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