TY - CHAP
T1 - STACOM challenge
T2 - 5th 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
AU - Asner, Liia
AU - Hadjicharalambous, Myria
AU - Lee, Jack
AU - Nordsletten, David
PY - 2015
Y1 - 2015
N2 - In this paper we outline our approach for creating subjectspecific whole-cycle canine left-ventricular models, as part of the 2014 STACOM Challenge. Each canine heart was modeled using the principle of stationary potential energy, with the myocardium treated as a nearly incompressible hyperelastic material. Given incomplete data on the motion and behavior of each canine heart, we decreased model complexity by employing reduced–parameter constitutive laws. Additionally, base plane motion and left ventricular volume input data were integrated into the cardiac cycle model through the inclusion of novel external energy potentials (using Lagrange multipliers), which allow for relaxed adherence to the constraints and minimize spurious energy modes stemming from model simplification and data noise. Subsequently, using the available data we employ the reduced-order unscented Kalman filter (ROUKF) approach to estimate the myocardial passive parameters and active tension. Finally, along with model predictions for each canine, we assess the spatial convergence and robustness of our model.
AB - In this paper we outline our approach for creating subjectspecific whole-cycle canine left-ventricular models, as part of the 2014 STACOM Challenge. Each canine heart was modeled using the principle of stationary potential energy, with the myocardium treated as a nearly incompressible hyperelastic material. Given incomplete data on the motion and behavior of each canine heart, we decreased model complexity by employing reduced–parameter constitutive laws. Additionally, base plane motion and left ventricular volume input data were integrated into the cardiac cycle model through the inclusion of novel external energy potentials (using Lagrange multipliers), which allow for relaxed adherence to the constraints and minimize spurious energy modes stemming from model simplification and data noise. Subsequently, using the available data we employ the reduced-order unscented Kalman filter (ROUKF) approach to estimate the myocardial passive parameters and active tension. Finally, along with model predictions for each canine, we assess the spatial convergence and robustness of our model.
KW - Canine heart
KW - Cardiac mechanics
KW - Data assimilation
KW - Parameter estimation
UR - http://www.scopus.com/inward/record.url?scp=84927794416&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-14678-2_13
DO - 10.1007/978-3-319-14678-2_13
M3 - Chapter
AN - SCOPUS:84927794416
SN - 9783319146775
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 123
EP - 134
BT - Statistical Atlases and Computational Models of the Heart - Imaging and Modelling Challenges
PB - Springer-Verlag Berlin Heidelberg
Y2 - 18 September 2014 through 18 September 2014
ER -