def load_kernels(self):
"""Load rotated kernels and project to the specific gradient scheme of this subject.
Dispatch to the proper function, depending on the model.
"""
if self.model is None:
ERROR('Model not set; call "set_model()" method first')
if self.scheme is None:
ERROR('Scheme not loaded; call "load_data()" first')
tic = time.time()
LOG('\n-> Resampling LUT for subject "%s":' %
self.get_config('subject'))
# auxiliary data structures
idx_OUT, Ylm_OUT = amico.lut.aux_structures_resample(
self.scheme, self.get_config('lmax'))
# hash table
self.htable = amico.lut.load_precomputed_hash_table(
self.get_config('ndirs'))
# Dispatch to the right handler for each model
self.KERNELS = self.model.resample(self.get_config('ATOMS_path'),
idx_OUT, Ylm_OUT,
self.get_config('doMergeB0'),
self.get_config('ndirs'))
LOG(' [ %.1f seconds ]' % (time.time() - tic))
def generate( self, out_path, aux, idx_in, idx_out, ndirs ) :
if self.scheme.version != 1 :
ERROR( 'This model requires a "VERSION: STEJSKALTANNER" scheme' )
scheme_high = amico.lut.create_high_resolution_scheme( self.scheme, b_scale=1E6 )
filename_scheme = pjoin( out_path, 'scheme.txt' )
np.savetxt( filename_scheme, scheme_high.raw, fmt='%15.8e', delimiter=' ', header='VERSION: STEJSKALTANNER', comments='' )
# temporary file where to store "datasynth" output
filename_signal = pjoin( tempfile._get_default_tempdir(), next(tempfile._get_candidate_names())+'.Bfloat' )
nATOMS = len(self.Rs) + len(self.d_perps) + len(self.d_isos)
progress = ProgressBar( n=nATOMS, prefix=" ", erase=False )
# Cylinder(s)
for R in self.Rs :
CMD = 'datasynth -synthmodel compartment 1 CYLINDERGPD %E 0 0 %E -schemefile %s -voxels 1 -outputfile %s 2> /dev/null' % ( self.d_par*1E-6, R, filename_scheme, filename_signal )
subprocess.call( CMD, shell=True )
if not exists( filename_signal ) :
ERROR( 'Problems generating the signal with "datasynth"' )
signal = np.fromfile( filename_signal, dtype='>f4' )
if exists( filename_signal ) :
remove( filename_signal )
lm = amico.lut.rotate_kernel( signal, aux, idx_in, idx_out, False, ndirs )
np.save( pjoin( out_path, 'A_%03d.npy'%progress.i ), lm )
progress.update()
# Zeppelin(s)
for d in self.d_perps :
CMD = 'datasynth -synthmodel compartment 1 ZEPPELIN %E 0 0 %E -schemefile %s -voxels 1 -outputfile %s 2> /dev/null' % ( self.d_par*1E-6, d*1e-6, filename_scheme, filename_signal )
subprocess.call( CMD, shell=True )
if not exists( filename_signal ) :
ERROR( 'Problems generating the signal with "datasynth"' )
signal = np.fromfile( filename_signal, dtype='>f4' )
if exists( filename_signal ) :
remove( filename_signal )
lm = amico.lut.rotate_kernel( signal, aux, idx_in, idx_out, False, ndirs )
np.save( pjoin( out_path, 'A_%03d.npy'%progress.i ), lm )
progress.update()
# Ball(s)
for d in self.d_isos :
CMD = 'datasynth -synthmodel compartment 1 BALL %E -schemefile %s -voxels 1 -outputfile %s 2> /dev/null' % ( d*1e-6, filename_scheme, filename_signal )
subprocess.call( CMD, shell=True )
if not exists( filename_signal ) :
ERROR( 'Problems generating the signal with "datasynth"' )
signal = np.fromfile( filename_signal, dtype='>f4' )
if exists( filename_signal ) :
remove( filename_signal )
lm = amico.lut.rotate_kernel( signal, aux, idx_in, idx_out, True, ndirs )
np.save( pjoin( out_path, 'A_%03d.npy'%progress.i ), lm )
progress.update()
def generate_kernels(self, regenerate=False, lmax=12, ndirs=32761):
"""Generate the high-resolution response functions for each compartment.
Dispatch to the proper function, depending on the model.
Parameters
----------
regenerate : boolean
Regenerate kernels if they already exist (default : False)
lmax : int
Maximum SH order to use for the rotation procedure (default : 12)
ndirs : int
Number of directions on the half of the sphere representing the possible orientations of the response functions (default : 32761)
"""
if self.scheme is None:
ERROR('Scheme not loaded; call "load_data()" first')
if self.model is None:
ERROR('Model not set; call "set_model()" method first')
if not is_valid(ndirs):
ERROR(
'Unsupported value for ndirs.\nNote: Supported values for ndirs are [500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 7000, 7500, 8000, 8500, 9000, 9500, 10000, 32761 (default)]'
)
# store some values for later use
self.set_config('lmax', lmax)
self.set_config('ndirs', ndirs)
self.model.scheme = self.scheme
LOG('\n-> Creating LUT for "%s" model:' % self.model.name)
# check if kernels were already generated
tmp = glob.glob(pjoin(self.get_config('ATOMS_path'), 'A_*.npy'))
if len(tmp) > 0 and not regenerate:
LOG(' [ LUT already computed. USe option "regenerate=True" to force regeneration ]'
)
return
# create folder or delete existing files (if any)
if not exists(self.get_config('ATOMS_path')):
makedirs(self.get_config('ATOMS_path'))
else:
for f in glob.glob(pjoin(self.get_config('ATOMS_path'), '*')):
remove(f)
# auxiliary data structures
aux = amico.lut.load_precomputed_rotation_matrices(lmax, ndirs)
idx_IN, idx_OUT = amico.lut.aux_structures_generate(self.scheme, lmax)
# Dispatch to the right handler for each model
tic = time.time()
self.model.generate(self.get_config('ATOMS_path'), aux, idx_IN,
idx_OUT, ndirs)
LOG(' [ %.1f seconds ]' % (time.time() - tic))
def fit( self, y, dirs, KERNELS, params, htable ) :
nD = dirs.shape[0]
if nD > 1 : # model works only with one direction
ERROR( '"%s" model requires exactly 1 orientation' % self.name )
n1 = len(self.d_perps)
n2 = len(self.d_isos)
nATOMS = n1+n2
if nATOMS == 0 : # empty dictionary
return [0, 0], None, None, None
