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Fast optimization-based elasticity parameter estimation using reduced models

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Abstract

Elasticity parameters are central to physically-based animation and medical image analysis. We present an accelerated method to automatically estimate these parameters for a deformation simulator using an iterative optimization framework, given the desired (target) output surface/shape. During the optimization, the input model is deformed by the simulator, and the distance between the deformed surface and the target surface is minimized numerically. To accelerate the optimization process, we introduce a dimension reduction technique to allow a trade-off between the computational efficiency and desired accuracy. The reduced model is constructed using statistical training with a set of example deformations. To demonstrate this approach, we apply the computational framework to 2D animations of elastic bodies simulated with a linear finite element method. We also present a 3D elastography example, which is simulated with a reduced-dimension finite element model to improve the performance of the optimizer.

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Acknowledgements

We thank Mark Foskey, Marc Niethammer, Ron Alterovitz, and Ed Chaney for helpful discussions. We also thank Zijie Xu and Dr. Ron Chen for providing prostate patient CT data. This work was partially supported by Army Research Office, National Science Foundation, and Carolina Development Foundation.

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  1. University of North Carolina, Chapel Hill, USA

    Huai-Ping Lee & Ming C. Lin

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Correspondence to Huai-Ping Lee.

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Lee, HP., Lin, M.C. Fast optimization-based elasticity parameter estimation using reduced models. Vis Comput 28, 553–562 (2012). https://doi.org/10.1007/s00371-012-0686-z

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