Manifold learning for cardiac modeling and estimation framework

Radomir Chabiniok, Kanwal K. Bhatia, Andrew P. King, Daniel Rueckert, Nic Smith

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work we apply manifold learning to biophysical modeling of cardiac contraction with the aim of estimating material parameters characterizing myocardial stiffness and contractility. The set of cardiac cycle simulations spanning the parameter space of myocardial stiffness and contractility is used to create a manifold structure based on the motion pattern of the left ventricle endocardial surfaces. First, we assess the proposed method by using synthetic data generated by the model specifically to test our approach with the known ground truth parameter values. Then, we apply the method on cardiac magnetic resonance imaging (MRI) data of two healthy volunteers. The post-processed cine MRI for each volunteer were embedded into the manifold together with the simulated samples and the global parameters of contractility and stiffness for the whole myocardium were estimated. Then, we used these parameters as an initialization into an estimator of regional contractilities based on a reduced order unscented Kalman filter. The global values of stiffness and contractility obtained by manifold learning corrected the model in comparison to a standard model calibration by generic parameters, and a significantly more accurate estimation of regional contractilities was reached when using the initialization given by manifold learning.

Original languageEnglish (US)
Title of host publicationStatistical Atlases and Computational Models of the Heart
Subtitle of host publicationImaging and Modelling Challenges - 5th International Workshop, STACOM 2014 Held in Conjunction with MICCAI 2014, Revised Selected Papers
EditorsMihaela Pop, Tommaso Mansi, Oscar Camara, Maxime Sermesant, Alistair Young, Kawal Rhode, Kawal Rhode
PublisherSpringer Verlag
Pages284-294
Number of pages11
ISBN (Electronic)9783319146775
DOIs
StatePublished - 2015
Externally publishedYes
Event5th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2014 Held in Conjunction with Medical Image Computing and Computer Assisted Intervention Conference, MICCAI 2014 - Boston, United States
Duration: Sep 18 2014Sep 18 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8896
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2014 Held in Conjunction with Medical Image Computing and Computer Assisted Intervention Conference, MICCAI 2014
Country/TerritoryUnited States
CityBoston
Period9/18/149/18/14

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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