def warpANTSAffine(targetName, referenceName, affineName, oname, interpolationType='trilinear'):
baseName=rcommon.getBaseFileName(targetName)
nib_target=nib.load(targetName)
nib_reference=nib.load(referenceName)
M=nib_target.get_affine()
F=nib_reference.get_affine()
referenceShape=np.array(nib_reference.shape, dtype=np.int32)
######Load and compose affine#####
if not affineName:
T=np.eye(4)
else:
T=rcommon.readAntsAffine(affineName)
affineComposition=np.linalg.inv(M).dot(T.dot(F))
######################
if interpolationType=='NN':
target=nib_target.get_data().squeeze().astype(np.int32)
target=np.copy(target, order='C')
warped=np.array(tf.warp_discrete_volumeNNAffine(target, referenceShape, affineComposition)).astype(np.int16)
else:
target=nib_target.get_data().squeeze().astype(np.float64)
target=np.copy(target, order='C')
warped=np.array(tf.warp_volume_affine(target, referenceShape, affineComposition)).astype(np.int16)
warped=nib.Nifti1Image(warped, F)
if not oname:
oname="warped"+baseName+"nii.gz"
warped.to_filename(oname)
def testEstimateMultimodalSyN3DMultiScale(fnameMoving, fnameFixed, fnameAffine,
warpDir, lambdaParam):
'''
testEstimateMultimodalDiffeomorphicField3DMultiScale('IBSR_01_ana_strip.nii.gz', 't1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'IBSR_01_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeledAffine.txt', 100)
'''
print 'Registering', fnameMoving, 'to', fnameFixed, 'with lambda=', lambdaParam
sys.stdout.flush()
moving = nib.load(fnameMoving)
fixed = nib.load(fnameFixed)
referenceShape = np.array(fixed.shape, dtype=np.int32)
M = moving.get_affine()
F = fixed.get_affine()
if not fnameAffine:
T = np.eye(4)
else:
T = rcommon.readAntsAffine(fnameAffine)
initAffine = np.linalg.inv(M).dot(T.dot(F))
print initAffine
moving = moving.get_data().squeeze().astype(np.float64)
fixed = fixed.get_data().squeeze().astype(np.float64)
moving = np.copy(moving, order='C')
fixed = np.copy(fixed, order='C')
moving = (moving - moving.min()) / (moving.max() - moving.min())
fixed = (fixed - fixed.min()) / (fixed.max() - fixed.min())
level = 2
maskMoving = moving > 0
maskFixed = fixed > 0
movingPyramid = [
img for img in rcommon.pyramid_gaussian_3D(moving, level, maskMoving)
]
fixedPyramid = [
img for img in rcommon.pyramid_gaussian_3D(fixed, level, maskFixed)
]
#maxOuterIter=[25,50,100,100, 100, 100]
maxOuterIter = [2, 2, 2, 2, 2, 2]
baseMoving = rcommon.getBaseFileName(fnameMoving)
baseFixed = rcommon.getBaseFileName(fnameFixed)
# if(os.path.exists('disp_'+baseMoving+'_'+baseFixed+'.npy')):
# displacement=np.load('disp_'+baseMoving+'_'+baseFixed+'.npy')
# else:
displacement, directInverse = estimateMultimodalSyN3DMultiScale(
movingPyramid, fixedPyramid, initAffine, lambdaParam, maxOuterIter, 0)
tf.prepend_affine_to_displacement_field(displacement, initAffine)
# np.save('disp_'+baseMoving+'_'+baseFixed+'.npy', displacement)
#####Warp all requested volumes
#---first the target using tri-linear interpolation---
moving = nib.load(fnameMoving).get_data().squeeze().astype(np.float64)
moving = np.copy(moving, order='C')
warped = np.array(tf.warp_volume(moving, displacement)).astype(np.int16)
imgWarped = nib.Nifti1Image(warped, F)
imgWarped.to_filename('warpedDiff_' + baseMoving + '_' + baseFixed +
'.nii.gz')
#---warp using affine only
moving = nib.load(fnameMoving).get_data().squeeze().astype(np.int32)
moving = np.copy(moving, order='C')
warped = np.array(
tf.warp_discrete_volumeNNAffine(moving, referenceShape,
initAffine)).astype(np.int16)
imgWarped = nib.Nifti1Image(