# prepare DICTIONARY from dir and lookup tables
lut_idx = amico.lut.dir_TO_lut_idx( dirs[0], htable )
A = np.zeros( (len(y), nATOMS), dtype=np.float64, order='F' )
A[:,:(nD*n1)] = KERNELS['D'][:,lut_idx,:].T
A[:,(nD*n1):] = KERNELS['CSF'].T
# fit
x = spams.lasso( np.asfortranarray( y.reshape(-1,1) ), D=A, **params ).todense().A1
# return estimates
v = x[ :n1 ].sum() / ( x.sum() + 1e-16 )
# checking that there is more than 1 isotropic compartment
if self.type == 'Mouse' :
v_blood = x[ n1 ] / ( x.sum() + 1e-16 )
v_csf = x[ n1+1 ] / ( x.sum() + 1e-16 )
return [ v, 1-v, v_blood, v_csf ], dirs, x, A
else :
return [ v, 1-v ], dirs, x, A
def resample( self, in_path, idx_out, Ylm_out, doMergeB0, ndirs ) :
if doMergeB0:
nS = 1+self.scheme.dwi_count
merge_idx = np.hstack((self.scheme.b0_idx[0],self.scheme.dwi_idx))
else:
nS = self.scheme.nS
merge_idx = np.arange(nS)
KERNELS = {}
KERNELS['model'] = self.id
KERNELS['D'] = np.zeros( (len(self.d_perps),ndirs,nS), dtype=np.float32 )
KERNELS['CSF'] = np.zeros( (len(self.d_isos),nS), dtype=np.float32 )
nATOMS = len(self.d_perps) + len(self.d_isos)
progress = ProgressBar( n=nATOMS, prefix=" ", erase=False )
# Tensor compartment(s)
for i in range(len(self.d_perps)) :
lm = np.load( pjoin( in_path, 'A_%03d.npy'%progress.i ) )
if lm.shape[0] != ndirs:
ERROR( 'Outdated LUT. Call "generate_kernels( regenerate=True )" to update the LUT' )
KERNELS['D'][i,...] = amico.lut.resample_kernel( lm, self.scheme.nS, idx_out, Ylm_out, False, ndirs )[:,merge_idx]
progress.update()
# Isotropic compartment(s)
for i in range(len(self.d_isos)) :
lm = np.load( pjoin( in_path, 'A_%03d.npy'%progress.i ) )
KERNELS['CSF'][i,...] = amico.lut.resample_kernel( lm, self.scheme.nS, idx_out, Ylm_out, True, ndirs )[merge_idx]
progress.update()
return KERNELS
def get_table( self) :
"""Return a matrix from the structure.
The first three columns represent the gradient directions.
Then, we return two formats to describe each gradient:
- if the shape of data is Nx4, the 4^ column is the b-value;
- if the shape of data is Nx7, the last 4 columns are, respectively, the gradient strength, big delta, small delta and TE.
"""
if self.raw is None:
ERROR( 'The structure has not been created.' )
if self.version == 0 :
scheme_table = np.zeros([self.b0_count + self.dwi_count, 4])
for shell in self.shells:
scheme_table[shell['idx'], 0:3 ] = shell['grad']
scheme_table[shell['idx'], 3 ] = shell['b']
if self.version == 1 :
scheme_table = np.zeros([self.b0_count + self.dwi_count, 7])
for shell in self.shells:
scheme_table[shell['idx'], 0:3 ] = shell['grad']
scheme_table[shell['idx'], 3 ] = shell['G']
scheme_table[shell['idx'], 4 ] = shell['Delta']
scheme_table[shell['idx'], 5 ] = shell['delta']
scheme_table[shell['idx'], 6 ] = shell['TE']
return scheme_table
def load_precomputed_rotation_matrices(lmax, ndirs):
"""Load precomputed the rotation matrices to rotate the high-resolution kernels.
Parameters
----------
lmax : int
Maximum SH order to use for the rotation phase
ndirs : int
Number of directions on the half of the sphere representing the possible orientations of the response functions
"""
filename = pjoin(dipy_home,
f'AMICO_aux_matrices_lmax={lmax}_ndirs={ndirs}.pickle')
if not isfile(filename):
ERROR('Auxiliary matrices not found; call "amico.setup()" first')
with open(filename, 'rb') as rotation_matrices_file:
if sys.version_info.major == 3:
aux = pickle.load(rotation_matrices_file,
fix_imports=True,
encoding='bytes')
# Pickle files written by Python 2 are loaded with byte
# keys, whereas those written by Python 3 are loaded with
# str keys, even when both are written using protocol=2 as
# in precompute_rotation_matrices
result_aux = {(k.decode() if hasattr(k, "decode") else k): v
for k, v in aux.items()}
return result_aux
else:
return pickle.load(rotation_matrices_file)
def set_solver(self, **params):
"""Setup the specific parameters of the solver to fit the model.
Dispatch to the proper function, depending on the model; a model shoudl provide a "set_solver" function to set these parameters.
"""
if self.model is None:
ERROR('Model not set; call "set_model()" method first')
self.set_config('solver_params', self.model.set_solver(**params))
def __init__( self, data, b0_thr = 0 ) :
"""Initialize the acquisition scheme.
Parameters
----------
data : string or numpy.ndarray
The filename of the scheme or a matrix containing the actual values
b0_thr : float
The threshold on the b-values to identify the b0 images (default: 0)
"""
if type(data) is str :
# try loading from file
try :
n = 0 # headers lines to skip to get to the numeric data
with open(data) as fid :
for line in fid :
if re_match( r'[+-]?(\d+(\.\d*)?|\.\d+)([eE][+-]?\d+)?', line.strip() ) :
break
n += 1
tmp = np.loadtxt( data, skiprows=n )
except :
ERROR( 'Unable to open scheme file' )
self.load_from_table( tmp, b0_thr )
else :
# try loading from matrix
self.load_from_table( data, b0_thr )
def cyl_neuman_le_perp_PGSE( self, d, R, G, delta, smalldel, roots ):
# When R=0, no need to do any calculation
if (R == 0.00):
LE = np.zeros(G.shape) # np.size(R) = 1
return LE
else:
ERROR( '"cyl_neuman_le_perp_PGSE" not yet validated for non-zero values' )
def legendre_gaussian_integral( self, Lpmp, n ):
if n > 6:
ERROR( 'The maximum value for n is 6, which corresponds to the 12th order Legendre polynomial' )
exact = Lpmp>0.05
approx = Lpmp<=0.05
mn = n + 1
I = np.zeros((len(Lpmp),mn))
sqrtx = np.sqrt(Lpmp[exact])
I[exact,0] = np.sqrt(np.pi)*scipy.special.erf(sqrtx)/sqrtx
dx = 1.0/Lpmp[exact]
emx = -np.exp(-Lpmp[exact])
for i in range(1,mn):
I[exact,i] = emx + (i-0.5)*I[exact,i-1]
I[exact,i] = I[exact,i]*dx
