunc))
if exfuncid >= timepoints or exfuncid < 0:
error("Example functional volume id must be in the "
"range 0 .. %d. Got %d." % (timepoints-1, exfuncid))
verbose(2, "Extract volume %i as example volume" % exfuncid)
ef_nim = NiftiImage(func_nim.data[exfuncid], func_nim.header)
ef_nim.save(os.path.join(opath, 'example_func' + nii_ext))
# close input file -- will operate on motion-corrected one later on
del func_nim
mcflirt_call = \
' '.join(
['mcflirt',
'-in ' + func_fname,
'-out ' + os.path.join(opath, 'func_mc'),
'-reffile ' + os.path.join(opath, 'example_func'),
'-verbose 0',
'-plots',
options.mcflirt_opts]).strip()
verbose(2, "Perform motion correction ('%s')" % mcflirt_call)
# run MCFLIRT (silence stderr; 5 being some random file descriptor)
if call(mcflirt_call, shell=True, stderr=None):
error("MCFLIRT failed to perform the motion correction.")
if options.mcflirt_plots:
verbose(2, "Plot motion parameters estimates")
externals.exists('pylab', raiseException=True)
mc = McFlirtParams(os.path.join(opath, 'func_mc.par'))
for k, (title, fields, ylabel) in enumerate(
(('Translation', ('x', 'y', 'z'), 'mm'),
('Rotation', ('rot1', 'rot2', 'rot3'), 'radians'))):
P.subplot(211+k)
P.title(title)
P.plot([0, timepoints], [0, 0], '0.6')
for dim in fields:
P.plot(mc[dim], label=dim)
P.legend()
P.axis('tight')
P.ylabel(ylabel)
P.gcf().savefig(os.path.join(opath, 'func_mc.pdf'))
bet_call = \
' '.join(
['bet',
os.path.join(opath, 'example_func'),
os.path.join(opath, 'example_func_brain'),
'-m',
options.bet_opts]).strip()
verbose(2, "Determine brain mask in functional space ('%s')" % bet_call)
# run BET (silence stderr; 5 being some random file descriptor)
if call(bet_call, shell=True, stderr=None):
error("BET failed to perform the skull stripping.")
verbose(2, "Threshold image background using brain mask")
mask_nim = NiftiImage(os.path.join(opath, 'example_func_brain_mask'))
func_nim = NiftiImage(os.path.join(opath, 'func_mc'))
# special case: single slice mask
if len(mask_nim.extent) < 3:
func_nim.data[:, N.asarray([mask_nim.data]) == 0] = 0
else:
func_nim.data[:, mask_nim.data == 0] = 0
func_nim.save()
if __name__ == '__main__':
main()
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