Working Paper (2021)
Faucher Benjamin, Assab Rania, Roux Jonathan, Levy-Bruhl Daniel, Tran-Kiem Cécile, Cauchemez Simon, Zanetti Laura, Colizza Vittoria, Boëlle Pierre-Yves, and Poletto Chiara
By Finsberg Henrik, Balaban Gabriel, Ross Stian, Håland Trine F, Odland Hans Henrik, Sundnes Joakim, and Wall Samuel
Journal of Computational Science (2017)
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.
Finsberg H., Balaban G., Ross S., Håland T., Odland H., Sundnes J., and Wall S. (2017) Estimating cardiac contraction through high resolution data assimilation of a personalized mechanical model. Journal of Computational Science.