# Computing the legendre gaussian integrals for large enough Lpmp
L = np.zeros((len(Lpmp),n+1))
for i in range(n+1):
if i == 0:
L[exact,0] = I[exact,0]
elif i == 1:
L[exact,1] = -0.5*I[exact,0] + 1.5*I[exact,1]
elif i == 2:
L[exact,2] = 0.375*I[exact,0] - 3.75*I[exact,1] + 4.375*I[exact,2]
elif i == 3:
L[exact,3] = -0.3125*I[exact,0] + 6.5625*I[exact,1] - 19.6875*I[exact,2] + 14.4375*I[exact,3]
elif i == 4:
L[exact,4] = 0.2734375*I[exact,0] - 9.84375*I[exact,1] + 54.140625*I[exact,2] - 93.84375*I[exact,3] + 50.2734375*I[exact,4]
elif i == 5:
L[exact,5] = -(63./256.)*I[exact,0] + (3465./256.)*I[exact,1] - (30030./256.)*I[exact,2] + (90090./256.)*I[exact,3] - (109395./256.)*I[exact,4] + (46189./256.)*I[exact,5]
elif i == 6:
L[exact,6] = (231./1024.)*I[exact,0] - (18018./1024.)*I[exact,1] + (225225./1024.)*I[exact,2] - (1021020./1024.)*I[exact,3] + (2078505./1024.)*I[exact,4] - (1939938./1024.)*I[exact,5] + (676039./1024.)*I[exact,6]
# Computing the legendre gaussian integrals for small Lpmp
x2=np.power(Lpmp[approx],2)
x3=x2*Lpmp[approx]
x4=x3*Lpmp[approx]
x5=x4*Lpmp[approx]
x6=x5*Lpmp[approx]
for i in range(n+1):
if i == 0:
L[approx,0] = 2 - 2*Lpmp[approx]/3 + x2/5 - x3/21 + x4/108
elif i == 1:
L[approx,1] = -4*Lpmp[approx]/15 + 4*x2/35 - 2*x3/63 + 2*x4/297
elif i == 2:
L[approx,2] = 8*x2/315 - 8*x3/693 + 4*x4/1287
elif i == 3:
L[approx,3] = -16*x3/9009 + 16*x4/19305
elif i == 4:
L[approx,4] = 32*x4/328185
elif i == 5:
L[approx,5] = -64*x5/14549535
elif i == 6:
L[approx,6] = 128*x6/760543875
return L
def synth_meas_iso_GPD( self, d, protocol ):
if protocol['pulseseq'] != 'PGSE' and protocol['pulseseq'] != 'STEAM':
ERROR( 'synth_meas_iso_GPD() : Protocol %s not translated from NODDI matlab code yet' % protocol['pulseseq'] )
GAMMA = 2.675987E8
modQ = GAMMA*protocol['smalldel'].transpose()*protocol['gradient_strength'].transpose()
modQ_Sq = np.power(modQ,2)
difftime = protocol['delta'].transpose()-protocol['smalldel']/3.0
return np.exp(-difftime*modQ_Sq*d)
def fit( self, y, dirs, KERNELS, params, htable ) :
singleb0 = True if len(y) == (1+self.scheme.dwi_count) else False
nD = dirs.shape[0]
if nD != 1 :
ERROR( '"%s" model requires exactly 1 orientation' % self.name )
# prepare DICTIONARY from dir and lookup tables
nWM = len(self.IC_ODs)*len(self.IC_VFs)
nATOMS = nWM + 1
if self.isExvivo == True :
nATOMS += 1
lut_idx = amico.lut.dir_TO_lut_idx( dirs[0], htable )
A = np.ones( (len(y), nATOMS), dtype=np.float64, order='F' )
A[:,:nWM] = KERNELS['wm'][:,lut_idx,:].T
A[:,-1] = KERNELS['iso']
# estimate CSF partial volume (and isotropic restriction, if exvivo) and remove from signal
x, _ = scipy.optimize.nnls( A, y )
yy = y - x[-1]*A[:,-1]
if self.isExvivo == True :
yy = yy - x[-2]*A[:,-2]
yy[ yy<0 ] = 0
# estimate IC and EC compartments and promote sparsity
if singleb0:
An = A[1:, :nWM] * KERNELS['norms']
yy = yy[1:].reshape(-1,1)
else:
An = A[ self.scheme.dwi_idx, :nWM ] * KERNELS['norms']
yy = yy[ self.scheme.dwi_idx ].reshape(-1,1)
x = spams.lasso( np.asfortranarray(yy), D=np.asfortranarray(An), **params ).todense().A1
# debias coefficients
x = np.append( x, 1 )
if self.isExvivo == True :
x = np.append( x, 1 )
idx = x>0
x[idx], _ = scipy.optimize.nnls( A[:,idx], y )
# return estimates
xx = x / ( x.sum() + 1e-16 )
xWM = xx[:nWM]
fISO = xx[-1]
xWM = xWM / ( xWM.sum() + 1e-16 )
f1 = np.dot( KERNELS['icvf'], xWM )
f2 = np.dot( (1.0-KERNELS['icvf']), xWM )
v = f1 / ( f1 + f2 + 1e-16 )
k = np.dot( KERNELS['kappa'], xWM )
od = 2.0/np.pi * np.arctan2(1.0,k)
if self.isExvivo:
return [v, od, fISO, xx[-2]], dirs, x, A
else:
return [v, od, fISO], dirs, x, A
def resample_kernel(KRlm, nS, idx_out, Ylm_out, is_isotropic, ndirs):
"""Project/resample a spherical function to signal space.
Parameters
----------
KRlm : numpy.array
Rotated spherical functions (in SH space) to project
nS : integer
Number of samples in the subject's acquisition scheme
idx_out : list of list
Index of samples in output kernel
Ylm_out : numpy.array
Matrix to project back all shells from SH space to signal space (of the subject)
is_isotropic : boolean
Indentifies whether Klm is an isotropic function or not
ndirs : int
Number of directions on the half of the sphere representing the possible orientations of the response functions
Returns
-------
KR = numpy.array
Rotated spherical functions projected to signal space of the subject
"""
if is_isotropic == False:
KR = np.ones((ndirs, nS), dtype=np.float32)
try:
for i in range(ndirs):
KR[i, idx_out] = np.dot(Ylm_out, KRlm[i, :]).astype(np.float32)
except:
ERROR(
'Outdated LUT. Call "generate_kernels( regenerate=True )" to update the LUT'
)
else:
KR = np.ones(nS, dtype=np.float32)
try:
for i in range(ndirs):
KR[idx_out] = np.dot(Ylm_out, KRlm).astype(np.float32)
except:
ERROR(
'Outdated LUT. Call "generate_kernels( regenerate=True )" to update the LUT'
)
return KR
def setup( lmax = 12, ndirs = 32761 ) :
"""General setup/initialization of the AMICO framework.
Parameters
----------
lmax : int
Maximum SH order to use for the rotation phase (default : 12)
ndirs : int
Number of directions on the half of the sphere representing the possible orientations of the response functions (default : 32761)
"""
if not is_valid(ndirs):
ERROR( 'Unsupported value for ndirs.\nNote: supported values for ndirs are [500, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 6500, 7000, 7500, 8000, 8500, 9000, 9500, 10000, 32761 (default)]' )
amico.lut.precompute_rotation_matrices( lmax, ndirs )
def set_model( self, model_name ) :
"""Set the model to use to describe the signal contributions in each voxel.
