def space_time_realign(Images,TR=2,numslices=None,SliceTime='asc_alt_2',RefScan=None):
'''
4D simultaneous slice timing and spatial realignment. Adapted from
Alexis Roche's example script, and extend to be used for multiplex
imaging sequences
Inputs:
Images: list of images, input as a list of strings
numslices: for non-multiplex sequence, default to be the number of
slices in the image. For multiplex sequence, enter as a tuple,
such that the first element is the number of planes acquired in
parallel between each other, and the second element is the number
of slices of each parallel plane/slab
SliceTime:enter as a string to specify how the slices are ordered.
Choices are the following
1).'ascending': sequential ascending acquisition
2).'descending': sequential descending acquisition
3).'asc_alt_2': ascending interleaved, starting at first slice
4).'asc_alt_2_1': ascending interleaved, starting at the second
slice
5).'desc_alt_2': descending interleaved, starting at last slice
6).'asc_alt_siemens': ascending interleaved, starting at the first
slice if odd number of slices, or second slice if even number
of slices
7).'asc_alt_half': ascending interleaved by half the volume
8).'desc_alt_half': descending interleaved by half the volume
RefScan: reference volume for spatial realignment movement estimation
'''
# load images
runs = [load_image(run) for run in Images]
# parse data info
if numslices is None:
numslices = runs[0].shape[2]
numplanes = 1
elif isinstance(numslices,tuple):
numslices = numslices[0]
numplanes = numplanes[1]
# parse slice timing according to the input
slice_timing = getattr(timefuncs,SliceTime)(TR,numslices)
#repeat the slice timing for multiplex seqquence
slice_timing = np.tile(slice_timing,numplanes)
# Spatio-temporal realigner assuming interleaved ascending slice order
R = SpaceTimeRealign(runs, tr=TR, slice_times=slice_timing, slice_info=2,
affine_class='Rigid')
print('Slice times: %s' % slice_timing)
# Estimate motion within- and between-sessions
R.estimate(refscan=RefScan)
# Resample data on a regular space+time lattice using 4d interpolation
print('Saving results ...')
for i in range(len(runs)):
corr_run = R.resample(i)
fname = os.path.join(os.path.split(Images[i])[0],'ra' + os.path.split(Images[i])[1])
save_image(corr_run, fname)
print(fname)
def time_space_realign(run_fnames, TR, slice_times, slice_axis):
run_imgs = [load_image(run) for run in run_fnames]
# Spatio-temporal realigner
R = SpaceTimeRealign(run_imgs,
tr=TR,
slice_times=slice_times,
slice_info=(slice_axis, 1))
# Estimate motion within- and between-sessions
R.estimate(refscan=None)
# Save back out
for i, fname in enumerate(run_fnames):
corr_run = R.resample(i)
pth, name = os.path.split(fname)
processed_fname = os.path.join(pth, 'ra' + name)
save_image(corr_run, processed_fname)
def space_time_realign(Images,TR=2,numslices=None,SliceTime='asc_alt_2',RefScan=0,Prefix='ra'):
'''
4D simultaneous slice timing and spatial realignment. Adapted from
Alexis Roche's example script, and extend to be used for multiplex
imaging sequences
Inputs:
Images: list of images, input as a list of strings/paths to images
numslices: for non-multiplex sequence, default to be the number of
slices in the image. For multiplex sequence, enter as a tuple,
such that the first element is the number of planes acquired in
parallel between each other, and the second element is the number
of slices of each parallel plane/slab, i.e. (numplanes,numslices)
SliceTime:enter as a string to specify how the slices are ordered.
Choices are the following
1).'ascending': sequential ascending acquisition
2).'descending': sequential descending acquisition
3).'asc_alt_2': ascending interleaved, starting at first slice
4).'asc_alt_2_1': ascending interleaved, starting at the second
slice
5).'desc_alt_2': descending interleaved, starting at last slice
6).'asc_alt_siemens': ascending interleaved, starting at the first
slice if odd number of slices, or second slice if even number
of slices
7).'asc_alt_half': ascending interleaved by half the volume
8).'desc_alt_half': descending interleaved by half the volume
RefScan: reference volume for spatial realignment movement estimation.
