def register_FA_same_subj_diff_sessions(dname_grid,dname_shell):
print('create temporary directory')
tmp_dir='/tmp'
print('load dicom data')
data_gr,affine_gr,bvals_gr,gradients_gr=dp.load_dcm_dir(dname_grid)
data_sh,affine_sh,bvals_sh,gradients_sh=dp.load_dcm_dir(dname_shell)
print('save DWI reference as nifti')
tmp_grid=os.path.join(tmp_dir,os.path.basename(dname_grid)+'_ref.nii')
tmp_shell=os.path.join(tmp_dir,os.path.basename(dname_shell)+'_ref.nii')
ni.save(ni.Nifti1Image(data_gr[...,0],affine_gr),tmp_grid)
ni.save(ni.Nifti1Image(data_sh[...,0],affine_sh),tmp_shell)
print('prepare filenames for haircut (bet)')
tmp_grid_bet=os.path.join(os.path.dirname(tmp_grid),\
os.path.splitext(os.path.basename(dname_grid))[0]+\
'_ref_bet.nii.gz')
tmp_shell_bet=os.path.join(os.path.dirname(tmp_shell),\
os.path.splitext(os.path.basename(dname_shell))[0]+\
'_ref_bet.nii.gz')
print('bet is running')
haircut_dwi_reference(tmp_grid,tmp_grid_bet)
haircut_dwi_reference(tmp_shell,tmp_shell_bet)
print('load nii.gz reference (s0) volumes')
img_gr_bet=ni.load(tmp_grid_bet)
img_sh_bet=ni.load(tmp_shell_bet)
print('register the shell reference to the grid reference')
source=img_sh_bet
target=img_gr_bet
T=dp.volume_register(source,target,similarity,\
interp,subsampling,search,optimizer)
print('apply the inverse of the transformation matrix')
dp.volume_transform(source, T.inv(), reference=target)
#ni.save(sourceT,'/tmp/result.nii.gz')
print('calculate FA for grid and shell data')
FA_grid=dp.Tensor( data_gr,bvals_gr,gradients_gr,thresh=50).FA
FA_shell=dp.Tensor(data_sh,bvals_sh,gradients_sh,thresh=50).FA
print('create an FA nibabel image for shell')
FA_shell_img=ni.Nifti1Image(FA_shell,affine_sh)
print('transform FA_shell')
FA_shell_imgT=dp.volume_transform(FA_shell_img,T.inv(),reference=target)
return ni.Nifti1Image(FA_grid,affine_gr),FA_shell_imgT
def register_source_2_target(source_data,source_affine,target_data,target_affine):
#subsampling=target_data.shape[:3]
target=ni.Nifti1Image(target_data,target_affine)
source=ni.Nifti1Image(source_data,source_affine)
T=dp.volume_register(source,target,similarity,\
interp,subsampling,search,optimizer)
sourceT=dp.volume_transform(source, T.inv(), reference=target)
return sourceT
def test_registration():
# S012='/tmp/compare_12_with_32_Verio_directly/18620_0004.nii_S0.nii.gz'
# S032='/tmp/compare_12_with_32_Verio_directly/18620_0006.nii_S0.nii.gz'
S012T='/tmp/compare_12_with_32_Verio_directly/S0_reg.nii.gz'
MP='/tmp/compare_12_with_32_Verio_directly/MPRAGE.nii'
D114=resources.get_paths('DTI STEAM 114 Trio')[2]
data,affine,bvals,gradients=dp.load_dcm_dir(D114)
D114i=ni.Nifti1Image(data[...,0],affine)
D101=resources.get_paths('DSI STEAM 101 Trio')[2]
data,affine,bvals,gradients=dp.load_dcm_dir(D101)
D101i=ni.Nifti1Image(data[...,0],affine)
ni.save(D101i,'/tmp/compare_12_with_32_Verio_directly/S0_101_reg.nii.gz')
#source=ni.load(S012)
source=D114i
#target=D101i
#target=ni.load(S032)
target=ni.load(MP)
target._data=np.squeeze(target._data)
#target._affine= np.dot(np.diag([-1, -1, 1, 1]), target._affine)
similarity='cr'
interp = 'tri'
subsampling=None
search='affine'
optimizer= 'powell'
T=dp.volume_register(source,target,similarity,\
interp,subsampling,search,optimizer)
print('Transformation matrix')
print(T.inv())
sourceT=dp.volume_transform(source,T.inv(),reference=target,interp_order=0)
sourceTd=sourceT.get_data()
sourceTd[sourceTd<0]=0
sourceT._data=sourceTd
ni.save(sourceT,S012T)
sourced=source.get_data()
targetd=target.get_data()
sourceTd=sourceT.get_data()
print 'source info',sourced.min(), sourced.max()
print 'target info',targetd.min(), targetd.max()
print 'sourceT info',sourceTd.min(), sourceTd.max()
def eddy_current_correction(data,affine,target=None,target_affine=None):
result=[]
no_dirs=data.shape[-1]
if target==None and target_affine==None:
target=ni.Nifti1Image(data[:,:,:,0],affine)
else:
target=ni.Nifti1Image(target,target_affine)
for i in range(1,no_dirs):
source=ni.Nifti1Image(data[:,:,:,i],affine)
T=dp.volume_register(source,target,similarity,\
interp,subsampling,search,optimizer)
sourceT=dp.volume_transform(source, T.inv(), reference=target)
print i, sourceT.get_data().shape, sourceT.affine.shape
result.append(sourceT)
result.insert(0,target)
print 'no of images',len(result)
return ni.concat_images(result)
data[:,:,:,1]
source=ni.Nifti1Image(data[:,:,:,1],affine)
target=ni.Nifti1Image(data[:,:,:,0],affine)
#similarity 'cc', 'cr', 'crl1', 'mi', je', 'ce', 'nmi', 'smi'. 'cr'
similarity='cr'
#interp 'pv', 'tri'
interp = 'tri'
#subsampling None or sequence (3,)
subsampling=None
#search 'affine', 'rigid', 'similarity' or ['rigid','affine']
search='affine'
#optimizer 'simplex', 'powell', 'steepest', 'cg', 'bfgs' or
#sequence of optimizers
optimizer= 'powell'
T=dp.volume_register(source,target,similarity,\
interp,subsampling,search,)
sourceT=dp.volume_transform(source, T.inv(), reference=target)
s=source.get_data()
t=target.get_data()
sT=sourceT.get_data()
data[:, :, :, 1]
source = ni.Nifti1Image(data[:, :, :, 1], affine)
target = ni.Nifti1Image(data[:, :, :, 0], affine)
#similarity 'cc', 'cr', 'crl1', 'mi', je', 'ce', 'nmi', 'smi'. 'cr'
similarity = 'cr'
#interp 'pv', 'tri'
interp = 'tri'
#subsampling None or sequence (3,)
subsampling = None
#search 'affine', 'rigid', 'similarity' or ['rigid','affine']
search = 'affine'
#optimizer 'simplex', 'powell', 'steepest', 'cg', 'bfgs' or
#sequence of optimizers
optimizer = 'powell'
T=dp.volume_register(source,target,similarity,\
interp,subsampling,search,)
sourceT = dp.volume_transform(source, T.inv(), reference=target)
s = source.get_data()
t = target.get_data()
sT = sourceT.get_data()