Parameters
----------
model_name : string
The name of the model (must match a class name in "amico.models" module)
"""
# Call the specific model constructor
if hasattr(amico.models, model_name ) :
self.model = getattr(amico.models,model_name)()
else :
ERROR( 'Model "%s" not recognized' % model_name )
self.set_config('ATOMS_path', pjoin( self.get_config('study_path'), 'kernels', self.model.id ))
# setup default parameters for fitting the model (can be changed later on)
self.set_solver()
def resample( self, in_path, idx_out, Ylm_out, doMergeB0, ndirs ):
nATOMS = len(self.IC_ODs)*len(self.IC_VFs) + 1
if doMergeB0:
nS = 1+self.scheme.dwi_count
merge_idx = np.hstack((self.scheme.b0_idx[0],self.scheme.dwi_idx))
else:
nS = self.scheme.nS
merge_idx = np.arange(nS)
KERNELS = {}
KERNELS['model'] = self.id
KERNELS['wm'] = np.zeros( (nATOMS-1,ndirs,nS), dtype=np.float32 )
KERNELS['iso'] = np.zeros( nS, dtype=np.float32 )
KERNELS['kappa'] = np.zeros( nATOMS-1, dtype=np.float32 )
KERNELS['icvf'] = np.zeros( nATOMS-1, dtype=np.float32 )
KERNELS['norms'] = np.zeros( (self.scheme.dwi_count, nATOMS-1) )
progress = ProgressBar( n=nATOMS, prefix=" ", erase=False )
# Coupled contributions
for i in range( len(self.IC_ODs) ):
for j in range( len(self.IC_VFs) ):
lm = np.load( pjoin( in_path, 'A_%03d.npy'%progress.i ) )
if lm.shape[0] != ndirs:
ERROR( 'Outdated LUT. Call "generate_kernels( regenerate=True )" to update the LUT' )
idx = progress.i - 1
KERNELS['wm'][idx,:,:] = amico.lut.resample_kernel( lm, self.scheme.nS, idx_out, Ylm_out, False, ndirs )[:,merge_idx]
KERNELS['kappa'][idx] = 1.0 / np.tan( self.IC_ODs[i]*np.pi/2.0 )
KERNELS['icvf'][idx] = self.IC_VFs[j]
if doMergeB0:
KERNELS['norms'][:,idx] = 1 / np.linalg.norm( KERNELS['wm'][idx,0,1:] ) # norm of coupled atoms (for l1 minimization)
else:
KERNELS['norms'][:,idx] = 1 / np.linalg.norm( KERNELS['wm'][idx,0,self.scheme.dwi_idx] ) # norm of coupled atoms (for l1 minimization)
progress.update()
# Isotropic
lm = np.load( pjoin( in_path, 'A_%03d.npy'%progress.i ) )
KERNELS['iso'] = amico.lut.resample_kernel( lm, self.scheme.nS, idx_out, Ylm_out, True, ndirs )[merge_idx]
progress.update()
return KERNELS
def save_results(self, path_suffix=None):
"""Save the output (directions, maps etc).
Parameters
----------
path_suffix : string
Text to be appended to the output path (default : None)
"""
if self.RESULTS is None:
ERROR('Model not fitted to the data; call "fit()" first')
if self.get_config('OUTPUT_path') is None:
RESULTS_path = pjoin('AMICO', self.model.id)
if path_suffix:
RESULTS_path = RESULTS_path + '_' + path_suffix
self.RESULTS['RESULTS_path'] = RESULTS_path
LOG('\n-> Saving output to "%s/*":' % RESULTS_path)
# delete previous output
RESULTS_path = pjoin(self.get_config('DATA_path'), RESULTS_path)
else:
RESULTS_path = self.get_config('OUTPUT_path')
if path_suffix:
RESULTS_path = RESULTS_path + '_' + path_suffix
self.RESULTS['RESULTS_path'] = RESULTS_path
LOG('\n-> Saving output to "%s/*":' % RESULTS_path)
if not exists(RESULTS_path):
makedirs(RESULTS_path)
else:
for f in glob.glob(pjoin(RESULTS_path, '*')):
remove(f)
# configuration
print('\t- configuration', end=' ')
with open(pjoin(RESULTS_path, 'config.pickle'), 'wb+') as fid:
pickle.dump(self.CONFIG, fid, protocol=2)
print(' [OK]')
# estimated orientations
print('\t- FIT_dir.nii.gz', end=' ')
niiMAP_img = self.RESULTS['DIRs']
affine = self.niiDWI.affine if nibabel.__version__ >= '2.0.0' else self.niiDWI.get_affine(
)
niiMAP = nibabel.Nifti1Image(niiMAP_img, affine)
niiMAP_hdr = niiMAP.header if nibabel.__version__ >= '2.0.0' else niiMAP.get_header(
)
niiMAP_hdr['cal_min'] = -1
niiMAP_hdr['cal_max'] = 1
niiMAP_hdr['scl_slope'] = 1
niiMAP_hdr['scl_inter'] = 0
nibabel.save(niiMAP, pjoin(RESULTS_path, 'FIT_dir.nii.gz'))
print(' [OK]')
# fitting error
if self.get_config('doComputeNRMSE'):
print('\t- FIT_nrmse.nii.gz', end=' ')
niiMAP_img = self.RESULTS['NRMSE']
niiMAP = nibabel.Nifti1Image(niiMAP_img, affine)
niiMAP_hdr = niiMAP.header if nibabel.__version__ >= '2.0.0' else niiMAP.get_header(
)
niiMAP_hdr['cal_min'] = 0
niiMAP_hdr['cal_max'] = 1
niiMAP_hdr['scl_slope'] = 1
niiMAP_hdr['scl_inter'] = 0
nibabel.save(niiMAP, pjoin(RESULTS_path, 'FIT_nrmse.nii.gz'))
print(' [OK]')
if self.get_config('doSaveCorrectedDWI'):
if self.model.name == 'Free-Water':
print('\t- dwi_fw_corrected.nii.gz', end=' ')
niiMAP_img = self.RESULTS['DWI_corrected']
niiMAP = nibabel.Nifti1Image(niiMAP_img, affine)
niiMAP_hdr = niiMAP.header if nibabel.__version__ >= '2.0.0' else niiMAP.get_header(
)
niiMAP_hdr['cal_min'] = 0
niiMAP_hdr['cal_max'] = 1
nibabel.save(niiMAP,
pjoin(RESULTS_path, 'dwi_fw_corrected.nii.gz'))
print(' [OK]')
else:
WARNING(
'"doSaveCorrectedDWI" option not supported for "%s" model'
% self.model.name)
# voxelwise maps
for i in range(len(self.model.maps_name)):
print('\t- FIT_%s.nii.gz' % self.model.maps_name[i], end=' ')
niiMAP_img = self.RESULTS['MAPs'][:, :, :, i]
niiMAP = nibabel.Nifti1Image(niiMAP_img, affine)
niiMAP_hdr = niiMAP.header if nibabel.__version__ >= '2.0.0' else niiMAP.get_header(
)
niiMAP_hdr['descrip'] = self.model.maps_descr[i]
niiMAP_hdr['cal_min'] = niiMAP_img.min()
niiMAP_hdr['cal_max'] = niiMAP_img.max()
niiMAP_hdr['scl_slope'] = 1
niiMAP_hdr['scl_inter'] = 0
nibabel.save(
niiMAP,
pjoin(RESULTS_path, 'FIT_%s.nii.gz' % self.model.maps_name[i]))
print(' [OK]')
LOG(' [ DONE ]')
def fit(self):
"""Fit the model to the data iterating over all voxels (in the mask) one after the other.