warped, F) #The affine transformation is the reference's one
imgWarped.to_filename('warpedAffine_' + baseMoving + '_' + baseFixed +
'.nii.gz')
#---now the rest of the targets using nearest neighbor
names = [os.path.join(warpDir, name) for name in os.listdir(warpDir)]
for name in names:
#---warp using the non-linear deformation
toWarp = nib.load(name).get_data().squeeze().astype(np.int32)
toWarp = np.copy(toWarp, order='C')
baseWarp = rcommon.getBaseFileName(name)
warped = np.array(tf.warp_discrete_volumeNN(
toWarp, displacement)).astype(np.int16)
imgWarped = nib.Nifti1Image(
warped, F) #The affine transformation is the reference's one
imgWarped.to_filename('warpedDiff_' + baseWarp + '_' + baseFixed +
'.nii.gz')
#---warp using affine inly
warped = np.array(
tf.warp_discrete_volumeNNAffine(toWarp, referenceShape,
initAffine)).astype(np.int16)
imgWarped = nib.Nifti1Image(
warped, F) #The affine transformation is the reference's one
imgWarped.to_filename('warpedAffine_' + baseWarp + '_' + baseFixed +
'.nii.gz')
#---finally, the deformed lattices (forward, inverse and resdidual)---
lambdaParam = 0.9
maxIter = 100
tolerance = 1e-4
print 'Computing inverse...'
inverse = np.array(
tf.invert_vector_field3D(displacement, lambdaParam, maxIter,
tolerance))
residual = np.array(tf.compose_vector_fields3D(displacement, inverse))
saveDeformedLattice3D(
displacement,
'latticeDispDiff_' + baseMoving + '_' + baseFixed + '.nii.gz')
saveDeformedLattice3D(
inverse, 'latticeInvDiff_' + baseMoving + '_' + baseFixed + '.nii.gz')
saveDeformedLattice3D(
residual, 'latticeResdiff_' + baseMoving + '_' + baseFixed + '.nii.gz')
residual = np.sqrt(np.sum(residual**2, 3))
print "Mean residual norm:", residual.mean(), " (", residual.std(
), "). Max residual norm:", residual.max()
def testEstimateMultimodalSyN3DMultiScale(fnameMoving, fnameFixed, fnameAffine, warpDir, lambdaParam):
'''
testEstimateMultimodalDiffeomorphicField3DMultiScale('IBSR_01_ana_strip.nii.gz', 't1_icbm_normal_1mm_pn0_rf0_peeled.nii.gz', 'IBSR_01_ana_strip_t1_icbm_normal_1mm_pn0_rf0_peeledAffine.txt', 100)
'''
print 'Registering', fnameMoving, 'to', fnameFixed,'with lambda=',lambdaParam
sys.stdout.flush()
moving = nib.load(fnameMoving)
fixed= nib.load(fnameFixed)
referenceShape=np.array(fixed.shape, dtype=np.int32)
M=moving.get_affine()
F=fixed.get_affine()
if not fnameAffine:
T=np.eye(4)
else:
T=rcommon.readAntsAffine(fnameAffine)
initAffine=np.linalg.inv(M).dot(T.dot(F))
print initAffine
moving=moving.get_data().squeeze().astype(np.float64)
fixed=fixed.get_data().squeeze().astype(np.float64)
moving=np.copy(moving, order='C')
fixed=np.copy(fixed, order='C')
moving=(moving-moving.min())/(moving.max()-moving.min())
fixed=(fixed-fixed.min())/(fixed.max()-fixed.min())
level=2
maskMoving=moving>0
maskFixed=fixed>0
movingPyramid=[img for img in rcommon.pyramid_gaussian_3D(moving, level, maskMoving)]
fixedPyramid=[img for img in rcommon.pyramid_gaussian_3D(fixed, level, maskFixed)]
#maxOuterIter=[25,50,100,100, 100, 100]
maxOuterIter=[2,2,2,2,2,2]
baseMoving=rcommon.getBaseFileName(fnameMoving)
baseFixed=rcommon.getBaseFileName(fnameFixed)
# if(os.path.exists('disp_'+baseMoving+'_'+baseFixed+'.npy')):
# displacement=np.load('disp_'+baseMoving+'_'+baseFixed+'.npy')
# else:
displacement, directInverse=estimateMultimodalSyN3DMultiScale(movingPyramid, fixedPyramid, initAffine, lambdaParam, maxOuterIter, 0)
tf.prepend_affine_to_displacement_field(displacement, initAffine)
# np.save('disp_'+baseMoving+'_'+baseFixed+'.npy', displacement)
#####Warp all requested volumes
