def load_data(self,
dwi_filename='DWI.nii',
scheme_filename='DWI.scheme',
mask_filename=None,
b0_thr=0):
"""Load the diffusion signal and its corresponding acquisition scheme.
Parameters
----------
dwi_filename : string
The file name of the DWI data, relative to the subject folder (default : 'DWI.nii')
scheme_filename : string
The file name of the corresponding acquisition scheme (default : 'DWI.scheme')
mask_filename : string
The file name of the (optional) binary mask (default : None)
b0_thr : float
The threshold below which a b-value is considered a b0 (default : 0)
"""
# Loading data, acquisition scheme and mask (optional)
LOG('\n-> Loading data:')
tic = time.time()
print('\t* DWI signal')
if not isfile(pjoin(self.get_config('DATA_path'), dwi_filename)):
ERROR('DWI file not found')
self.set_config('dwi_filename', dwi_filename)
self.niiDWI = nibabel.load(
pjoin(self.get_config('DATA_path'), dwi_filename))
self.niiDWI_img = self.niiDWI.get_data().astype(np.float32)
hdr = self.niiDWI.header if nibabel.__version__ >= '2.0.0' else self.niiDWI.get_header(
)
self.set_config('dim', self.niiDWI_img.shape[:3])
self.set_config('pixdim', tuple(hdr.get_zooms()[:3]))
print('\t\t- dim = %d x %d x %d x %d' % self.niiDWI_img.shape)
print('\t\t- pixdim = %.3f x %.3f x %.3f' % self.get_config('pixdim'))
# Scale signal intensities (if necessary)
if (np.isfinite(hdr['scl_slope']) and np.isfinite(hdr['scl_inter'])
and hdr['scl_slope'] != 0
and (hdr['scl_slope'] != 1 or hdr['scl_inter'] != 0)):
print('\t\t- rescaling data ', end='')
self.niiDWI_img = self.niiDWI_img * hdr['scl_slope'] + hdr[
'scl_inter']
print('[OK]')
print('\t* Acquisition scheme')
if not isfile(pjoin(self.get_config('DATA_path'), scheme_filename)):
ERROR('SCHEME file not found')
self.set_config('scheme_filename', scheme_filename)
self.set_config('b0_thr', b0_thr)
self.scheme = amico.scheme.Scheme(
pjoin(self.get_config('DATA_path'), scheme_filename), b0_thr)
print('\t\t- %d samples, %d shells' %
(self.scheme.nS, len(self.scheme.shells)))
print('\t\t- %d @ b=0' % (self.scheme.b0_count), end=' ')
for i in range(len(self.scheme.shells)):
print(', %d @ b=%.1f' % (len(
self.scheme.shells[i]['idx']), self.scheme.shells[i]['b']),
end=' ')
print()
if self.scheme.nS != self.niiDWI_img.shape[3]:
ERROR('Scheme does not match with DWI data')
print('\t* Binary mask')
if mask_filename is not None:
if not isfile(pjoin(self.get_config('DATA_path'), mask_filename)):
ERROR('MASK file not found')
self.niiMASK = nibabel.load(
pjoin(self.get_config('DATA_path'), mask_filename))
self.niiMASK_img = self.niiMASK.get_data().astype(np.uint8)
niiMASK_hdr = self.niiMASK.header if nibabel.__version__ >= '2.0.0' else self.niiMASK.get_header(
)
print('\t\t- dim = %d x %d x %d' % self.niiMASK_img.shape[:3])
print('\t\t- pixdim = %.3f x %.3f x %.3f' %
niiMASK_hdr.get_zooms()[:3])
if self.niiMASK.ndim != 3:
ERROR('The provided MASK if 4D, but a 3D dataset is expected')
if self.get_config('dim') != self.niiMASK_img.shape[:3]:
ERROR('MASK geometry does not match with DWI data')
else:
self.niiMASK = None
self.niiMASK_img = np.ones(self.get_config('dim'))
print('\t\t- not specified')
print('\t\t- voxels = %d' % np.count_nonzero(self.niiMASK_img))
LOG(' [ %.1f seconds ]' % (time.time() - tic))
# Preprocessing
LOG('\n-> Preprocessing:')
tic = time.time()
if self.get_config('doDebiasSignal'):
print('\t* Debiasing signal... ', end='')
sys.stdout.flush()
if self.get_config('DWI-SNR') == None:
ERROR(
"Set noise variance for debiasing (eg. ae.set_config('RicianNoiseSigma', sigma))"
)
self.niiDWI_img = debiasRician(self.niiDWI_img,
self.get_config('DWI-SNR'),
self.niiMASK_img, self.scheme)
print(' [OK]')
if self.get_config('doNormalizeSignal'):
print('\t* Normalizing to b0... ', end='')
sys.stdout.flush()
if self.scheme.b0_count > 0:
self.mean_b0s = np.mean(self.niiDWI_img[:, :, :,