Call the appropriate fit() method of the actual model used.
"""
if self.niiDWI is None:
ERROR('Data not loaded; call "load_data()" first')
if self.model is None:
ERROR('Model not set; call "set_model()" first')
if self.KERNELS is None:
ERROR(
'Response functions not generated; call "generate_kernels()" and "load_kernels()" first'
)
if self.KERNELS['model'] != self.model.id:
ERROR('Response functions were not created with the same model')
self.set_config('fit_time', None)
totVoxels = np.count_nonzero(self.niiMASK_img)
LOG('\n-> Fitting "%s" model to %d voxels:' %
(self.model.name, totVoxels))
# setup fitting directions
peaks_filename = self.get_config('peaks_filename')
if peaks_filename is None:
DIRs = np.zeros([
self.get_config('dim')[0],
self.get_config('dim')[1],
self.get_config('dim')[2], 3
],
dtype=np.float32)
nDIR = 1
if self.get_config('doMergeB0'):
gtab = gradient_table(
np.hstack((0, self.scheme.b[self.scheme.dwi_idx])),
np.vstack((np.zeros(
(1, 3)), self.scheme.raw[self.scheme.dwi_idx, :3])))
else:
gtab = gradient_table(self.scheme.b, self.scheme.raw[:, :3])
DTI = dti.TensorModel(gtab)
else:
niiPEAKS = nibabel.load(
pjoin(self.get_config('DATA_path'), peaks_filename))
DIRs = niiPEAKS.get_data().astype(np.float32)
nDIR = np.floor(DIRs.shape[3] / 3)
print('\t* peaks dim = %d x %d x %d x %d' % DIRs.shape[:4])
if DIRs.shape[:3] != self.niiMASK_img.shape[:3]:
ERROR('PEAKS geometry does not match with DWI data')
# setup other output files
MAPs = np.zeros([
self.get_config('dim')[0],
self.get_config('dim')[1],
self.get_config('dim')[2],
len(self.model.maps_name)
],
dtype=np.float32)
if self.get_config('doComputeNRMSE'):
NRMSE = np.zeros([
self.get_config('dim')[0],
self.get_config('dim')[1],
self.get_config('dim')[2]
],
dtype=np.float32)
if self.get_config('doSaveCorrectedDWI'):
DWI_corrected = np.zeros(self.niiDWI.shape, dtype=np.float32)
# fit the model to the data
# =========================
t = time.time()
progress = ProgressBar(n=totVoxels, prefix=" ", erase=False)
for iz in range(self.niiMASK_img.shape[2]):
for iy in range(self.niiMASK_img.shape[1]):
for ix in range(self.niiMASK_img.shape[0]):
if self.niiMASK_img[ix, iy, iz] == 0:
continue
# prepare the signal
y = self.niiDWI_img[ix, iy, iz, :].astype(np.float64)
y[y < 0] = 0 # [NOTE] this should not happen!
# fitting directions
if peaks_filename is None:
dirs = DTI.fit(y).directions[0]
else:
dirs = DIRs[ix, iy, iz, :]
# dispatch to the right handler for each model
MAPs[ix, iy,
iz, :], DIRs[ix, iy, iz, :], x, A = self.model.fit(
y, dirs.reshape(-1, 3), self.KERNELS,
self.get_config('solver_params'), self.htable)
# compute fitting error
if self.get_config('doComputeNRMSE'):
y_est = np.dot(A, x)
den = np.sum(y**2)
NRMSE[ix, iy,
iz] = np.sqrt(np.sum(
(y - y_est)**2) / den) if den > 1e-16 else 0
if self.get_config('doSaveCorrectedDWI'):
if self.model.name == 'Free-Water':
n_iso = len(self.model.d_isos)
# keep only FW components of the estimate
x[0:x.shape[0] - n_iso] = 0
# y_fw_corrected below is the predicted signal by the anisotropic part (no iso part)
y_fw_part = np.dot(A, x)
# y is the original signal
y_fw_corrected = y - y_fw_part
y_fw_corrected[
y_fw_corrected <
0] = 0 # [NOTE] this should not happen!
if self.get_config('doNormalizeSignal'
) and self.scheme.b0_count > 0:
y_fw_corrected = y_fw_corrected * self.mean_b0s[
ix, iy, iz]
if self.get_config('doKeepb0Intact'
) and self.scheme.b0_count > 0:
# put original b0 data back in.
y_fw_corrected[self.scheme.b0_idx] = y[
self.scheme.b0_idx] * self.mean_b0s[ix, iy,
iz]
DWI_corrected[ix, iy, iz, :] = y_fw_corrected
progress.update()
self.set_config('fit_time', time.time() - t)
LOG(' [ %s ]' % (time.strftime(
"%Hh %Mm %Ss", time.gmtime(self.get_config('fit_time')))))
# store results
self.RESULTS = {}
self.RESULTS['DIRs'] = DIRs
self.RESULTS['MAPs'] = MAPs
if self.get_config('doComputeNRMSE'):
self.RESULTS['NRMSE'] = NRMSE
if self.get_config('doSaveCorrectedDWI'):
self.RESULTS['DWI_corrected'] = DWI_corrected
def set_solver( self ) :
ERROR( 'Not implemented' )
def fit(self):
"""Fit the model to the data iterating over all voxels (in the mask) one after the other.
Call the appropriate fit() method of the actual model used.
"""
@delayed
@wrap_non_picklable_objects
def fit_voxel(self, ix, iy, iz, dirs, DTI):
"""Perform the fit in a single voxel.
"""
# prepare the signal
y = self.niiDWI_img[ix, iy, iz, :].astype(np.float64)
y[y < 0] = 0 # [NOTE] this should not happen!
