def warp_tracks(input_tracks_filename, input_flirt_fmatrix, input_fa_filename, output_filename = None, input_ref = '/usr/share/fsl/data/standard/FMRIB58_FA_1mm.nii.gz'):
print 'Loading fa, flirt matrix ...'
img_fa = nib.load(input_fa_filename)
flirt_affine= np.loadtxt(input_flirt_fmatrix)
img_ref =nib.load(input_ref)
#create affine matrix from flirt
mat=flirt2aff(flirt_affine,img_fa,img_ref)
#read tracks
print 'Loading tracks ...'
tensor_tracks = load_tracks(input_tracks_filename)
#linear tranform for tractography
tracks_warped_linear = transform_tracks(tensor_tracks,mat)
if output_filename == None:
filename_save = input_tracks_filename.split('.')[0]+'_linear.dpy'
else:
filename_save = os.path.abspath(output_filename)
#save tracks_warped_linear
print 'Saving warped tracks into :', filename_save
dpr_linear = Dpy(filename_save, 'w')
dpr_linear.write_tracks(tracks_warped_linear)
dpr_linear.close()
return filename_save
def warp_tracks_linearly(flirt_filename,fa_filename, tracks_filename,linear_filename):
import nibabel as nib
from dipy.external.fsl import flirt2aff
fsl_ref = '/usr/share/fsl/data/standard/FMRIB58_FA_1mm.nii.gz'
img_fa = nib.load(fa_filename)
flirt_affine= np.loadtxt(flirt_filename)
img_ref =nib.load(fsl_ref)
#create affine matrix from flirt
mat=flirt2aff(flirt_affine,img_fa,img_ref)
#read tracks
tensor_tracks = load_whole_tract(tracks_filename)
#linear tranform for tractography
tracks_warped_linear = transform_tracks(tensor_tracks,mat)
#save tracks_warped_linear
dpr_linear = Dpy(linear_filename, 'w')
dpr_linear.write_tracks(tracks_warped_linear)
dpr_linear.close()
def warp_tracks_linearly(flirt_filename,fa_filename, tracks_filename,linear_filename):
fsl_ref = '/usr/share/fsl/data/standard/FMRIB58_FA_1mm.nii.gz'
img_fa = nib.load(fa_filename)
flirt_affine= np.loadtxt(flirt_filename)
img_ref =nib.load(fsl_ref)
#create affine matrix from flirt
mat=flirt2aff(flirt_affine,img_fa,img_ref)
#read tracks
dpr = Dpy(tracks_filename, 'r')
tensor_tracks = dpr.read_tracks()
dpr.close()
#linear tranform for tractography
tracks_warped_linear = transform_tracks(tensor_tracks,mat)
#save tracks_warped_linear
dpr_linear = Dpy(linear_filename, 'w')
dpr_linear.write_tracks(tracks_warped_linear)
dpr_linear.close()
def humans():
no_seeds=10**6
visualize = False
save_odfs = False
dirname = "data/"
for root, dirs, files in os.walk(dirname):
if root.endswith('101_32'):
base_dir = root+'/'
filename = 'raw'
base_filename = base_dir + filename
nii_filename = base_filename + 'bet.nii.gz'
bvec_filename = base_filename + '.bvec'
bval_filename = base_filename + '.bval'
flirt_mat = base_dir + 'DTI/flirt.mat'
fa_filename = base_dir + 'DTI/fa.nii.gz'
fsl_ref = '/usr/share/fsl/data/standard/FMRIB58_FA_1mm.nii.gz'
dpy_filename = base_dir + 'DTI/res_tracks_dti.dpy'
print bvec_filename
img = nib.load(nii_filename)
data = img.get_data()
affine = img.get_affine()
bvals = np.loadtxt(bval_filename)
gradients = np.loadtxt(bvec_filename).T # this is the unitary direction of the gradient
tensors = Tensor(data, bvals, gradients, thresh=50)
FA = tensors.fa()
FA = tensors.fa()
famask=FA>=.2
ds=DiffusionSpectrum(data,bvals,gradients,odf_sphere='symmetric642',mask=famask,half_sphere_grads=True,auto=True,save_odfs=save_odfs)
gq=GeneralizedQSampling(data,bvals,gradients,1.2,odf_sphere='symmetric642',mask=famask,squared=False,save_odfs=save_odfs)