self.scheme.b0_idx],
axis=3)
else:
ERROR('No b0 volume to normalize signal with')
norm_factor = self.mean_b0s.copy()
idx = self.mean_b0s <= 0
norm_factor[idx] = 1
norm_factor = 1 / norm_factor
norm_factor[idx] = 0
for i in range(self.scheme.nS):
self.niiDWI_img[:, :, :, i] *= norm_factor
print('[ min=%.2f, mean=%.2f, max=%.2f ]' %
(self.niiDWI_img.min(), self.niiDWI_img.mean(),
self.niiDWI_img.max()))
if self.get_config('doMergeB0'):
print('\t* Merging multiple b0 volume(s)')
mean = np.expand_dims(np.mean(self.niiDWI_img[:, :, :,
self.scheme.b0_idx],
axis=3),
axis=3)
self.niiDWI_img = np.concatenate(
(mean, self.niiDWI_img[:, :, :, self.scheme.dwi_idx]), axis=3)
else:
print('\t* Keeping all b0 volume(s)')
if self.get_config('doDirectionalAverage'):
print(
'\t* Performing the directional average on the signal of each shell... '
)
numShells = len(self.scheme.shells)
dir_avg_img = self.niiDWI_img[:, :, :, :(numShells + 1)]
scheme_table = np.zeros([numShells + 1, 7])
id_bval = 0
dir_avg_img[:, :, :,
id_bval] = np.mean(self.niiDWI_img[:, :, :,
self.scheme.b0_idx],
axis=3)
scheme_table[id_bval, :] = np.array([1, 0, 0, 0, 0, 0, 0])
bvals = []
for shell in self.scheme.shells:
bvals.append(shell['b'])
sort_idx = np.argsort(bvals)
for shell_idx in sort_idx:
shell = self.scheme.shells[shell_idx]
id_bval = id_bval + 1
dir_avg_img[:, :, :,
id_bval] = np.mean(self.niiDWI_img[:, :, :,
shell['idx']],
axis=3)
scheme_table[id_bval, :] = np.array([
1, 0, 0, shell['G'], shell['Delta'], shell['delta'],
shell['TE']
])
self.niiDWI_img = dir_avg_img.astype(np.float32)
self.set_config('dim', self.niiDWI_img.shape[:3])
print('\t\t- dim = %d x %d x %d x %d' % self.niiDWI_img.shape)
print('\t\t- pixdim = %.3f x %.3f x %.3f' %
self.get_config('pixdim'))
print('\t* Acquisition scheme')
self.scheme = amico.scheme.Scheme(scheme_table, b0_thr)
print('\t\t- %d samples, %d shells' %
(self.scheme.nS, len(self.scheme.shells)))
print('\t\t- %d @ b=0' % (self.scheme.b0_count), end=' ')
for i in range(len(self.scheme.shells)):
print(', %d @ b=%.1f' % (len(
self.scheme.shells[i]['idx']), self.scheme.shells[i]['b']),
end=' ')
print()
if self.scheme.nS != self.niiDWI_img.shape[3]:
ERROR('Scheme does not match with DWI data')
LOG(' [ %.1f seconds ]' % (time.time() - tic))
def load_data( self, dwi_filename = 'DWI.nii',
scheme_filename = 'DWI.scheme', mask_filename = None, b0_thr = 0 ) :
"""Load the diffusion signal and its corresponding acquisition scheme.
Parameters
----------
dwi_filename : string
The file name of the DWI data, relative to the subject folder (default : 'DWI.nii')
scheme_filename : string
The file name of the corresponding acquisition scheme (default : 'DWI.scheme')
mask_filename : string
The file name of the (optional) binary mask (default : None)
b0_thr : float
The threshold below which a b-value is considered a b0 (default : 0)
"""
# Loading data, acquisition scheme and mask (optional)
tic = time.time()
print '\n-> Loading data:'
print '\t* DWI signal...'
self.set_config('dwi_filename', dwi_filename)
self.niiDWI = nibabel.load( pjoin( self.get_config('DATA_path'), dwi_filename) )
self.niiDWI_img = self.niiDWI.get_data().astype(np.float32)
hdr = self.niiDWI.header if nibabel.__version__ >= '2.0.0' else self.niiDWI.get_header()
self.set_config('dim', self.niiDWI_img.shape[:3])
self.set_config('pixdim', tuple( hdr.get_zooms()[:3] ))
print '\t\t- dim = %d x %d x %d x %d' % self.niiDWI_img.shape
print '\t\t- pixdim = %.3f x %.3f x %.3f' % self.get_config('pixdim')
# Scale signal intensities (if necessary)
if ( np.isfinite(hdr['scl_slope']) and np.isfinite(hdr['scl_inter']) and hdr['scl_slope'] != 0 and
( hdr['scl_slope'] != 1 or hdr['scl_inter'] != 0 ) ):
print '\t\t- rescaling data',
self.niiDWI_img = self.niiDWI_img * hdr['scl_slope'] + hdr['scl_inter']
print "[OK]"
print '\t* Acquisition scheme...'
