Estimating cardiac contraction through high resolution data assimilation of a personalized mechanical model

Henrik Finsberg, Gabriel Balaban, Stian Ross, Trine F. Håland, Hans Henrik Odland, Joakim Sundnes, Samuel Wall

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)
141 Downloads (Pure)

Abstract

Cardiac computational models, individually personalized, can provide clinicians with useful diagnostic information and aid in treatment planning. A major bottleneck in this process can be determining model parameters to fit created models to individual patient data. However, adjoint-based data assimilation techniques can now rapidly estimate high dimensional parameter sets. This method is used on a cohort of heart failure patients, capturing cardiac mechanical information and comparing it with a healthy control group. Excellent fit (R2 ≥ 0.95) to systolic strains is obtained, and analysis shows a significant difference in estimated contractility between the two groups.
Original languageEnglish
JournalJournal of Computational Science
Early online date18 Jul 2017
DOIs
Publication statusE-pub ahead of print - 18 Jul 2017

Keywords

  • Cardiac Mechanics
  • Adjoint Method
  • Data assimilation
  • PDE-constrained optimization
  • Contractility

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