[1] Maier A, Gee MW, Reeps C, Pongratz J, Eckstein HH, Wall WA. A Comparison of Diameter, Wall Stress, and Rupture Potential Index for Abdominal Aortic Aneurysm Rupture Risk Prediction. Annals of Biomedical Engineering May 2010; 38(10):3124–3134. [2] Gokhale, Nachiket H., Paul E. Barbone, and Assad A. Oberai. "Solution of the nonlinear elasticity imaging inverse problem: the compressible case." Inverse Problems 24.4 (2008): 045010. [3] Fowler, K. R., and C. T. Kelley. "Pseudo-transient continuation for nonsmooth nonlinear equations." SIAM journal on numerical analysis 43.4 (2005): 1385-1406. Prediction of AAA – growth using computational methods • Computational models become increasingly capable of surpassing conventional methods of rupture risk prediction for abdominal aortic aneurysms (AAA), such as the diameter criterion[1]. • Incorporating arterial growth adds further accuracy to the predicting capabilities of computational approaches. Reliable predictions need accurate spatial representations of model parameters • The spatial distribution of material parameters is a priori unknown. • Due to its non invasive character, image registration techniques play a key role in providing accurate information (“measurements of deformation”) being used in parameter identification problems[2].