Spatially-Adaptive Multi-scale Optimization for Local Parameter Estimation: Application in Cardiac Electrophysiological Models
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The estimation of local parameter values for a 3D cardiac model is important for revealing abnormal tissues with altered material properties and for building patient-specific models. Existing works in local parameter estimation typically represent the heart with a small number of pre-defined segments to reduce the dimension of unknowns. Such low-resolution approaches have limited ability to estimate tissues with varying sizes, locations, and distributions. We present a novel optimization framework to achieve a higher-resolution parameter estimation without using a high number of unknowns. It has two central elements: (1) a multi-scale coarse-to-fine optimization that uses low-resolution solutions to facilitate the higher-resolution optimization; and (2) a spatially-adaptive scheme that dedicates higher resolution to regions of heterogeneous tissue properties whereas retaining low resolution in homogeneous regions. Synthetic and real-data experiments demonstrate the ability of the presented framework to improve the accuracy of local parameter estimation in comparison to optimization based on fixed-segment models.
KeywordsParameter estimation Cardiac electrophysiological model Multi-scale optimization Gaussian process
This work is supported by the National Science Foundation under CAREER Award ACI-1350374 and the National Institute of Heart, Lung, and Blood of the National Institutes of Health under Award R21Hl125998.
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