|Authors||J. Dhamala, P. Bajracharya, H. Arevalo, J. L. Sapp, M. B. Horácek, K. C. Wu, N. A. Trayanova and L. Wang|
|Title||Embedding high-dimensional Bayesian optimization via generative modeling: Parameter personalization of cardiac electrophysiological models|
|Project(s)||Department of Computational Physiology|
|Publication Type||Journal Article|
|Year of Publication||2020|
|Journal||Medical Image Analysis|
|Keywords||High-dimensional Bayesian optimization, personalized modeling, variational autoencoder|
The estimation of patient-specific tissue properties in the form of model parameters is important for personalized physiological models. Because tissue properties are spatially varying across the underlying geometrical model, it presents a significant challenge of high-dimensional (HD) optimization at the presence of limited measurement data. A common solution to reduce the dimension of the parameter space is to explicitly partition the geometrical mesh. In this paper, we present a novel concept that uses a generative variational auto-encoder (VAE) to embed HD Bayesian optimization into a low-dimensional (LD) latent space that represents the generative code of HD parameters. We further utilize VAE-encoded knowledge about the generative code to guide the exploration of the search space. The presented method is applied to estimating tissue excitability in a cardiac electrophysiological model in a range of synthetic and real-data experiments, through which we demonstrate its improved accuracy and substantially reduced computational cost in comparison to existing methods that rely on geometry-based reduction of the HD parameter space.