1. Algorithms for Image Registration: Advanced Normalization Tools (ANTS) Brian Avants, Nick Tustison, Gang Song, James C. Gee Penn Image Computing and Science Laboratory Departments of Radiology, University of Pennsylvania, Philadelphia, PA, USA

2. 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.

3. 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.

4. Synthetic Database 95.480.8813.43.621.2350.0970.0390.019prior1 50 95.291.3814.94.571.5540.0930.040.021prior 50 64.970.666.582.011.6120.1560.0940.025 mstart 200 94.380001.6850.2020.0950.044 grad 0 mNCrNC rMSE rMIdtdKdSdRStrategy Image Similarity Metric (%)Transform parameters metric 48 images warped from the template, 256x256x124, Affine warping + random Bspline nonrigid warping.

5. 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.

6. 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

7. 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 transformationPET overlayed on CT Original Pet TransmissionOriginal CT

8. Diffeomorphic Mapping Difffeomorphism Elastic Under-Normalization

9. 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.

10. 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.

11. 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.

12. 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. 93.4 41.1311.602.99prior1 50 93.6 71.2414.103.11prior 50 92.9 70.617.040.98mstart 200 92.2 9000grad 0 mNCrNCrMSErMIStrategy Image Similarity Metric (%)