def resample_proxy(in_file, order=3, new_zooms=None, out_file=None):
"""
Performs regridding of an image to set isotropic voxel sizes using dipy.
"""
if out_file is None:
fname, fext = op.splitext(op.basename(in_file))
if fext == '.gz':
fname, fext2 = op.splitext(fname)
fext = fext2 + fext
out_file = op.abspath('./%s_reslice%s' % (fname, fext))
img = nb.load(in_file)
hdr = img.header.copy()
data = img.get_data().astype(np.float32)
affine = img.affine
im_zooms = hdr.get_zooms()[:3]
if new_zooms is None:
minzoom = np.array(im_zooms).min()
new_zooms = tuple(np.ones((3, )) * minzoom)
if np.all(im_zooms == new_zooms):
return in_file
data2, affine2 = resample(data, affine, im_zooms, new_zooms, order=order)
tmp_zooms = np.array(hdr.get_zooms())
tmp_zooms[:3] = new_zooms[0]
hdr.set_zooms(tuple(tmp_zooms))
hdr.set_data_shape(data2.shape)
hdr.set_xyzt_units('mm')
nb.Nifti1Image(data2.astype(hdr.get_data_dtype()), affine2,
hdr).to_filename(out_file)
return out_file, new_zooms
def resample( input_file, target_file, output_file, interpolation=0 ):
'''
Resample the input image to match the target image.
input_file
the input file path
target_file
the target file path
output_file
the output file path
'''
# load the input image
input_image = nibabel.load( input_file )
# load the target image
target_image = nibabel.load( target_file )
# configure the zooms
old_zooms = input_image.get_header().get_zooms()[:3]
new_zooms = target_image.get_header().get_zooms()[:3]
# .. and the affine
affine = input_image.get_affine()
# .. and header
header = input_image.get_header()
# resample the input to match the target
resampled_data, new_affine = resampler.resample( input_image.get_data(), affine, old_zooms, new_zooms, interpolation )
# save the resampled image
klass = input_image.__class__
nibabel.save( klass( resampled_data, new_affine, header ), output_file )
def resample_voxel_size(input_filename, output_filename=None):
#print("Loading data: %s" % input_filename)
img = nib.load(input_filename)
old_data = img.get_data()
old_affine = img.get_affine()
zooms=img.get_header().get_zooms()[:3]
print 'Old zooms:', zooms
new_zooms=(2.,2.,2.)
print 'New zoom', new_zooms
#print 'Resample data and affine ...'
data,affine=resample(old_data,old_affine,zooms,new_zooms)
if output_filename == None:
filename_save = input_filename.split('.')[0]+'_iso.nii.gz'
else:
filename_save = os.path.abspath(output_filename)
#print "Saving data after resapling to ", filename_save
data_img = nib.Nifti1Image(data=data, affine=affine)
nib.save(data_img, filename_save)
return filename_save
def resample_proxy(in_file, order=3, new_zooms=None, out_file=None):
"""
Performs regridding of an image to set isotropic voxel sizes using dipy.