# fitting directions if not
if not self.get_config('doDirectionalAverage') and DTI is not None:
dirs = DTI.fit(y).directions[0].reshape(-1, 3)
# dispatch to the right handler for each model
results, dirs, x, A = self.model.fit(
y, dirs, self.KERNELS, self.get_config('solver_params'),
self.htable)
# compute fitting error
if self.get_config('doComputeNRMSE'):
y_est = np.dot(A, x)
den = np.sum(y**2)
NRMSE = np.sqrt(np.sum(
(y - y_est)**2) / den) if den > 1e-16 else 0
else:
NRMSE = 0.0
y_fw_corrected = None
if self.get_config('doSaveCorrectedDWI'):
if self.model.name == 'Free-Water':
n_iso = len(self.model.d_isos)
# keep only FW components of the estimate
x[0:x.shape[0] - n_iso] = 0
# y_fw_corrected below is the predicted signal by the anisotropic part (no iso part)
y_fw_part = np.dot(A, x)
# y is the original signal
y_fw_corrected = y - y_fw_part
y_fw_corrected[y_fw_corrected <
0] = 0 # [NOTE] this should not happen!
if self.get_config(
'doNormalizeSignal') and self.scheme.b0_count > 0:
y_fw_corrected = y_fw_corrected * self.mean_b0s[ix, iy,
iz]
if self.get_config(
'doKeepb0Intact') and self.scheme.b0_count > 0:
# put original b0 data back in.
y_fw_corrected[self.scheme.b0_idx] = y[
self.scheme.b0_idx] * self.mean_b0s[ix, iy, iz]
return results, dirs, NRMSE, y_fw_corrected
if self.niiDWI is None:
ERROR('Data not loaded; call "load_data()" first')
if self.model is None:
ERROR('Model not set; call "set_model()" first')
if self.KERNELS is None:
ERROR(
'Response functions not generated; call "generate_kernels()" and "load_kernels()" first'
)
if self.KERNELS['model'] != self.model.id:
ERROR('Response functions were not created with the same model')
n_jobs = self.get_config('parallel_jobs')
if n_jobs == -1:
n_jobs = cpu_count()
elif n_jobs == 0 or n_jobs < -1:
ERROR('Number of parallel jobs must be positive or -1')
self.set_config('fit_time', None)
totVoxels = np.count_nonzero(self.niiMASK_img)
LOG(f'\n-> Fitting "{self.model.name}" model to {totVoxels} voxels (using {n_jobs} job{"s" if n_jobs>1 else ""}):'
)
# setup fitting directions
peaks_filename = self.get_config('peaks_filename')
if peaks_filename is None:
DIRs = np.zeros([
self.get_config('dim')[0],
self.get_config('dim')[1],
self.get_config('dim')[2], 3
],
dtype=np.float32)
nDIR = 1
if self.get_config('doMergeB0'):
gtab = gradient_table(
np.hstack((0, self.scheme.b[self.scheme.dwi_idx])),
np.vstack((np.zeros(
(1, 3)), self.scheme.raw[self.scheme.dwi_idx, :3])))
else:
gtab = gradient_table(self.scheme.b, self.scheme.raw[:, :3])
DTI = dti.TensorModel(gtab)
else:
if not isfile(pjoin(self.get_config('DATA_path'), peaks_filename)):
ERROR('PEAKS file not found')
niiPEAKS = nibabel.load(
pjoin(self.get_config('DATA_path'), peaks_filename))
DIRs = niiPEAKS.get_data().astype(np.float32)
nDIR = np.floor(DIRs.shape[3] / 3)
PRINT('\t* peaks dim = %d x %d x %d x %d' % DIRs.shape[:4])
if DIRs.shape[:3] != self.niiMASK_img.shape[:3]:
ERROR('PEAKS geometry does not match with DWI data')
DTI = None
# setup other output files
MAPs = np.zeros([
self.get_config('dim')[0],
self.get_config('dim')[1],
self.get_config('dim')[2],
len(self.model.maps_name)
],
dtype=np.float32)
# fit the model to the data
# =========================
t = time.time()
ix, iy, iz = np.nonzero(self.niiMASK_img)
n_per_thread = np.floor(totVoxels / n_jobs)
idx = np.arange(0, totVoxels + 1, n_per_thread, dtype=np.int32)
idx[-1] = totVoxels
estimates = Parallel(n_jobs=n_jobs)(
fit_voxel(self, ix[i], iy[i], iz[i], DIRs[ix[i], iy[i],
iz[i], :], DTI)
for i in tqdm(range(totVoxels),
ncols=70,
bar_format=' |{bar}| {percentage:4.1f}%',
disable=(get_verbose() < 3)))
self.set_config('fit_time', time.time() - t)
LOG(' [ %s ]' % (time.strftime(
"%Hh %Mm %Ss", time.gmtime(self.get_config('fit_time')))))
# store results
self.RESULTS = {}
for i in range(totVoxels):
MAPs[ix[i], iy[i], iz[i], :] = estimates[i][0]
DIRs[ix[i], iy[i], iz[i], :] = estimates[i][1]
self.RESULTS['DIRs'] = DIRs
self.RESULTS['MAPs'] = MAPs
if self.get_config('doComputeNRMSE'):
NRMSE = np.zeros([
self.get_config('dim')[0],
self.get_config('dim')[1],
self.get_config('dim')[2]
],
dtype=np.float32)
for i in range(totVoxels):
NRMSE[ix[i], iy[i], iz[i]] = estimates[i][2]
self.RESULTS['NRMSE'] = NRMSE
if self.get_config('doSaveCorrectedDWI'):
DWI_corrected = np.zeros(self.niiDWI.shape, dtype=np.float32)
for i in range(totVoxels):
DWI_corrected[ix[i], iy[i], iz[i], :] = estimates[i][3]
self.RESULTS['DWI_corrected'] = DWI_corrected
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(
f'\t\t- {self.scheme.nS} samples, {len(self.scheme.shells)} shells'
)
PRINT(f'\t\t- {self.scheme.b0_count} @ b=0', end=' ')
for i in range(len(self.scheme.shells)):
PRINT(
f', {len(self.scheme.shells[i]["idx"])} @ b={self.scheme.shells[i]["b"]:.1f}',
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(f'\t\t- voxels = {np.count_nonzero(self.niiMASK_img)}')
LOG(f' [ {time.time() - tic:.1f} seconds ]')
# 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(
f'[ min={self.niiDWI_img.min():.2f}, mean={self.niiDWI_img.mean():.2f}, max={self.niiDWI_img.max():.2f} ]'
)
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(
f'\t\t- {self.scheme.nS} samples, {len(self.scheme.shells)} shells'
)
PRINT(f'\t\t- {self.scheme.b0_count} @ b=0', end=' ')
for i in range(len(self.scheme.shells)):
PRINT(
f', {len(self.scheme.shells[i]["idx"])} @ b={self.scheme.shells[i]["b"]:.1f}',
end=' ')
PRINT()
if self.scheme.nS != self.niiDWI_img.shape[3]:
ERROR('Scheme does not match with DWI data')
LOG(f' [ {time.time() - tic:.1f} seconds ]')
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')
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 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:
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)')
LOG(' [ %.1f seconds ]' % (time.time() - tic))
def save_results(self, path_suffix=None, save_dir_avg=False):
"""Save the output (directions, maps etc).
