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Estimation of tissue contractility from cardiac cine-MRI using a biomechanical heart model

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Abstract

The objective of this paper is to propose and assess an estimation procedure—based on data assimilation principles—well suited to obtain some regional values of key biophysical parameters in a beating heart model, using actual Cine-MR images. The motivation is twofold: (1) to provide an automatic tool for personalizing the characteristics of a cardiac model in order to achieve predictivity in patient-specific modeling and (2) to obtain some useful information for diagnosis purposes in the estimated quantities themselves. In order to assess the global methodology, we specifically devised an animal experiment in which a controlled infarct was produced and data acquired before and after infarction, with an estimation of regional tissue contractility—a key parameter directly affected by the pathology—performed for every measured stage. After performing a preliminary assessment of our proposed methodology using synthetic data, we then demonstrate a full-scale application by first estimating contractility values associated with 6 regions based on the AHA subdivision, before running a more detailed estimation using the actual AHA segments. The estimation results are assessed by comparison with the medical knowledge of the specific infarct, and with late enhancement MR images. We discuss their accuracy at the various subdivision levels, in the light of the inherent modeling limitations and of the intrinsic information contents featured in the data.

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References

  • Ashikaga H, Mickelsen SR, Ennis DB, Rodriguez I, Kellman P, Wen H, McVeigh ER (2005) Electromechanical analysis of infarct border zone in chronic myocardial infarction. Am J Physiol Heart Circ Physiol 288: H1099–H1105

    Article  Google Scholar 

  • Augenstein KF, Cowan BR, LeGrice IJ, Nielsen PMF, Young AA (2005) Method and apparatus for soft tissue material parameter estimation using tissue tagged magnetic resonance imaging. J Biomech Eng 127(1): 148–157

    Article  Google Scholar 

  • Axel L, Montillo A, Kim D (2005) Tagged magnetic resonance imaging of the heart: a survey. Med Image Anal 9(4): 376–393

    Article  Google Scholar 

  • Baerentzen J, Aanaes H (2005) Signed distance computation using the angle weighted pseudo-normal. IEEE Trans Vis Comput Graph 11(3): 243–253

    Article  Google Scholar 

  • Bathe KJ (1996) Finite Element Procedures. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  • Bensoussan A (1971) Filtrage Optimal des Systèmes Linéaires. Dunod, Paris

    MATH  Google Scholar 

  • Bestel J, Clément F, Sorine M (2001) A biomechanical model of muscle contraction. In: Niessen WJ, Viergever MA (eds) Lectures notes in Computer Science, vol 2208. Springer, Berlin

    Google Scholar 

  • Blum J, Le Dimet F-X, Navon IM (2009) Data assimilation for geophysical fluids. Comput Methods Atmos Ocean 14: 385–441

    MathSciNet  Google Scholar 

  • Bogatyrenko E, Hanebeck U (2010) Simultaneous state and parameter estimation for physics-based tracking of heart surface motion. In: IEEE conference on multisensor fusion and integration for intelligent systems (MFI), 2010, pp 109–114

  • Cerqueira MD, Weissman NJ, Dilsizian V et al (2002) Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart: a statement of healthcare professionals from the cardiac imaging comittee of the council on clinical cardiology of the American Heart Association. Circulation 105: 539–542

    Article  Google Scholar 

  • Chabiniok R, Chapelle D, Lesault PF, Rahmouni A, Deux JF (2009) Validation of a biomechanical heart model using animal data with acute myocardial infarction. In: CI2BM09—MICCAI workshop on cardiovascular interventional imaging and biophysical modelling, London, UK

  • Chapelle D, Fernàndez MA, Gerbeau J-F, Moireau P, Sainte-Marie J, Zenzemi N (2009) Numerical simulation of the electromechanical activity of the heart. In: Proceedings of functional imaging and modeling of the heart 2009 (FIMH’09), vol 5528 of LNCS. pp 357–365

  • Chapelle D, Le Tallec P, Moireau P, Sorine M (2010) An energy-preserving muscle tissue model: formulation and compatible discretizations. Int J Multiscale Comput Eng (in press)

  • Ciarlet PG, Geymonat G (1982) Sur les lois de comportement en élasticité non linéaire. CRAS, Série II 295: 423–426

    MathSciNet  MATH  Google Scholar 

  • Costa KD, Holmes JW, McCulloch AD (2001) Modeling cardiac mechanical properties in three dimensions. Philos Trans R Soc Lond A 359: 1233–1250