"""
if out_file is None:
fname, fext = op.splitext(op.basename(in_file))
if fext == '.gz':
fname, fext2 = op.splitext(fname)
fext = fext2 + fext
out_file = op.abspath('./%s_reslice%s' % (fname, fext))
img = nb.load(in_file)
hdr = img.get_header().copy()
data = img.get_data().astype(np.float32)
affine = img.get_affine()
im_zooms = hdr.get_zooms()[:3]
if new_zooms is None:
minzoom = np.array(im_zooms).min()
new_zooms = tuple(np.ones((3,)) * minzoom)
if np.all(im_zooms == new_zooms):
return in_file
data2, affine2 = resample(data, affine, im_zooms, new_zooms, order=order)
tmp_zooms = np.array(hdr.get_zooms())
tmp_zooms[:3] = new_zooms[0]
hdr.set_zooms(tuple(tmp_zooms))
hdr.set_data_shape(data2.shape)
hdr.set_xyzt_units('mm')
nb.Nifti1Image(data2.astype(hdr.get_data_dtype()),
affine2, hdr).to_filename(out_file)
return out_file, new_zooms
def test_resample():
fimg, _, _ = get_data("small_25")
img = nib.load(fimg)
data = img.get_data()
affine = img.get_affine()
zooms = img.get_header().get_zooms()[:3]
# test that new zooms are correctly from the affine (check with 3D volume)
new_zooms = (1, 1.2, 2.1)
data2, affine2 = reslice(data[..., 0], affine, zooms, new_zooms, order=1,
mode='constant')
img2 = nib.Nifti1Image(data2, affine2)
new_zooms_confirmed = img2.get_header().get_zooms()[:3]
assert_almost_equal(new_zooms, new_zooms_confirmed)
# same with resample
new_zooms = (1, 1.2, 2.1)
data2, affine2 = resample(data[..., 0], affine, zooms, new_zooms, order=1,
mode='constant')
img2 = nib.Nifti1Image(data2, affine2)
new_zooms_confirmed = img2.get_header().get_zooms()[:3]
assert_almost_equal(new_zooms, new_zooms_confirmed)
# test that shape changes correctly for the first 3 dimensions (check 4D)
new_zooms = (1, 1, 1.)
data2, affine2 = reslice(data, affine, zooms, new_zooms, order=0,
mode='reflect')
assert_equal(2 * np.array(data.shape[:3]), data2.shape[:3])
assert_equal(data2.shape[-1], data.shape[-1])
# same with different interpolation order
new_zooms = (1, 1, 1.)
data3, affine2 = reslice(data, affine, zooms, new_zooms, order=5,
mode='reflect')
assert_equal(2 * np.array(data.shape[:3]), data3.shape[:3])
assert_equal(data3.shape[-1], data.shape[-1])
# test that the sigma will be reduced with interpolation
sigmas = estimate_sigma(data)
sigmas2 = estimate_sigma(data2)
sigmas3 = estimate_sigma(data3)
assert_(np.all(sigmas > sigmas2))
assert_(np.all(sigmas2 > sigmas3))
def downsampleImage(img):
'''Returns an image downsampled to its largest voxel dimension.
Inputs:
img = Nibabel Image object (Nifti1, Analyze,...)
Outputs:
data2 = Downsampled image data
affine2 = New affine matrix'''
d = img.get_data()
zooms = img.get_header().get_zooms(
)[:3] #only need 3 zooms, fourth one is 1 since this is the b-val dir
affine = img.get_affine()
nzooms = [np.max(zooms) for i in range(3)]
data2, affine2 = aniso2iso.resample(d, affine, zooms, nzooms)
return data2, affine2
def rescaling_isotropic_voxel(src_iso_dir, out_iso_dir, subj_name):
try:
src_iso_file = os.path.join(src_iso_dir, subj_name + par_ecc_suffix)
out_iso_file = os.path.join(out_iso_dir, subj_name + par_iso_suffix)
if src_iso_file is not None and out_iso_file is not None:
src_img = nib.load(src_iso_file)
src_data = src_img.get_data()
src_affine = src_img.get_affine()
src_iso_size = src_img.get_header().get_zooms()[:3]
out_iso_size = par_iso_voxel_size
data, affine = resample(src_data, src_affine, src_iso_size,out_iso_size)
data_img = nib.Nifti1Image(data, affine)
nib.save(data_img, out_iso_file)
except:
print "FAIL: isotropic rescaling - File: %s" % src_iso_file
exit
def makeIso(filename, outname, voxel_size=1.):
img = nib.load(filename)
data = img.get_data()
zooms = img.get_header().get_zooms()[:3]
affine = img.get_affine()
# reslice image into 1x1x1 iso voxel
new_zooms = (voxel_size, voxel_size, voxel_size)
data, affine = resample(data, affine, zooms, new_zooms)
img = nib.Nifti1Image(data, affine)
print data.shape
print img.get_header().get_zooms()
print "###"
nib.save(img, outname)
def rescaling_isotropic_voxel(src_iso_dir, out_iso_dir, subj_name):
try:
src_iso_file = os.path.join(src_iso_dir, subj_name + par_ecc_suffix)
out_iso_file = os.path.join(out_iso_dir, subj_name + par_iso_suffix)
if src_iso_file is not None and out_iso_file is not None:
src_img = nib.load(src_iso_file)
src_data = src_img.get_data()
src_affine = src_img.get_affine()
src_iso_size = src_img.get_header().get_zooms()[:3]
out_iso_size = par_iso_voxel_size
data, affine = resample(src_data, src_affine, src_iso_size,
out_iso_size)
data_img = nib.Nifti1Image(data, affine)
nib.save(data_img, out_iso_file)
except:
print "FAIL: isotropic rescaling - File: %s" % src_iso_file
exit
def white_matter_mask_wmparc(dir_src, dir_out, verbose=False):
fwmparc_src = pjoin(dir_src, 'wmparc.nii.gz')
fribbon_src = pjoin(dir_src, 'ribbon.nii.gz')
wmparc, affine = load_nifti(fwmparc_src)
ribbon, affine = load_nifti(fribbon_src)
mask = np.zeros_like(wmparc)
for label in ribbon_structures:
mask[ribbon == label] = 1
for label in wmparc_structures + wmparc_cc_structures:
mask[wmparc == label] = 1
for label in wmparc_del_structures + wmparc_del_structures2:
mask[wmparc == label] = 0
mask = mask.astype('f8')
mask2, affine2 = resample(mask, affine,
(0.7,) * 3, (par_dim_vox,) * 3, order=0)
wm_out = "wm_mask_%s_%s_%s.nii.gz" % (par_b_tag, par_dim_tag, par_wmp_tag)
save_nifti(pjoin(dir_out, wm_out), mask2, affine2)
def white_matter_mask_wmparc(dir_src, dir_out, verbose=False):
fwmparc_src = pjoin(dir_src, 'wmparc.nii.gz')
fribbon_src = pjoin(dir_src, 'ribbon.nii.gz')
wmparc, affine = load_nifti(fwmparc_src)
ribbon, affine = load_nifti(fribbon_src)
mask = np.zeros_like(wmparc)
for label in ribbon_structures:
mask[ribbon == label] = 1
for label in wmparc_structures + wmparc_cc_structures:
mask[wmparc == label] = 1
for label in wmparc_del_structures + wmparc_del_structures2:
mask[wmparc == label] = 0
mask = mask.astype('f8')
mask2, affine2 = resample(mask, affine,
(0.7,) * 3, (par_dim_vox,) * 3, order=0)
wm_out = "wm_mask_%s_%s_%s.nii.gz" % (par_b_tag, par_dim_tag, par_wmp_tag)
save_nifti(pjoin(dir_out, wm_out), mask2, affine2)
""" ``(4.0, 4.0, 5.0)`` Set the required new voxel size. """ new_zooms = (3., 3., 3.) new_zooms """ ``(3.0, 3.0, 3.0)`` Start resampling (reslicing). Trilinear interpolation is used by default. """ data2, affine2 = resample(data, affine, zooms, new_zooms) data2.shape """ ``(77, 77, 40)`` Save the result as a new Nifti file. """ img2 = nib.Nifti1Image(data2, affine2) nib.save(img2, 'iso_vox.nii.gz') """ Or as analyze format or any other supported format. """
gtab = gradient_table(bvals, bvecs, b0_threshold=10)
b0_index = np.where(gtab.b0s_mask == True)[0]
mask = nib.load(fmask).get_data()
print(data.shape)
print(affine)
print(nib.aff2axcodes(affine))
print('>>> Resample data to 1x1x1 mm^3...')
from dipy.align.aniso2iso import resample
data2, affine2 = resample(data, affine,
zooms=zooms,
new_zooms=(1., 1., 1.))
mask2, affine2 = resample(mask, affine,
zooms=zooms,
new_zooms=(1., 1., 1.))