#---first the target using tri-linear interpolation---
moving=nib.load(fnameMoving).get_data().squeeze().astype(np.float64)
moving=np.copy(moving, order='C')
warped=np.array(tf.warp_volume(moving, displacement)).astype(np.int16)
imgWarped=nib.Nifti1Image(warped, F)
imgWarped.to_filename('warpedDiff_'+baseMoving+'_'+baseFixed+'.nii.gz')
#---warp using affine only
moving=nib.load(fnameMoving).get_data().squeeze().astype(np.int32)
moving=np.copy(moving, order='C')
warped=np.array(tf.warp_discrete_volumeNNAffine(moving, referenceShape, initAffine)).astype(np.int16)
imgWarped=nib.Nifti1Image(warped, F)#The affine transformation is the reference's one
imgWarped.to_filename('warpedAffine_'+baseMoving+'_'+baseFixed+'.nii.gz')
#---now the rest of the targets using nearest neighbor
names=[os.path.join(warpDir,name) for name in os.listdir(warpDir)]
for name in names:
#---warp using the non-linear deformation
toWarp=nib.load(name).get_data().squeeze().astype(np.int32)
toWarp=np.copy(toWarp, order='C')
baseWarp=rcommon.getBaseFileName(name)
warped=np.array(tf.warp_discrete_volumeNN(toWarp, displacement)).astype(np.int16)
imgWarped=nib.Nifti1Image(warped, F)#The affine transformation is the reference's one
imgWarped.to_filename('warpedDiff_'+baseWarp+'_'+baseFixed+'.nii.gz')
#---warp using affine inly
warped=np.array(tf.warp_discrete_volumeNNAffine(toWarp, referenceShape, initAffine)).astype(np.int16)
imgWarped=nib.Nifti1Image(warped, F)#The affine transformation is the reference's one
imgWarped.to_filename('warpedAffine_'+baseWarp+'_'+baseFixed+'.nii.gz')
#---finally, the deformed lattices (forward, inverse and resdidual)---
lambdaParam=0.9
maxIter=100
tolerance=1e-4
print 'Computing inverse...'
inverse=np.array(tf.invert_vector_field3D(displacement, lambdaParam, maxIter, tolerance))
residual=np.array(tf.compose_vector_fields3D(displacement, inverse))
saveDeformedLattice3D(displacement, 'latticeDispDiff_'+baseMoving+'_'+baseFixed+'.nii.gz')
saveDeformedLattice3D(inverse, 'latticeInvDiff_'+baseMoving+'_'+baseFixed+'.nii.gz')
saveDeformedLattice3D(residual, 'latticeResdiff_'+baseMoving+'_'+baseFixed+'.nii.gz')
residual=np.sqrt(np.sum(residual**2,3))
print "Mean residual norm:", residual.mean()," (",residual.std(), "). Max residual norm:", residual.max()
def estimateNewMultimodalDiffeomorphicField3D(moving,
fixed,
initAffine,
lambdaDisplacement,
quantizationLevels,
maxOuterIter,
previousDisplacement,
reportProgress=False):
innerTolerance = 1e-3
outerTolerance = 1e-3
displacement = np.empty(shape=(fixed.shape) + (3, ), dtype=np.float64)
residuals = np.zeros(shape=(fixed.shape), dtype=np.float64)
gradientField = np.empty(shape=(fixed.shape) + (3, ), dtype=np.float64)
totalDisplacement = np.zeros(shape=(fixed.shape) + (3, ), dtype=np.float64)
if (previousDisplacement != None):
totalDisplacement[...] = previousDisplacement
fixedQ = None
grayLevels = None
fixedQ, grayLevels, hist = tf.quantizePositiveVolumeCYTHON(
fixed, quantizationLevels)
fixedQ = np.array(fixedQ, dtype=np.int32)
finished = False
outerIter = 0
maxDisplacement = None
maxVariation = None
maxResidual = 0
fixedMask = (fixed > 0).astype(np.int32)
movingMask = (moving > 0).astype(np.int32)
trustRegion = fixedMask * np.array(
tf.warp_discrete_volumeNNAffine(
movingMask, np.array(fixedMask.shape, dtype=np.int32),
initAffine)) #consider only the overlap after affine registration
while ((not finished) and (outerIter < maxOuterIter)):
outerIter += 1
if (reportProgress):
print 'Iter:', outerIter, '/', maxOuterIter
#sys.stdout.flush()
#---E step---
#print "Warping..."
