#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Model order reduction for left ventricular mechanics via congruency training


Autoři: Paolo Di Achille aff001;  Jaimit Parikh aff001;  Svyatoslav Khamzin aff002;  Olga Solovyova aff002;  James Kozloski aff001;  Viatcheslav Gurev aff001
Působiště autorů: Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, United States of America aff001;  Ural Federal University, Yekaterinburg, Russia aff002;  Institute of Immunology and Physiology, Ural Branch of the Russian Academy of Sciences (UB RAS), Yekaterinburg, Russia aff003
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0219876

Souhrn

Computational models of the cardiovascular system and specifically heart function are currently being investigated as analytic tools to assist medical practice and clinical trials. To achieve clinical utility, models should be able to assimilate the diagnostic multi-modality data available for each patient and generate consistent representations of the underlying cardiovascular physiology. While finite element models of the heart can naturally account for patient-specific anatomies reconstructed from medical images, optimizing the many other parameters driving simulated cardiac functions is challenging due to computational complexity. With the goal of streamlining parameter adaptation, in this paper we present a novel, multifidelity strategy for model order reduction of 3-D finite element models of ventricular mechanics. Our approach is centered around well established findings on the similarity between contraction of an isolated muscle and the whole ventricle. Specifically, we demonstrate that simple linear transformations between sarcomere strain (tension) and ventricular volume (pressure) are sufficient to reproduce global pressure-volume outputs of 3-D finite element models even by a reduced model with just a single myocyte unit. We further develop a procedure for congruency training of a surrogate low-order model from multi-scale finite elements, and we construct an example of parameter optimization based on medical images. We discuss how the presented approach might be employed to process large datasets of medical images as well as databases of echocardiographic reports, paving the way towards application of heart mechanics models in the clinical practice.

Klíčová slova:

Simulation and modeling – Magnetic resonance imaging – Muscle cells – Hemodynamics – Heart failure – Ejection fraction – Cardiac ventricles – Finite element analysis


Zdroje

1. Kayvanpour E, Mansi T, Sedaghat-Hamedani F, Amr A, Neumann D, Georgescu B, et al. Towards personalized cardiology: multi-scale modeling of the failing heart. PLoS One. 2015;10(7):e0134869. doi: 10.1371/journal.pone.0134869 26230546

2. Chabiniok R, Wang VY, Hadjicharalambous M, Asner L, Lee J, Sermesant M, et al. Multiphysics and Multiscale Modelling, Data–Model Fusion and Integration of Organ Physiology in the Clinic: Ventricular Cardiac Mechanics. Interface Focus. 2016;6(2):20150083. doi: 10.1098/rsfs.2015.0083 27051509

3. Marchesseau S, Delingette H, Sermesant M, Cabrera-Lozoya R, Tobon-Gomez C, Moireau P, et al. Personalization of a cardiac electromechanical model using reduced order unscented Kalman filtering from regional volumes. Medical image analysis. 2013;17(7):816–829. doi: 10.1016/j.media.2013.04.012 23707227

4. Okada Ji, Washio T, Nakagawa M, Watanabe M, Kadooka Y, Kariya T, et al. Multi-scale, tailor-made heart simulation can predict the effect of cardiac resynchronization therapy. Journal of molecular and cellular cardiology. 2017;108:17–23. doi: 10.1016/j.yjmcc.2017.05.006

5. Baillargeon B, Rebelo N, Fox DD, Taylor RL, Kuhl E. The Living Heart Project: A Robust and Integrative Simulator for Human Heart Function. European Journal of Mechanics—A/Solids. 2014;48:38–47. doi: 10.1016/j.euromechsol.2014.04.001

6. Pfaller MR, Hörmann JM, Weigl M, Nagler A, Chabiniok R, Bertoglio C, et al. The Importance of the Pericardium for Cardiac Biomechanics: From Physiology to Computational Modeling. 2018.

