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#!/usr/bin/env python
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
This script requires the nipy-data package to run. It is an example of
simultaneous motion correction and slice timing correction in
multi-session fMRI data from the FIAC 2005 dataset. Specifically, it
uses the first two sessions of subject 'fiac0'.
Usage:
python space_time_realign [iterations]
where iterations is a positive integer (set to 1 by default). If
zero, no motion correction is performed and the data is only
corrected for slice timing. The larger iterations, the more accurate
motion correction.
Two images will be created in the working directory for the realigned
series:
rarun1.nii
rarun2.nii
Author: Alexis Roche, 2009.
"""
from nipy.neurospin.registration import FmriRealign4d
from nipy.io.imageformats import load as load_image, save as save_image
from nipy.utils import example_data
from os.path import join, split
import sys
# Optinal argument
iterations = 1
if len(sys.argv)>1:
iterations = int(sys.argv[1])
# Input images are provided with the nipy-data package
runnames = [example_data.get_filename('fiac','fiac0',run+'.nii.gz') \
for run in ('run1','run2')]
runs = [load_image(run) for run in runnames]
# Spatio-temporal realigner
R = FmriRealign4d(runs, tr=2.5, slice_order='ascending', interleaved=True)
# Correct motion within- and between-sessions
R.correct_motion(iterations=iterations)
# Resample data on a regular space+time lattice using 4d interpolation
corr_runs = R.resample()
# Save images
for i in range(len(runs)):
aux = split(runnames[i])
save_image(corr_runs[i], join('ra'+aux[1]))
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