#sys.stdout.flush()
warped = np.array(tf.warp_volume(moving, totalDisplacement,
initAffine))
warpedMask = np.array(
tf.warp_discrete_volumeNN(
trustRegion, totalDisplacement, np.eye(4))).astype(
np.int32) #the affine mapping was already applied
#print "Warping NN..."
#sys.stdout.flush()
#warpedMovingMask=np.array(tf.warp_volumeNN(movingMask, totalDisplacement)).astype(np.int32)
#print "Class stats..."
#sys.stdout.flush()
means, variances = tf.computeMaskedVolumeClassStatsCYTHON(
warpedMask, warped, quantizationLevels, fixedQ)
means[0] = 0
means = np.array(means)
variances = np.array(variances)
sigmaField = variances[fixedQ]
deltaField = means[
fixedQ] - warped #########Delta-field using Arce's rule
#--M step--
g0, g1, g2 = sp.gradient(warped)
gradientField[:, :, :, 0] = g0
gradientField[:, :, :, 1] = g1
gradientField[:, :, :, 2] = g2
maxVariation = 1 + innerTolerance
innerIter = 0
maxInnerIter = 100
displacement[...] = 0
#print "Iterating..."
#sys.stdout.flush()
while ((maxVariation > innerTolerance) and (innerIter < maxInnerIter)):
innerIter += 1
maxVariation = tf.iterateDisplacementField3DCYTHON(
deltaField, sigmaField, gradientField, lambdaDisplacement,
totalDisplacement, displacement, residuals)
opt = np.max(residuals)
if (maxResidual < opt):
maxResidual = opt
#--accumulate displacement--
#print "Exponential3D. Range D:", displacement.min(), displacement.max()
#sys.stdout.flush()
expd, inverseNone = tf.vector_field_exponential3D(displacement, False)
expd = np.array(expd)
#print "Range expd:", expd.min(), expd.max(), "Range TD:", totalDisplacement.min(), totalDisplacement.max()
#print "Compose vector fields..."
#sys.stdout.flush()
totalDisplacement, stats = tf.compose_vector_fields3D(
expd, totalDisplacement)
totalDisplacement = np.array(totalDisplacement)
#print "Composed rage:", totalDisplacement.min(), totalDisplacement.max()
#sys.stdout.flush()
#--check stop condition--
nrm = np.sqrt(displacement[..., 0]**2 + displacement[..., 1]**2 +
displacement[..., 2]**2)
#maxDisplacement=np.max(nrm)
maxDisplacement = np.mean(nrm)
if ((maxDisplacement < outerTolerance) or (outerIter >= maxOuterIter)):
finished = True
print "Iter: ", outerIter, "Mean displacement:", maxDisplacement, "Max variation:", maxVariation, "Max residual:", maxResidual
#sh=fixed.shape
#rcommon.overlayImages(warped[:,sh[1]//2,:], fixed[:,sh[1]//2,:])
#rcommon.overlayImages(warped[:,sh[1]//2,:]*warpedMask[:,sh[1]//2,:], fixed[:,sh[1]//2,:])
#sys.stdout.flush()
if (previousDisplacement != None):
#print 'Range TD:', totalDisplacement.min(), totalDisplacement.max(),'. Range PD:', previousDisplacement.min(), previousDisplacement.max()
#sys.stdout.flush()
return totalDisplacement - previousDisplacement
return totalDisplacement
def estimateNewMultimodalDiffeomorphicField3D(moving, fixed, initAffine, lambdaDisplacement, quantizationLevels, maxOuterIter, previousDisplacement, reportProgress=False):
innerTolerance=1e-3
outerTolerance=1e-3
displacement =np.empty(shape=(fixed.shape)+(3,), dtype=np.float64)
residuals=np.zeros(shape=(fixed.shape), dtype=np.float64)
gradientField =np.empty(shape=(fixed.shape)+(3,), dtype=np.float64)
totalDisplacement=np.zeros(shape=(fixed.shape)+(3,), dtype=np.float64)
if(previousDisplacement!=None):
totalDisplacement[...]=previousDisplacement
fixedQ=None
grayLevels=None
fixedQ, grayLevels, hist=tf.quantizePositiveVolumeCYTHON(fixed, quantizationLevels)
fixedQ=np.array(fixedQ, dtype=np.int32)
finished=False
outerIter=0
maxDisplacement=None
maxVariation=None
maxResidual=0
fixedMask=(fixed>0).astype(np.int32)
movingMask=(moving>0).astype(np.int32)
trustRegion=fixedMask*np.array(tf.warp_discrete_volumeNNAffine(movingMask, np.array(fixedMask.shape, dtype=np.int32), initAffine))#consider only the overlap after affine registration
while((not finished) and (outerIter<maxOuterIter)):
outerIter+=1
if(reportProgress):
print 'Iter:',outerIter,'/',maxOuterIter
#sys.stdout.flush()
#---E step---
#print "Warping..."