mask2[mask2 > 0] = 1
# As these datasets are huge we will use ndindex to apply the mask
# rather than data2[mask2==0] = np.zeros(data2.shape[-1])
from dipy.core.ndindex import ndindex
for index in ndindex(data2.shape[:3]):
if mask2[index] == 0:
data2[index] = np.zeros(data2.shape[-1])
affine=img.get_affine() zooms=img.get_header().get_zooms()[:3] zooms """ (4.0, 4.0, 5.0) """ new_zooms=(3.,3.,3.) new_zooms """ (3.0, 3.0, 3.0) """ data2,affine2=resample(data,affine,zooms,new_zooms) data2.shape """ (77, 77, 40) Save the result as a nifti """ img2=nib.Nifti1Image(data2,affine2) nib.save(img2,'iso_vox.nii.gz') """ Or as analyze format """
assert(bvals.size==gradients.shape[0])
print("Loading bvals and bvec: %s" % bvals.size)
print("Loading nifti data: %s" % nii_bet_ecc_filename)
img = nib.load(nii_bet_ecc_filename)
old_data = img.get_data()
old_affine = img.get_affine()
from dipy.align.aniso2iso import resample
zooms=img.get_header().get_zooms()[:3]
print 'old zooms:', zooms
new_zooms=(2.,2.,2.)
print 'new zoom', new_zooms
data,affine=resample(old_data,old_affine,zooms,new_zooms)
print("Computing FA...")
tensors = Tensor(data, bvals, gradients, thresh=b0_threshold)
FA = tensors.fa()
print("FA:", FA.shape)
# print("Computing EuDX reconstruction.")
# euler = EuDX(a=FA, ind=tensors.ind(),
# odf_vertices=get_sphere('symmetric362').vertices,
# seeds=eudx_seeds, a_low=eudx_fa_stop)
# tensor_tracks_old = [track for track in euler]
# tensor_tracks = [track for track in tensor_tracks_old if track.shape[0]>1]
print("Saving results.")
# RISLICE WM MASK LIKE DSI
print '... resample WM mask'
origmask = os.path.join(main_dir,tp,'CMP/fs_output/HR__registered-TO-b0','fsmask_1mm.nii.gz')
command = 'mri_convert -rl "' + fimgfirst + '" -rt nearest "' + origmask + '" "' + fmask + '"'
#os.system(command)
# Prepare data for deconvolution
print('... prepare data for deconvolution')
data, affine, zooms, bvals, bvecs, mask = prepare_q4half_257vol(fimg, ftxt, fbval, fbvec, fmask, flipy)
# Create diffusion MR gradients
gtab = gradient_table(bvals, bvecs)
# Resample diffusion data and mask
print('... resample data')
data, affine = resample(data, affine, zooms, new_zooms)
mask, affine = resample(mask, affine, zooms, new_zooms, order=0)
# Deconvolution
t = time.time()
print('... perform deconvolution')
dsmodel = DiffusionSpectrumDeconvModel(gtab)
dsipeaks = peaks_from_model(model=dsmodel,
data=data,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask,
return_odf=True,
normalize_peaks=True)
#save_pickle(os.path.join(main_dir,tp,'CMP','scalars','dsideconv.pkl'), dsipeaks)
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 21 15:56:57 2015
@author: u0091609
"""
from dipy.align import aniso2iso
import glob
import nibabel as nib
import numpy as np
filelist=glob.glob('*_basic_Full_V1.nii')
temp_zooms=np.diag(nib.load('FVL_19657_1L_a_iE1_6_1_Brain_M4_FS.hdr').get_affine())
for f in filelist:
im=nib.load(f)
aff=im.get_affine()
zooms=np.diag(aff)
n_arr, n_aff=aniso2iso.resample(im.get_data(), aff, zooms[:3], temp_zooms[:3])
nif=nib.Nifti1Image(n_arr, n_aff)
nib.save(nif, f[:-4]+'HR.nii')
def process(self, brain):
new_pixeldim = tuple(self.factor * np.asarray(brain.infos['pixeldim']))
brain.image, brain.infos['affine'] = resample(brain.image, brain.infos['affine'],
brain.infos['pixeldim'], new_pixeldim,
order=self.order)
def test_resample():
fimg, _, _ = get_data("small_25")
img = nib.load(fimg)
data = img.get_data()
affine = img.get_affine()
zooms = img.get_header().get_zooms()[:3]
# test that new zooms are correctly from the affine (check with 3D volume)
new_zooms = (1, 1.2, 2.1)
data2, affine2 = reslice(data[..., 0],
affine,
zooms,
new_zooms,
order=1,
mode='constant')
img2 = nib.Nifti1Image(data2, affine2)
new_zooms_confirmed = img2.get_header().get_zooms()[:3]
assert_almost_equal(new_zooms, new_zooms_confirmed)
# same with resample
new_zooms = (1, 1.2, 2.1)
data2, affine2 = resample(data[..., 0],
affine,
zooms,
new_zooms,
order=1,
mode='constant')
img2 = nib.Nifti1Image(data2, affine2)
new_zooms_confirmed = img2.get_header().get_zooms()[:3]
assert_almost_equal(new_zooms, new_zooms_confirmed)
# test that shape changes correctly for the first 3 dimensions (check 4D)
new_zooms = (1, 1, 1.)