Note that scan 0 is the first scan.
Prefix: prefix of the new corrected images. Default is 'ra'
Author: Alexis Roche, 2009.
Edward Cui, February 2014
'''
# Load images
runs = [load_image(run) for run in Images]
# Parse data info
if numslices is None:
numslices = runs[0].shape[2]
numplanes = 1
elif isinstance(numslices,tuple):
(numplanes,numslices) = numslices
else:
numplanes = 1
# Print image info
if numplanes>1:
print('Running multiplex: %s' % numplanes)
print('Number of slices: %s' % numslices)
# Parse slice timing according to the input
slice_timing = getattr(timefuncs,SliceTime)(numslices,TR)
# Repeat the slice timing for multiplex seqquence
slice_timing = np.tile(slice_timing,numplanes)
# Print slice timing info
print('Slice times: %s' % slice_timing)
# Spatio-temporal realigner
R = SpaceTimeRealign(runs, tr=TR, slice_times=slice_timing, slice_info=2)
# Estimate motion within- and between-sessions
print('Estimating motion ...')
R.estimate(refscan=RefScan)
# Resample data on a regular space+time lattice using 4d interpolation
fname=[None]*len(Images) # output images
mfname=[None]*len(Images) # output motion parameter files
print('Saving results ...')
for n in range(len(Images)):
# extract motion parameters
motionparams = np.array([np.concatenate((M.translation,M.rotation),axis=1) for M in R._transforms[n]])
# set motion parameter file name
mfname[n] = os.path.join(os.path.split(Images[n])[0], 'rp_a0001.txt')
# write the motion parameters to file
np.savetxt(mfname[n],motionparams,fmt='%10.7e',delimiter='\t')
# resample data
corr_run = R.resample(n)
# set image name
fname[n] = os.path.join(os.path.split(Images[n])[0], Prefix + os.path.split(Images[n])[1])
# save image
save_image(corr_run, fname[n])
print(fname[n])
return(fname,mfname)
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 assuming interleaved ascending slice order
R = SpaceTimeRealign(runs, tr=2.5, slice_times='asc_alt_2', slice_info=2)
# If you are not sure what the above is doing, you can alternatively
# declare slice times explicitly using the following equivalent code
"""
tr = 2.5
nslices = runs[0].shape[2]
slice_times = (tr / float(nslices)) *\
np.argsort(range(0, nslices, 2) + range(1, nslices, 2))
print('Slice times: %s' % slice_times)
R = SpaceTimeRealign(runs, tr=tr, slice_times=slice_times, slice_info=2)
"""
# Estimate motion within- and between-sessions
R.estimate(refscan=None)
# Resample data on a regular space+time lattice using 4d interpolation
# Save images
cwd = abspath(os.getcwd())
print('Saving results in: %s' % cwd)
for i in range(len(runs)):
corr_run = R.resample(i)
fname = 'ra' + psplit(runnames[i])[1]
save_image(corr_run, fname)
# 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 assuming interleaved ascending slice order
R = SpaceTimeRealign(runs, tr=2.5, slice_times='asc_alt_2', slice_info=2)
# If you are not sure what the above is doing, you can alternatively
# declare slice times explicitly using the following equivalent code
"""
tr = 2.5
nslices = runs[0].shape[2]
slice_times = (tr / float(nslices)) *\
np.argsort(range(0, nslices, 2) + range(1, nslices, 2))
print('Slice times: %s' % slice_times)
R = SpaceTimeRealign(runs, tr=tr, slice_times=slice_times, slice_info=2)
"""
# Estimate motion within- and between-sessions
R.estimate(refscan=None)
# Resample data on a regular space+time lattice using 4d interpolation
# Save images
cwd = abspath(os.getcwd())
print('Saving results in: %s' % cwd)
for i in range(len(runs)):
corr_run = R.resample(i)
fname = 'ra' + psplit(runnames[i])[1]
save_image(corr_run, fname)