7. Moin DS, Sackheim J, Hamo CE, Butler J. Cardiac Myosin Activators in Systolic Heart Failure: More Friend than Foe? Curr Cardiol Rep. 2016;18(10):100. doi: 10.1007/s11886-016-0778-x 27568794

8. Teerlink JR, Felker GM, McMurray JJV, Solomon SD, Adams KF, Cleland JGF, et al. Chronic Oral Study of Myosin Activation to Increase Contractility in Heart Failure (COSMIC-HF): A Phase 2, Pharmacokinetic, Randomised, Placebo-Controlled Trial. Lancet. 2016;388(10062):2895–2903. doi: 10.1016/S0140-6736(16)32049-9 27914656

9. Teerlink JR, Felker GM, McMurray JJV, Ponikowski P, Metra M, Filippatos GS, et al. Acute Treatment With Omecamtiv Mecarbil to Increase Contractility in Acute Heart Failure: The ATOMIC-AHF Study. J Am Coll Cardiol. 2016;67(12):1444–1455. doi: 10.1016/j.jacc.2016.01.031 27012405

10. Xiu D. Numerical Methods for Stochastic Computations: A Spectral Method Approach. Princeton University Press; 2010.

11. Lamata P, Casero R, Carapella V, Niederer SA, Bishop MJ, Schneider JE, et al. Images as Drivers of Progress in Cardiac Computational Modelling. Progress in Biophysics and Molecular Biology. 2014;115(2):198–212. doi: 10.1016/j.pbiomolbio.2014.08.005 25117497

12. Niederer SA, Lumens J, Trayanova NA. Computational Models in Cardiology. Nature Reviews Cardiology. 2018; p. 1.

13. Arts T, Delhaas T, Bovendeerd P, Verbeek X, Prinzen FW. Adaptation to Mechanical Load Determines Shape and Properties of Heart and Circulation: The CircAdapt Model. American Journal of Physiology-Heart and Circulatory Physiology. 2005;288(4):H1943–H1954. doi: 10.1152/ajpheart.00444.2004 15550528

14. Lumens J, Delhaas T. Cardiovascular Modeling in Pulmonary Arterial Hypertension: Focus on Mechanisms and Treatment of Right Heart Failure Using the CircAdapt Model. The American Journal of Cardiology. 2012;110(6, Supplement):S39–S48. doi: 10.1016/j.amjcard.2012.06.015

15. Moulton MJ, Secomb TW. A Low-Order Parametric Description of Left Ventricular Kinematics. Cardiovasc Eng Tech. 2014;5(4):348–358. doi: 10.1007/s13239-014-0191-9

16. Moulton MJ, Hong BD, Secomb TW. Simulation of Left Ventricular Dynamics Using a Low-Order Mathematical Model. Cardiovasc Eng Tech. 2017;8(4):480–494. doi: 10.1007/s13239-017-0327-9

17. Berkooz G, Holmes P, Lumley JL. The Proper Orthogonal Decomposition in the Analysis of Turbulent Flows. Annual Review of Fluid Mechanics. 1993;25(1):539–575. doi: 10.1146/annurev.fl.25.010193.002543

18. Chapelle D, Gariah A, Sainte-Marie J. Galerkin Approximation with Proper Orthogonal Decomposition: New Error Estimates and Illustrative Examples. ESAIM: Mathematical Modelling and Numerical Analysis. 2012;46(4):731–757. doi: 10.1051/m2an/2011053

19. Rama RR, Skatulla S, Sansour C. Real-Time Modelling of Diastolic Filling of the Heart Using the Proper Orthogonal Decomposition with Interpolation. International Journal of Solids and Structures. 2016;96:409–422. doi: 10.1016/j.ijsolstr.2016.04.003

20. Pfaller MR, Varona MC, Lang J, Bertoglio C, Wall WA. Parametric Model Order Reduction and Its Application to Inverse Analysis of Large Nonlinear Coupled Cardiac Problems. 2018.

21. Rozza G, Huynh DBP, Patera AT. Reduced Basis Approximation and a Posteriori Error Estimation for Affinely Parametrized Elliptic Coercive Partial Differential Equations. ARCO. 2007;15(3):1. doi: 10.1007/BF03024948

22. Quarteroni A, Rozza G, Manzoni A. Certified Reduced Basis Approximation for Parametrized Partial Differential Equations and Applications. Journal of Mathematics in Industry. 2011;1(1):3. doi: 10.1186/2190-5983-1-3

23. Ryckelynck D. Hyper-Reduction of Mechanical Models Involving Internal Variables. International Journal for Numerical Methods in Engineering. 2009;77(1):75–89. doi: 10.1002/nme.2406

24. Hong BD, Moulton MJ, Secomb TW. Modeling Left Ventricular Dynamics with Characteristic Deformation Modes. Biomech Model Mechanobiol. 2019. doi: 10.1007/s10237-019-01168-8 31129860

