AuthorsJ. Dhamala, P. Bajracharya, H. Arevalo, J. L. Sapp, M. B. Horácek, K. C. Wu, N. A. Trayanova and L. Wang
TitleEmbedding high-dimensional Bayesian optimization via generative modeling: Parameter personalization of cardiac electrophysiological models
AfilliationScientific Computing
Project(s)Department of Computational Physiology
StatusPublished
Publication TypeJournal Article
Year of Publication2020
JournalMedical Image Analysis
Volume62
Pagination101670
PublisherElsevier
ISSN1361-8415
KeywordsHigh-dimensional Bayesian optimization, personalized modeling, variational autoencoder
Abstract

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.

URLhttp://www.sciencedirect.com/science/article/pii/S1361841520300360
DOI10.1016/j.media.2020.101670
Citation KeyDHAMALA2020101670