Algorithms for Image Registration:Advanced Normalization Tools (ANTS)

Brian Avants, Nick Tustison, Gang Song, James C. Gee

Penn Image Computing and Science LaboratoryDepartments of Radiology, University of Pennsylvania,

Philadelphia, PA, USA

Advanced Normalization Tools (ANTs)

• An open-source toolkit for low and high-dimensional image registration.

• Simple command-line user interface reflects the variational optimization equation.

• Few parameters for most applications.• Well-evaluated & focus on usability.• Large range of functionality – similarity

metrics, landmarks, multiple optimization terms, multiple transformation models.

Affine Registration

• Stochastic gradient descent (Klein, Staring, Pluim) for speed per iteration.

• Multi-start global optimization option (MMBIA 2007) for challenging problems.

• Mutual information similarity• Landmarks & cost-masking enabled• Mapping decomposed into Rotation, Shearing,

Translation: easily generates GL group subspaces.

Synthetic Database

95.480.8813.43.621.2350.0970.0390.019prior1 50

95.291.3814.94.571.5540.0930.040.021prior 50

64.970.666.582.011.6120.1560.0940.025mstart 200

94.380001.6850.2020.0950.044grad 0

mNCrNCrMSErMIdtdKdSdRStrategy

Image Similarity Metric (%)Transform parameters metric

48 images warped from the template, 256x256x124, Affine warping + random Bspline nonrigid warping.

Deformation Models

• Elastic, e.g. Demons method.• Exponential Map Diff, e.g. Ashburner’s Dartel.• Time-Dependent Diff, e.g. LDDMM.• Bi-directional Diff (Exp, T-D or greedy impl.)• All are available as optional transformation

models. • Models may also be combined, in some cases.

Similarity Metrics

ANTS -DIFF –m SSD(I,J,w1) –m MI(I,J,w2) –m LM(I,J,w3)

Diff Regularization

May be easily combined turned on/off, applied to different images etc

ANTS -m MI[CT.nii,PET.nii,32] -Exp -n 3 -i 10x10x10 -o PETtoCT

PET warped to CT

Unregistered PET and CT

Jacobian of transformation PET overlayed on CT

Original Pet TransmissionOriginal CT

Diffeomorphic Mapping

Difffeomorphism

Elastic Under-Normalization

OS & Input/Output Issues

• ITK-compatible – builds using standard ITK, cmake, etc.

• NIFTI/SIFTI friendly, using ITK I/O.• How do we deal with orientation, etc?• Experience has shown header information

(particularly origin, orientation, affine matrix) is not always “right.”

• We thus allow its use as an option.

Conclusion & Future Work

• Parallelization and memory-efficient.• Xml format for organizing processing/results.• Alternative optimization – gradient descent now.• To Obtain: seek “Advanced Normalization Tools

(ANTs)” at sourceforge.net also at NITRC.• References:

– Evaluation of 14 non-rigid registration algorithms, A Klein, et al in preparation.– B Avants, et al. Symmetric diffeomorphic image registration, 2008.– Euler-Lagrange equations of computational anatomy, M. I. Miller, et al, 2003.

Affine Transform Space Parameterization

• Affine Registration: T(x) = Ax + t• A = R x S x K.

– Rotation R: a unit quaternion vector – Scaling S: 3 scaling factors in each axis– Shearing K: 3 coefficients in the upper triangle.

Real Image Database

prior 50 mstart 200 grad0

template test image their difference

registration

differencing

67 images of elderly and neurodegenerative human brains, T1 MRI 1.5 T, 1x1x1.5mm,

256x256x124.

93.441.1311.602.99prior1 50

93.671.2414.103.11prior 50

92.970.617.040.98mstart 200

92.29000grad 0

mNCrNCrMSErMIStrategy

Image Similarity Metric (%)