self.set_config('scheme_filename', scheme_filename)
self.set_config('b0_thr', b0_thr)
self.scheme = amico.scheme.Scheme( pjoin( self.get_config('DATA_path'), scheme_filename), b0_thr )
print '\t\t- %d samples, %d shells' % ( self.scheme.nS, len(self.scheme.shells) )
print '\t\t- %d @ b=0' % ( self.scheme.b0_count ),
for i in xrange(len(self.scheme.shells)) :
print ', %d @ b=%.1f' % ( len(self.scheme.shells[i]['idx']), self.scheme.shells[i]['b'] ),
print
if self.scheme.nS != self.niiDWI_img.shape[3] :
raise ValueError( 'Scheme does not match with DWI data' )
print '\t* Binary mask...'
if mask_filename is not None :
self.niiMASK = nibabel.load( pjoin( self.get_config('DATA_path'), mask_filename) )
self.niiMASK_img = self.niiMASK.get_data().astype(np.uint8)
niiMASK_hdr = self.niiMASK.header if nibabel.__version__ >= '2.0.0' else self.niiMASK.get_header()
print '\t\t- dim = %d x %d x %d' % self.niiMASK_img.shape[:3]
print '\t\t- pixdim = %.3f x %.3f x %.3f' % niiMASK_hdr.get_zooms()[:3]
if self.get_config('dim') != self.niiMASK_img.shape[:3] :
raise ValueError( 'MASK geometry does not match with DWI data' )
else :
self.niiMASK = None
self.niiMASK_img = np.ones( self.get_config('dim') )
print '\t\t- not specified'
print '\t\t- voxels = %d' % np.count_nonzero(self.niiMASK_img)
# Preprocessing
print '\n-> Preprocessing:'
if self.get_config('doDebiasSignal') :
print '\t* Debiasing signal...\n',
sys.stdout.flush()
if self.get_config('DWI-SNR') == None:
raise ValueError( "Set noise variance for debiasing (eg. ae.set_config('RicianNoiseSigma', sigma))" )
self.niiDWI_img = debiasRician(self.niiDWI_img,self.get_config('DWI-SNR'),self.niiMASK_img,self.scheme)
if self.get_config('doNormalizeSignal') :
print '\t* Normalizing to b0...',
sys.stdout.flush()
if self.scheme.b0_count > 0 :
self.mean_b0s = np.mean( self.niiDWI_img[:,:,:,self.scheme.b0_idx], axis=3 )
else:
raise ValueError( 'No b0 volume to normalize signal with' )
norm_factor = self.mean_b0s.copy()
idx = self.mean_b0s <= 0
norm_factor[ idx ] = 1
norm_factor = 1 / norm_factor
norm_factor[ idx ] = 0
for i in xrange(self.scheme.nS) :
self.niiDWI_img[:,:,:,i] *= norm_factor
print '[ min=%.2f, mean=%.2f, max=%.2f ]' % ( self.niiDWI_img.min(), self.niiDWI_img.mean(), self.niiDWI_img.max() )
if self.get_config('doMergeB0') :
print '\t* Merging multiple b0 volume(s)...',
mean = np.expand_dims( np.mean( self.niiDWI_img[:,:,:,self.scheme.b0_idx], axis=3 ), axis=3 )
self.niiDWI_img = np.concatenate( (mean, self.niiDWI_img[:,:,:,self.scheme.dwi_idx]), axis=3 )
else :
print '\t* Keeping all b0 volume(s)...'
print ' [ %.1f seconds ]' % ( time.time() - tic )
def load_data( self, dwi_filename = 'DWI.nii',
scheme_filename = 'DWI.scheme', mask_filename = None, b0_thr = 0 ) :
"""Load the diffusion signal and its corresponding acquisition scheme.