Parameters
----------
path_suffix : string
Text to be appended to the output path (default : None)
save_dir_avg : boolean
If true and the option doDirectionalAverage is true
the directional average signal and the scheme
will be saved in files (default : False)
"""
if self.RESULTS is None:
ERROR('Model not fitted to the data; call "fit()" first')
if self.get_config('OUTPUT_path') is None:
RESULTS_path = pjoin('AMICO', self.model.id)
if path_suffix:
RESULTS_path = RESULTS_path + '_' + path_suffix
self.RESULTS['RESULTS_path'] = RESULTS_path
LOG('\n-> Saving output to "%s/*":' % RESULTS_path)
# delete previous output
RESULTS_path = pjoin(self.get_config('DATA_path'), RESULTS_path)
else:
RESULTS_path = self.get_config('OUTPUT_path')
if path_suffix:
RESULTS_path = RESULTS_path + '_' + path_suffix
self.RESULTS['RESULTS_path'] = RESULTS_path
LOG('\n-> Saving output to "%s/*":' % RESULTS_path)
if not exists(RESULTS_path):
makedirs(RESULTS_path)
else:
for f in glob.glob(pjoin(RESULTS_path, '*')):
remove(f)
# configuration
print('\t- configuration', end=' ')
with open(pjoin(RESULTS_path, 'config.pickle'), 'wb+') as fid:
pickle.dump(self.CONFIG, fid, protocol=2)
print(' [OK]')
affine = self.niiDWI.affine if nibabel.__version__ >= '2.0.0' else self.niiDWI.get_affine(
)
hdr = self.niiDWI.header if nibabel.__version__ >= '2.0.0' else self.niiDWI.get_header(
)
hdr['datatype'] = 16
hdr['bitpix'] = 32
# estimated orientations
if not self.get_config('doDirectionalAverage'):
print('\t- FIT_dir.nii.gz', end=' ')
niiMAP_img = self.RESULTS['DIRs']
niiMAP = nibabel.Nifti1Image(niiMAP_img, affine, hdr)
niiMAP_hdr = niiMAP.header if nibabel.__version__ >= '2.0.0' else niiMAP.get_header(
)
niiMAP_hdr['cal_min'] = -1
niiMAP_hdr['cal_max'] = 1
niiMAP_hdr['scl_slope'] = 1
niiMAP_hdr['scl_inter'] = 0
nibabel.save(niiMAP, pjoin(RESULTS_path, 'FIT_dir.nii.gz'))
print(' [OK]')
# fitting error
if self.get_config('doComputeNRMSE'):
print('\t- FIT_nrmse.nii.gz', end=' ')
niiMAP_img = self.RESULTS['NRMSE']
niiMAP = nibabel.Nifti1Image(niiMAP_img, affine, hdr)
niiMAP_hdr = niiMAP.header if nibabel.__version__ >= '2.0.0' else niiMAP.get_header(
)
niiMAP_hdr['cal_min'] = 0
niiMAP_hdr['cal_max'] = 1
niiMAP_hdr['scl_slope'] = 1
niiMAP_hdr['scl_inter'] = 0
nibabel.save(niiMAP, pjoin(RESULTS_path, 'FIT_nrmse.nii.gz'))
print(' [OK]')
if self.get_config('doSaveCorrectedDWI'):
if self.model.name == 'Free-Water':
print('\t- dwi_fw_corrected.nii.gz', end=' ')
niiMAP_img = self.RESULTS['DWI_corrected']
niiMAP = nibabel.Nifti1Image(niiMAP_img, affine, hdr)
niiMAP_hdr = niiMAP.header if nibabel.__version__ >= '2.0.0' else niiMAP.get_header(
)
niiMAP_hdr['cal_min'] = 0
niiMAP_hdr['cal_max'] = 1
nibabel.save(niiMAP,
pjoin(RESULTS_path, 'dwi_fw_corrected.nii.gz'))
print(' [OK]')
else:
WARNING(
'"doSaveCorrectedDWI" option not supported for "%s" model'
% self.model.name)
# voxelwise maps
for i in range(len(self.model.maps_name)):
print('\t- FIT_%s.nii.gz' % self.model.maps_name[i], end=' ')
niiMAP_img = self.RESULTS['MAPs'][:, :, :, i]
niiMAP = nibabel.Nifti1Image(niiMAP_img, affine, hdr)
niiMAP_hdr = niiMAP.header if nibabel.__version__ >= '2.0.0' else niiMAP.get_header(
)
niiMAP_hdr['descrip'] = self.model.maps_descr[
i] + ' (AMICO v%s)' % self.get_config('version')
niiMAP_hdr['cal_min'] = niiMAP_img.min()
niiMAP_hdr['cal_max'] = niiMAP_img.max()
niiMAP_hdr['scl_slope'] = 1
niiMAP_hdr['scl_inter'] = 0
nibabel.save(
niiMAP,
pjoin(RESULTS_path, 'FIT_%s.nii.gz' % self.model.maps_name[i]))
print(' [OK]')
# Directional average signal
if save_dir_avg:
if self.get_config('doDirectionalAverage'):
print('\t- dir_avg_signal.nii.gz', end=' ')
niiMAP = nibabel.Nifti1Image(self.niiDWI_img, affine, hdr)
niiMAP_hdr = niiMAP.header if nibabel.__version__ >= '2.0.0' else niiMAP.get_header(
)
niiMAP_hdr[
'descrip'] = 'Directional average signal of each shell' + ' (AMICO v%s)' % self.get_config(
'version')
nibabel.save(niiMAP,
pjoin(RESULTS_path, 'dir_avg_signal.nii.gz'))
print(' [OK]')
print('\t- dir_avg.scheme', end=' ')
np.savetxt(pjoin(RESULTS_path, 'dir_avg.scheme'),
self.scheme.get_table(),
fmt="%.06f",
delimiter="\t",
header="VERSION: {}".format(self.scheme.version),
comments='')
print(' [OK]')
else:
WARNING(
'The directional average signal was not created (The option doDirectionalAverage is False).'
)
LOG(' [ DONE ]')
def fit( self, y, dirs, KERNELS, params ) :
ERROR( 'Not implemented' )
def load_from_table( self, data, b0_thr = 0 ) :
"""Build the structure from an input matrix.
The first three columns represent the gradient directions.
Then, we accept two formats to describe each gradient:
- if the shape of data is Nx4, the 4^ column is the b-value;
- if the shape of data is Nx7, the last 4 columns are, respectively, the gradient strength, big delta, small delta and TE.