data2, affine2 = reslice(data,
affine,
zooms,
new_zooms,
order=0,
mode='reflect')
assert_equal(2 * np.array(data.shape[:3]), data2.shape[:3])
assert_equal(data2.shape[-1], data.shape[-1])
# same with different interpolation order
new_zooms = (1, 1, 1.)
data3, affine2 = reslice(data,
affine,
zooms,
new_zooms,
order=5,
mode='reflect')
assert_equal(2 * np.array(data.shape[:3]), data3.shape[:3])
assert_equal(data3.shape[-1], data.shape[-1])
# test that the sigma will be reduced with interpolation
sigmas = estimate_sigma(data)
sigmas2 = estimate_sigma(data2)
sigmas3 = estimate_sigma(data3)
assert_(np.all(sigmas > sigmas2))
assert_(np.all(sigmas2 > sigmas3))
dirname='/home/eg309/Data/project01_dsi/connectome_0001/tp1/RAWDATA/DSI'
data,affine,bvals,bvecs = read_mosaic_dir(dirname,globber='mr*',sort_func='instance number')
bvecs[np.isnan(bvecs)]=0
#project identical b-vectors to the other hemisphere
bvecs2,pairs=project_hemisph_bvecs(bvals,bvecs)
#stop
#get voxel size
zooms=np.sqrt(np.sum(affine[:3,:3]**2,axis=0))
nzooms=(zooms[0],zooms[0],zooms[0])
#resample datasets
print(data.shape)
data,affine=resample(data,affine,zooms,nzooms)
print(data.shape)
#mask a bit the background or load the mask
mask=data[:,:,:,0]>20
#img=nib.load('mask.nii.gz')
#mask.nii.gz')
#mask=img.get_data()>0
data_part=data[50:80,30:70,27,:]
dat=data_part[0:15,0:20].reshape(15,20,1,data_part.shape[-1])
np.save('/tmp/bvals.npz',bvals)
np.save('/tmp/bvecs.npy',bvecs)
np.save('/tmp/bvecs2.npy',bvecs2)
np.save('/tmp/data.npy',dat)
new_zooms = (2., 2., 2.)
# Flip data along axiAl direction
flipy = True
# Set triangulated sphere for ODF sampling ('symmetric362', 'symmetric642' or 'symmetric724')
sphere = get_sphere('symmetric724')
# Prepare data for deconvolution
print('... prepare data for deconvolution')
data, affine, zooms, bvals, bvecs, mask = prepare_q4half_257vol(fimg, ftxt, fbval, fbvec, fmask, flipy)
# Create diffusion MR gradients
gtab = gradient_table(bvals, bvecs)
# Resample diffusion data and mask
print('... resample data')
data_new, affine_new = resample(data, affine, zooms, new_zooms)
mask_new, affine_new = resample(mask, affine, zooms, new_zooms, order=0)
# Deconvolution
t = time.time()
print('... perform deconvolution')
dsmodel = DiffusionSpectrumDeconvModel(gtab)
dsipeaks = peaks_from_model(model=dsmodel,
data=data,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
mask=mask,
return_odf=True,
normalize_peaks=True)
