STACOM challenge: Simulating left ventricular mechanics in the canine heart

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8896, Issue , 2015, Pages 123-134

  Asner, Liya ^a   Hadjicharalambous, Myrianthi ^a   Lee, Jack ^a   Nordsletten, David ^a  

^a KING'S COLLEGE LONDON   (United Kingdom)

Author keywords

Canine heart; Cardiac mechanics; Data assimilation; Parameter estimation

Indexed keywords

COMPUTATION THEORY; COMPUTATIONAL METHODS; DATA REDUCTION; LAGRANGE MULTIPLIERS; MEDICAL IMAGING; PARAMETER ESTIMATION; POTENTIAL ENERGY;

CARDIAC MECHANICS; DATA ASSIMILATION; MODEL COMPLEXITY; MODEL PREDICTION; MODEL SIMPLIFICATION; NEARLY INCOMPRESSIBLE; STATIONARY POTENTIALS; UNSCENTED KALMAN FILTER;

HEART;

EID: 84927794416     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-14678-2_13     Document Type: Conference Paper

References (15)

1. Augenstein, K., Cowan, B., LeGrice, I., Nielsen, P., Young, A.: Method and apparatus for soft tissue material parameter estimation using tissue tagged magnetic resonance imaging. J. Biomech. Eng. 127(1), 148–157 (2005)
2. Bonet, J., Wood, R.: Nonlinear Continuum Mechanics for Finite Element Analysis. Cambridge University Press (2008)
3. Brezzi, F., Fortin, M.: Mixed and hybrid finite element methods. Springer, Heidelberg (1991)
4. Chabiniok, R., Moireau, P., Lesault, P.-F., Rahmouni, A., Deux, J.-F., Chapelle, D.: Estimation of tissue contractility from cardiac cine-MRI using a biomechanical heart model. Biomech. Model. Mechan. 11(5), 609–630 (2012)
5. Chapelle, D., Fragu, M., Mallet, V., Moireau, P.: Fundamental principles of data assimilation underlying the Verdandi library: applications to biophysical model personalization within euHeart. Med. Biol. Eng. Comput. 51(11), 1221–1233 (2013)
6. Hadjicharalambous, M., Lee, J., Smith, N., Nordsletten, D.: A displacement-based finite element formulation for incompressible and nearly-incompressible cardiac mechanics. Comput. Method. Appl. M. 274, 213–236 (2014)
7. Hadjicharalambous, M., Asner, L., Chabiniok, R., Sammut, E., Wong, J., Carr- White, G., Razavi, R., Nordsletten, D.: Analysis of cardiac constitutive laws for parameter estimation using 3D tagged MRI. Biomech. Model. Mechan (accepted for publication)
8. Holzapfel, G., Ogden, R.: Constitutive modelling of passive myocardium: a structurally based framework for material characterization. Philos. T. R. Soc. A 367, 3445–3475 (2009)
9. Kerckhoffs, R., Bovendeerd, P., Prinzen, F., Smits, K., Arts, T.: Intra- and interventricular asynchrony of electromechanics in the ventricularly paced heart. J. Eng. Math. 47, 201–216 (2003)
10. Klotz, S., Hay, I., Dickstein, M., Yi, G., Wang, J., Maurer, M., Kass, D., Burkhoff, D.: Single-beat estimation of end-diastolic pressure-volume relationship: a novel method with potential for noninvasive application. Am. J. Physiol.-Heart C 291, H403–H412 (2006)
11. Marchesseau, S., Delingette, H., Sermesant, M., Ayache, N.: Fast parameter calibration of a cardiac electromechanical model from medical images based on the unscented transform. Biomech. Model. Mechan. 12(4), 815–831 (2013)
12. Moireau, P., Chapelle, D.: Reduced-order unscented Kalman filtering with application to parameter identification in large-dimensional systems. ESAIM Contr. Op. Ca. Va. 17(2), 380–405 (2011)
13. Nordsletten, D., Niederer, S., Nash, M., Hunter, P., Smith, N.: Coupling multi-physics models to cardiac mechanics. Prog. Biophys. Mol. Bio. 104, 77–88 (2011)
14. Wang, V., Lam, H., Ennis, D., Cowan, B., Young, A., Nash, M.: Modelling passive diastolic mechanics with quantitative MRI of cardiac structure and function. Med. Image Anal. 13(5), 773–784 (2009)
15. Xi, J., Lamata, P., Niederer, S., Land, S., Shi, W., Zhuang, X., Ourselin, S., Duckett, S., Shetty, A., Rinaldi, A., Rueckert, D., Razavi, R., Smith, N.: The estimation of patient-specific cardiac diastolic functions from clinical measurements. Med. Image Anal. 17(2), 133–146 (2013)