Parameters
----------
data : numpy.ndarray
Matrix containing tall the values.
b0_thr : float
The threshold on the b-values to identify the b0 images (default: 0)
"""
if data.ndim == 1 :
data = np.expand_dims( data, axis=0 )
self.raw = data
# number of samples
# self.nS = self.raw.shape[0] JL: incomplete getter/setter incompatible with 3.6; this is never used any as getter always returns derived value
# set/calculate the b-values
if self.raw.shape[1] == 4 :
self.version = 0
self.b = self.raw[:,3]
elif self.raw.shape[1] == 7 :
self.version = 1
self.b = ( 267.513e6 * self.raw[:,3] * self.raw[:,5] )**2 * (self.raw[:,4] - self.raw[:,5]/3.0) * 1e-6 # in mm^2/s
else :
ERROR( 'Unrecognized scheme format' )
# store information about the volumes
self.b0_thr = b0_thr
self.b0_idx = np.where( self.b <= b0_thr )[0]
self.b0_count = len( self.b0_idx )
self.dwi_idx = np.where( self.b > b0_thr )[0]
self.dwi_count = len( self.dwi_idx )
# ensure the directions are in the spherical range [0,180]x[0,180]
idx = np.where( self.raw[:,1] < 0 )[0]
self.raw[idx,0:3] = -self.raw[idx,0:3]
# store information about each shell in a dictionary
self.shells = []
tmp = np.ascontiguousarray( self.raw[:,3:] )
schemeUnique, schemeUniqueInd = np.unique( tmp.view([('', tmp.dtype)]*tmp.shape[1]), return_index=True )
schemeUnique = schemeUnique.view(tmp.dtype).reshape((schemeUnique.shape[0], tmp.shape[1]))
schemeUnique = [tmp[index] for index in sorted(schemeUniqueInd)]
bUnique = [self.b[index] for index in sorted(schemeUniqueInd)]
for i in range(len(schemeUnique)) :
if bUnique[i] <= b0_thr :
continue
shell = {}
shell['b'] = bUnique[i]
if self.version == 0 :
shell['G'] = None
shell['Delta'] = None
shell['delta'] = None
shell['TE'] = None
else :
shell['G'] = schemeUnique[i][0]
shell['Delta'] = schemeUnique[i][1]
shell['delta'] = schemeUnique[i][2]
shell['TE'] = schemeUnique[i][3]
shell['idx'] = np.where((tmp == schemeUnique[i]).all(axis=1))[0]
shell['grad'] = self.raw[shell['idx'],0:3]
self.shells.append( shell )
def watson_SH_coeff( self, kappa ):
if isinstance(kappa,np.ndarray):
ERROR( '"watson_SH_coeff()" not implemented for multiple kappa input yet' )
# In the scope of AMICO only a single value is used for kappa
n = 6
C = np.zeros((n+1))
# 0th order is a constant
C[0] = 2*np.sqrt(np.pi)
# Precompute the special function values
sk = np.sqrt(kappa)
sk2 = sk*kappa
sk3 = sk2*kappa
sk4 = sk3*kappa
sk5 = sk4*kappa
sk6 = sk5*kappa
sk7 = sk6*kappa
k2 = np.power(kappa,2)
k3 = k2*kappa
k4 = k3*kappa
k5 = k4*kappa
k6 = k5*kappa
k7 = k6*kappa
erfik = scipy.special.erfi(sk)
ierfik = 1/erfik
ek = np.exp(kappa)
dawsonk = 0.5*np.sqrt(np.pi)*erfik/ek
if kappa > 0.1:
# for large enough kappa
C[1] = 3*sk - (3 + 2*kappa)*dawsonk
C[1] = np.sqrt(5)*C[1]*ek
C[1] = C[1]*ierfik/kappa
C[2] = (105 + 60*kappa + 12*k2)*dawsonk
C[2] = C[2] -105*sk + 10*sk2
C[2] = .375*C[2]*ek/k2
C[2] = C[2]*ierfik
C[3] = -3465 - 1890*kappa - 420*k2 - 40*k3
C[3] = C[3]*dawsonk
C[3] = C[3] + 3465*sk - 420*sk2 + 84*sk3
C[3] = C[3]*np.sqrt(13*np.pi)/64/k3
C[3] = C[3]/dawsonk
C[4] = 675675 + 360360*kappa + 83160*k2 + 10080*k3 + 560*k4
C[4] = C[4]*dawsonk
C[4] = C[4] - 675675*sk + 90090*sk2 - 23100*sk3 + 744*sk4
C[4] = np.sqrt(17)*C[4]*ek
C[4] = C[4]/512/k4
C[4] = C[4]*ierfik
C[5] = -43648605 - 22972950*kappa - 5405400*k2 - 720720*k3 - 55440*k4 - 2016*k5
C[5] = C[5]*dawsonk
C[5] = C[5] + 43648605*sk - 6126120*sk2 + 1729728*sk3 - 82368*sk4 + 5104*sk5
C[5] = np.sqrt(21*np.pi)*C[5]/4096/k5
C[5] = C[5]/dawsonk
C[6] = 7027425405 + 3666482820*kappa + 872972100*k2 + 122522400*k3 + 10810800*k4 + 576576*k5 + 14784*k6
C[6] = C[6]*dawsonk
C[6] = C[6] - 7027425405*sk + 1018467450*sk2 - 302630328*sk3 + 17153136*sk4 - 1553552*sk5 + 25376*sk6
C[6] = 5*C[6]*ek
C[6] = C[6]/16384/k6
C[6] = C[6]*ierfik
# for very large kappa
if kappa>30:
lnkd = np.log(kappa) - np.log(30)
lnkd2 = lnkd*lnkd
lnkd3 = lnkd2*lnkd
lnkd4 = lnkd3*lnkd
lnkd5 = lnkd4*lnkd
lnkd6 = lnkd5*lnkd
C[1] = 7.52308 + 0.411538*lnkd - 0.214588*lnkd2 + 0.0784091*lnkd3 - 0.023981*lnkd4 + 0.00731537*lnkd5 - 0.0026467*lnkd6
C[2] = 8.93718 + 1.62147*lnkd - 0.733421*lnkd2 + 0.191568*lnkd3 - 0.0202906*lnkd4 - 0.00779095*lnkd5 + 0.00574847*lnkd6
C[3] = 8.87905 + 3.35689*lnkd - 1.15935*lnkd2 + 0.0673053*lnkd3 + 0.121857*lnkd4 - 0.066642*lnkd5 + 0.0180215*lnkd6
C[4] = 7.84352 + 5.03178*lnkd - 1.0193*lnkd2 - 0.426362*lnkd3 + 0.328816*lnkd4 - 0.0688176*lnkd5 - 0.0229398*lnkd6
C[5] = 6.30113 + 6.09914*lnkd - 0.16088*lnkd2 - 1.05578*lnkd3 + 0.338069*lnkd4 + 0.0937157*lnkd5 - 0.106935*lnkd6
C[6] = 4.65678 + 6.30069*lnkd + 1.13754*lnkd2 - 1.38393*lnkd3 - 0.0134758*lnkd4 + 0.331686*lnkd5 - 0.105954*lnkd6
if kappa <= 0.1:
# for small kappa
C[1] = 4/3*kappa + 8/63*k2
C[1] = C[1]*np.sqrt(np.pi/5)
C[2] = 8/21*k2 + 32/693*k3
C[2] = C[2]*(np.sqrt(np.pi)*0.2)
C[3] = 16/693*k3 + 32/10395*k4
C[3] = C[3]*np.sqrt(np.pi/13)
C[4] = 32/19305*k4
C[4] = C[4]*np.sqrt(np.pi/17)
C[5] = 64*np.sqrt(np.pi/21)*k5/692835
C[6] = 128*np.sqrt(np.pi)*k6/152108775
return C