#sys.stdout.flush()
warped=np.array(tf.warp_volume(moving, totalDisplacement, initAffine))
warpedMask=np.array(tf.warp_discrete_volumeNN(trustRegion, totalDisplacement, np.eye(4))).astype(np.int32)#the affine mapping was already applied
#print "Warping NN..."
#sys.stdout.flush()
#warpedMovingMask=np.array(tf.warp_volumeNN(movingMask, totalDisplacement)).astype(np.int32)
#print "Class stats..."
#sys.stdout.flush()
means, variances=tf.computeMaskedVolumeClassStatsCYTHON(warpedMask, warped, quantizationLevels, fixedQ)
means[0]=0
means=np.array(means)
variances=np.array(variances)
sigmaField=variances[fixedQ]
deltaField=means[fixedQ]-warped#########Delta-field using Arce's rule
#--M step--
g0, g1, g2=sp.gradient(warped)
gradientField[:,:,:,0]=g0
gradientField[:,:,:,1]=g1
gradientField[:,:,:,2]=g2
maxVariation=1+innerTolerance
innerIter=0
maxInnerIter=100
displacement[...]=0
#print "Iterating..."
#sys.stdout.flush()
while((maxVariation>innerTolerance)and(innerIter<maxInnerIter)):
innerIter+=1
maxVariation=tf.iterateDisplacementField3DCYTHON(deltaField, sigmaField, gradientField, lambdaDisplacement, totalDisplacement, displacement, residuals)
opt=np.max(residuals)
if(maxResidual<opt):
maxResidual=opt
#--accumulate displacement--
#print "Exponential3D. Range D:", displacement.min(), displacement.max()
#sys.stdout.flush()
expd, inverseNone=tf.vector_field_exponential3D(displacement, False)
expd=np.array(expd)
#print "Range expd:", expd.min(), expd.max(), "Range TD:", totalDisplacement.min(), totalDisplacement.max()
#print "Compose vector fields..."
#sys.stdout.flush()
totalDisplacement, stats=tf.compose_vector_fields3D(expd, totalDisplacement)
totalDisplacement=np.array(totalDisplacement)
#print "Composed rage:", totalDisplacement.min(), totalDisplacement.max()
#sys.stdout.flush()
#--check stop condition--
nrm=np.sqrt(displacement[...,0]**2+displacement[...,1]**2+displacement[...,2]**2)
#maxDisplacement=np.max(nrm)
maxDisplacement=np.mean(nrm)
if((maxDisplacement<outerTolerance)or(outerIter>=maxOuterIter)):
finished=True
print "Iter: ",outerIter, "Mean displacement:", maxDisplacement, "Max variation:",maxVariation, "Max residual:", maxResidual
#sh=fixed.shape
#rcommon.overlayImages(warped[:,sh[1]//2,:], fixed[:,sh[1]//2,:])
#rcommon.overlayImages(warped[:,sh[1]//2,:]*warpedMask[:,sh[1]//2,:], fixed[:,sh[1]//2,:])
#sys.stdout.flush()
if(previousDisplacement!=None):
#print 'Range TD:', totalDisplacement.min(), totalDisplacement.max(),'. Range PD:', previousDisplacement.min(), previousDisplacement.max()
#sys.stdout.flush()
return totalDisplacement-previousDisplacement
return totalDisplacement
