TY - GEN
T1 - Personalised electromechanical model of the heart for the prediction of the acute effects of cardiac resynchronisation therapy
AU - Sermesant, Maxime
AU - Billet, Florence
AU - Chabiniok, Radomir
AU - Mansi, Tommaso
AU - Chinchapatnam, Phani
AU - Moireau, Philippe
AU - Peyrat, Jean Marc
AU - Rhode, Kawal
AU - Ginks, Matt
AU - Lambiase, Pier
AU - Arridge, Simon
AU - Delingette, Hervé
AU - Sorine, Michel
AU - Rinaldi, C. Aldo
AU - Chapelle, Dominique
AU - Razavi, Reza
AU - Ayache, Nicholas
PY - 2009
Y1 - 2009
N2 - Cardiac resynchronisation therapy (CRT) has been shown to be an effective adjunctive treatment for patients with dyssynchronous ventricular contraction and symptoms of the heart failure. However, clinical trials have also demonstrated that up to 30% of patients may be classified as non-responders. In this article, we present how the personalisation of an electromechanical model of the myocardium could help the therapy planning for CRT. We describe the four main components of our myocardial model, namely the anatomy, the electrophysiology, the kinematics and the mechanics. For each of these components we combine prior knowledge and observable parameters in order to personalise these models to patient data. Then the acute effects of a pacemaker on the cardiac function are predicted with the in silico model on a clinical case. This is a proof of concept of the potential of virtual physiological models to better select and plan the therapy.
AB - Cardiac resynchronisation therapy (CRT) has been shown to be an effective adjunctive treatment for patients with dyssynchronous ventricular contraction and symptoms of the heart failure. However, clinical trials have also demonstrated that up to 30% of patients may be classified as non-responders. In this article, we present how the personalisation of an electromechanical model of the myocardium could help the therapy planning for CRT. We describe the four main components of our myocardial model, namely the anatomy, the electrophysiology, the kinematics and the mechanics. For each of these components we combine prior knowledge and observable parameters in order to personalise these models to patient data. Then the acute effects of a pacemaker on the cardiac function are predicted with the in silico model on a clinical case. This is a proof of concept of the potential of virtual physiological models to better select and plan the therapy.
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U2 - 10.1007/978-3-642-01932-6_26
DO - 10.1007/978-3-642-01932-6_26
M3 - Conference contribution
AN - SCOPUS:68849100096
SN - 3642019315
SN - 9783642019319
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 239
EP - 248
BT - Functional Imaging and Modeling of the Heart - 5th International Conference, FIMH 2009, Proceedings
T2 - 5th International Conference on Functional Imaging and Modeling of the Heart, FIMH 2009
Y2 - 3 June 2009 through 5 June 2009
ER -