25. Rasmussen CE, Williams CK. Gaussian Processes for Machine Learning. vol. 1. MIT press Cambridge; 2006.

26. Nasopoulou A, Shetty A, Lee J, Nordsletten D, Rinaldi CA, Lamata P, et al. Improved Identifiability of Myocardial Material Parameters by an Energy-Based Cost Function. Biomech Model Mechanobiol. 2017;16(3):971–988. doi: 10.1007/s10237-016-0865-3 28188386

27. Di Achille P, Harouni A, Khamzin S, Solovyova O, Rice JJ, Gurev V. Gaussian Process Regressions for Inverse Problems and Parameter Searches in Models of Ventricular Mechanics. Front Physiol. 2018;9. doi: 10.3389/fphys.2018.01002 30154725

28. Booker AJ, Dennis JE, Frank PD, Serafini DB, Torczon V, Trosset MW. A rigorous framework for optimization of expensive functions by surrogates. Structural Optimization. 1999;17(1):1–13. doi: 10.1007/BF01197708

29. Marsden AL, Feinstein JA, Taylor CA. A computational framework for derivative-free optimization of cardiovascular geometries. Computer Methods in Applied Mechanics and Engineering. 2008;197(21):1890–1905. doi: 10.1016/j.cma.2007.12.009

30. Perdikaris P, Venturi D, Royset JO, Karniadakis GE. Multi-Fidelity Modelling via Recursive Co-Kriging and Gaussian–Markov Random Fields. In: Proc. R. Soc. A. vol. 471. The Royal Society; 2015. p. 20150018.

31. Sahli Costabal F, Matsuno K, Yao J, Perdikaris P, Kuhl E. Machine Learning in Drug Development: Characterizing the Effect of 30 Drugs on the QT Interval Using Gaussian Process Regression, Sensitivity Analysis, and Uncertainty Quantification. Computer Methods in Applied Mechanics and Engineering. 2019;348:313–333. doi: 10.1016/j.cma.2019.01.033

32. Radau P, Lu Y, Connelly K, Paul G, Dick AJ, Wright GA. Evaluation Framework for Algorithms Segmenting Short Axis Cardiac MRI. The Midas Journal. 2009.

33. Gurev V, Pathmanathan P, Fattebert JL, Wen HF, Magerlein J, Gray RA, et al. A High-Resolution Computational Model of the Deforming Human Heart. Biomech Model Mechanobiol. 2015;14(4):829–849. doi: 10.1007/s10237-014-0639-8 25567753

34. Usyk TP, Mazhari R, McCulloch AD. Effect of Laminar Orthotropic Myofiber Architecture on Regional Stress and Strain in the Canine Left Ventricle. Journal of Elasticity. 2000;61(1-3):143–164. doi: 10.1023/A:1010883920374

35. Young RJ, Panfilov AV. Anisotropy of Wave Propagation in the Heart Can Be Modeled by a Riemannian Electrophysiological Metric. Proceedings of the National Academy of Sciences. 2010;107(34):15063–15068. doi: 10.1073/pnas.1008837107

36. Durrer D, Van Dam RT, Freud GE, Janse MJ, Meijler FL, Arzbaecher RC. Total Excitation of the Isolated Human Heart. Circulation. 1970;41(6):899–912. doi: 10.1161/01.cir.41.6.899 5482907

37. Abramson M, Audet C, Dennis J, Digabel S. OrthoMADS: A Deterministic MADS Instance with Orthogonal Directions. SIAM J Optim. 2009;20(2):948–966. doi: 10.1137/080716980

38. Konhilas JP, Irving TC, de Tombe PP. Frank-Starling Law of the Heart and the Cellular Mechanisms of Length-Dependent Activation. Pflugers Arch—Eur J Physiol. 2002;445(3):305–310. doi: 10.1007/s00424-002-0902-1

39. Gomez JF, Cardona K, Trenor B. Lessons Learned from Multi-Scale Modeling of the Failing Heart. Journal of Molecular and Cellular Cardiology. 2015;89:146–159. doi: 10.1016/j.yjmcc.2015.10.016 26476237

40. Braunwald E. The War against Heart Failure: The Lancet Lecture. The Lancet. 2015;385(9970):812–824. doi: 10.1016/S0140-6736(14)61889-4


Článok vyšiel v časopise

PLOS One


2020 Číslo 1
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Získaná hemofilie - Povědomí o nemoci a její diagnostika
nový kurz

Eozinofilní granulomatóza s polyangiitidou
Autori: doc. MUDr. Martina Doubková, Ph.D.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#