ei=EquatorialInversion(data,bvals,gradients,odf_sphere='symmetric642',mask=famask,half_sphere_grads=True,auto=False,save_odfs=save_odfs,fast=True)
ei.radius=np.arange(0,5,0.4)
ei.gaussian_weight=0.05
ei.set_operator('laplacian')
ei.update()
ei.fit()
ds.PK[FA<.2]=np.zeros(5)
ei.PK[FA<.2]=np.zeros(5)
gq.PK[FA<.2]=np.zeros(5)
print 'create seeds'
x,y,z,g=ei.PK.shape
seeds=np.zeros((no_seeds,3))
sid=0
while sid<no_seeds:
rx=(x-1)*np.random.rand()
ry=(y-1)*np.random.rand()
rz=(z-1)*np.random.rand()
seed=np.ascontiguousarray(np.array([rx,ry,rz]),dtype=np.float64)
seeds[sid]=seed
sid+=1
euler = EuDX(a=FA, ind=tensors.ind(), seeds=seeds, a_low=.2)
dt_tracks = [track for track in euler]
euler2 = EuDX(a=ds.PK, ind=ds.IN, seeds=seeds, odf_vertices=ds.odf_vertices, a_low=.2)
ds_tracks = [track for track in euler2]
euler3 = EuDX(a=gq.PK, ind=gq.IN, seeds=seeds, odf_vertices=gq.odf_vertices, a_low=.2)
gq_tracks = [track for track in euler3]
euler4 = EuDX(a=ei.PK, ind=ei.IN, seeds=seeds, odf_vertices=ei.odf_vertices, a_low=.2)
ei_tracks = [track for track in euler4]
if visualize:
renderer = fvtk.ren()
fvtk.add(renderer, fvtk.line(tensor_tracks, fvtk.red, opacity=1.0))
fvtk.show(renderer)
print 'Load images to be used for registration'
img_fa =nib.load(fa_filename)
img_ref =nib.load(fsl_ref)
mat=flirt2aff(np.loadtxt(flirt_mat),img_fa,img_ref)
del img_fa
del img_ref
print 'transform the tracks'
dt_linear = transform_tracks(dt_tracks,mat)
ds_linear = transform_tracks(ds_tracks,mat)
gq_linear = transform_tracks(gq_tracks,mat)
ei_linear = transform_tracks(ei_tracks,mat)
print 'save tensor tracks'
dpy_filename = base_dir + 'DTI/dt_linear.dpy'
print dpy_filename
dpr_linear = Dpy(dpy_filename, 'w')
dpr_linear.write_tracks(dt_linear)
dpr_linear.close()
print 'save ei tracks'
dpy_filename = base_dir + 'DTI/ei_linear.dpy'
print dpy_filename
dpr_linear = Dpy(dpy_filename, 'w')
dpr_linear.write_tracks(ei_linear)
dpr_linear.close()
print 'save ds tracks'
dpy_filename = base_dir + 'DTI/ds_linear.dpy'
print dpy_filename
dpr_linear = Dpy(dpy_filename, 'w')
dpr_linear.write_tracks(ds_linear)
dpr_linear.close()
print 'save gq tracks'
dpy_filename = base_dir + 'DTI/gq_linear.dpy'
print dpy_filename
dpr_linear = Dpy(dpy_filename, 'w')
dpr_linear.write_tracks(gq_linear)
dpr_linear.close()
print 'save lengths'
pkl_filename = base_dir + 'DTI/dt_lengths.pkl'
save_pickle(pkl_filename,lengths(dt_linear))
pkl_filename = base_dir + 'DTI/ei_lengths.pkl'
save_pickle(pkl_filename,lengths(ei_linear))
pkl_filename = base_dir + 'DTI/gq_lengths.pkl'
save_pickle(pkl_filename,lengths(gq_linear))
pkl_filename = base_dir + 'DTI/ds_lengths.pkl'
save_pickle(pkl_filename,lengths(ds_linear))
if __name__=='__main__':
dname='/home/eg309/Data/orbital_phantoms/dwi_dir/subject1/'
fsl_ref = '/usr/share/fsl/data/standard/FMRIB58_FA_1mm.nii.gz'
img_ref =nib.load(fsl_ref)
ffa='data/subj_01/101_32/DTI/fa.nii.gz'
fmat='data/subj_01/101_32/DTI/flirt.mat'
img_fa =nib.load(ffa)
img_ref =nib.load(fsl_ref)
ref_shape=img_ref.get_data().shape
mat=flirt2aff(np.loadtxt(fmat),img_fa,img_ref)
ftracks_dti=dname+'dti_tracks.dpy'
ftracks_dsi=dname+'dsi_tracks.dpy'
ftracks_gqi=dname+'gqi_tracks.dpy'
ftracks_eit=dname+'eit_tracks.dpy'
ftracks=[ftracks_dti,ftracks_dsi,ftracks_gqi,ftracks_eit]
#load data
fraw=dname+'data.nii.gz'
fbval=dname+'bvals'
fbvec=dname+'bvecs'
img = nib.load(fraw)
data = img.get_data()
affine = img.get_affine()
