Spatially-Adaptive Multi-scale Optimization for Local Parameter Estimation: Application in Cardiac Electrophysiological Models

  • Jwala DhamalaEmail author
  • John L. Sapp
  • Milan Horacek
  • Linwei Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)


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.


Parameter 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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Authors and Affiliations

  • Jwala Dhamala
    • 1
    Email author
  • John L. Sapp
    • 2
  • Milan Horacek
    • 2
  • Linwei Wang
    • 1
  1. 1.Rochester Institute of TechnologyRochesterUSA
  2. 2.Dalhousie UniversityHalifaxCanada

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