Parameters
----------
dwi_filename : string
The file name of the DWI data, relative to the subject folder (default : 'DWI.nii')
scheme_filename : string
The file name of the corresponding acquisition scheme (default : 'DWI.scheme')
mask_filename : string
The file name of the (optional) binary mask (default : None)
b0_thr : float
The threshold below which a b-value is considered a b0 (default : 0)
"""
# Loading data, acquisition scheme and mask (optional)
tic = time.time()
print('\n-> Loading data:')
print('\t* DWI signal...')
self.set_config('dwi_filename', dwi_filename)
self.niiDWI = nibabel.load( pjoin( self.get_config('DATA_path'), dwi_filename) )
self.niiDWI_img = self.niiDWI.get_data().astype(np.float32)
hdr = self.niiDWI.header if nibabel.__version__ >= '2.0.0' else self.niiDWI.get_header()
self.set_config('dim', self.niiDWI_img.shape[:3])
self.set_config('pixdim', tuple( hdr.get_zooms()[:3] ))
print('\t\t- dim = %d x %d x %d x %d' % self.niiDWI_img.shape)
print('\t\t- pixdim = %.3f x %.3f x %.3f' % self.get_config('pixdim'))
# Scale signal intensities (if necessary)
if ( np.isfinite(hdr['scl_slope']) and np.isfinite(hdr['scl_inter']) and hdr['scl_slope'] != 0 and
( hdr['scl_slope'] != 1 or hdr['scl_inter'] != 0 ) ):
print('\t\t- rescaling data', end=' ')
self.niiDWI_img = self.niiDWI_img * hdr['scl_slope'] + hdr['scl_inter']
print("[OK]")
print('\t* Acquisition scheme...')
self.set_config('scheme_filename', scheme_filename)
self.set_config('b0_thr', b0_thr)
self.scheme = amico.scheme.Scheme( pjoin( self.get_config('DATA_path'), scheme_filename), b0_thr )
print('\t\t- %d samples, %d shells' % ( self.scheme.nS, len(self.scheme.shells) ))
print('\t\t- %d @ b=0' % ( self.scheme.b0_count ), end=' ')
for i in xrange(len(self.scheme.shells)) :
print(', %d @ b=%.1f' % ( len(self.scheme.shells[i]['idx']), self.scheme.shells[i]['b'] ), end=' ')
print()
if self.scheme.nS != self.niiDWI_img.shape[3] :
raise ValueError( 'Scheme does not match with DWI data' )
print('\t* Binary mask...')
if mask_filename is not None :
self.niiMASK = nibabel.load( pjoin( self.get_config('DATA_path'), mask_filename) )
self.niiMASK_img = self.niiMASK.get_data().astype(np.uint8)
niiMASK_hdr = self.niiMASK.header if nibabel.__version__ >= '2.0.0' else self.niiMASK.get_header()
print('\t\t- dim = %d x %d x %d' % self.niiMASK_img.shape[:3])
print('\t\t- pixdim = %.3f x %.3f x %.3f' % niiMASK_hdr.get_zooms()[:3])
if self.get_config('dim') != self.niiMASK_img.shape[:3] :
raise ValueError( 'MASK geometry does not match with DWI data' )
else :
self.niiMASK = None
self.niiMASK_img = np.ones( self.get_config('dim') )
print('\t\t- not specified')
print('\t\t- voxels = %d' % np.count_nonzero(self.niiMASK_img))
# Preprocessing
print('\n-> Preprocessing:')
if self.get_config('doDebiasSignal') :
print('\t* Debiasing signal...\n')
sys.stdout.flush()
if self.get_config('DWI-SNR') == None:
raise ValueError( "Set noise variance for debiasing (eg. ae.set_config('RicianNoiseSigma', sigma))" )
self.niiDWI_img = debiasRician(self.niiDWI_img,self.get_config('DWI-SNR'),self.niiMASK_img,self.scheme)
if self.get_config('doNormalizeSignal') :
print('\t* Normalizing to b0...', end=' ')
sys.stdout.flush()
if self.scheme.b0_count > 0 :
self.mean_b0s = np.mean( self.niiDWI_img[:,:,:,self.scheme.b0_idx], axis=3 )
else:
raise ValueError( 'No b0 volume to normalize signal with' )
norm_factor = self.mean_b0s.copy()
idx = self.mean_b0s <= 0
norm_factor[ idx ] = 1
norm_factor = 1 / norm_factor
norm_factor[ idx ] = 0
for i in xrange(self.scheme.nS) :
self.niiDWI_img[:,:,:,i] *= norm_factor
print('[ min=%.2f, mean=%.2f, max=%.2f ]' % ( self.niiDWI_img.min(), self.niiDWI_img.mean(), self.niiDWI_img.max() ))
if self.get_config('doMergeB0') :
print('\t* Merging multiple b0 volume(s)...', end=' ')
mean = np.expand_dims( np.mean( self.niiDWI_img[:,:,:,self.scheme.b0_idx], axis=3 ), axis=3 )
self.niiDWI_img = np.concatenate( (mean, self.niiDWI_img[:,:,:,self.scheme.dwi_idx]), axis=3 )
else :
print('\t* Keeping all b0 volume(s)...')
print(' [ %.1f seconds ]' % ( time.time() - tic ))