    Article  MATH  Google Scholar 

  • Ecabert O, Smith N (2008) euHeart: integrated cardiac care using patient-specific cardiovascular modeling. SPIE Newsroom. doi:10.1117/2.1200804.1126

  • Frey PJ, George PL (2008) Mesh Generation. Wiley, London

    Book  MATH  Google Scholar 

  • Göktepe S, Acharya S, Wong J, Kuhl E (2011) Computational modeling of passive myocardium. Int J Numer Method Biomed Eng 27: 1–12

    Article  MATH  Google Scholar 

  • Holzapfel GA, Ogden RW (2009) Constitutive modelling of passive myocardium: a structurally based framework for material characterization. Philos Trans R Soc A 367: 3445–3475

    Article  MathSciNet  MATH  Google Scholar 

  • Hoteit I, Pham DT, Blum J (2002) A simplified reduced order Kalman filtering and application to altimetric data assimilation in Tropical Pacific. J Mar Syst 36(1–2): 101–127

    Article  Google Scholar 

  • Hu ZH, Metaxas D, Axel L (2003) In vivo strain and stress estimation of the heart left and right ventricles from MRI images. Med Image Anal 7(4): 435–444

    Article  Google Scholar 

  • Humphrey JD (2002) Cardiovascular solid mechanics—cells tissues and organs. Springer, Berlin

    Google Scholar 

  • Hunter P et al (2010) A vision and strategy for the virtual physiological human in 2010 and beyond. Philos Trans R Soc A 368: 2595–2614

    Article  Google Scholar 

  • Huxley AF (1957) Muscle structure and theories of contraction. In: Progress in biophysics and biological chemistry, vol 7. Pergamon press, NY, pp 255–318

  • Imperiale A, Chabiniok R, Moireau P, Chapelle D (2011) Constitutive parameter estimation methodology using tagged-MRI data. In: Proceedings of functional imaging and modeling of the heart 2011 (FIMH’11). Springer, Berlin

  • Julier SJ, Uhlmann JK, Durrant-Whyte HF (1995) A new approach for filtering nonlinear systems. In: Proceedings of the American control conference, vol 3. pp 1628–1632

  • Kalman R (1960) A new approach to linear filtering and prediction problems. Trans ASME J Basic Eng 82: 35–45

    Article  Google Scholar 

  • Kim RJ, Fieno DS, Parrish TD, Harris K, Chen E-L, Simonetti JO, Bundy J, Finn JP, Klocke FJ, Judd RM (1999) Relationship of MRI delayed contrast enhancement to irreversible injury, infarct age, and contractile function. Circulation 100: 1992–2002

    Google Scholar 

  • Le Tallec P (1994) Numerical methods for nonlinear three-dimensional elasticity. In: Ciarlet PG, Lions J-L (eds) Handbook of numerical analysis, vol 3. Elsevier, Amsterdam

  • Liu H, Shi P (2009) Maximum a posteriori strategy for the simultaneous motion and material property estimation of the heart. IEEE Trans Biomed Eng 56(2): 378–389

    Article  Google Scholar 

  • Mansi T, Peyrat J-M, Sermesant M, Delingette H, Blanc J, Boudjemline Y, Ayache N (2009) Physically-constrained diffeomorphic demons for the estimation of 3D myocardium strain from Cine-MRI. In: Proceedings of functional imaging and modeling of the heart 2009 (FIMH’09), vol 5528 of LNCS. Springer, Berlin, pp 201–210

  • Moireau P, Chapelle D (2011) Reduced-order unscented Kalman filtering with application to parameter identification in large-dimensional systems. ESAIM: COCV 17:380–405

    Google Scholar 

  • Moireau P, Chapelle D, Le Tallec P (2008) Joint state and parameter estimation for distributed mechanical systems. Comput Method Appl Mech Eng 197(6–8): 659–677

    Article  MathSciNet  MATH  Google Scholar 

  • Moireau P, Chapelle D, Le Tallec P (2009) Filtering for distributed mechanical systems using position measurements: perspectives in medical imaging. Inverse problems 25(3)

  • Moireau P, Nan X, Astorino M, Figueroa CA, Chapelle D, Taylor CA, Gerbeau J-F (2011) External tissue support and fluid-structure simulation in blood flows. Biomech Model Mechanobiol (in press)

  • Nash MP, Hunter PJ (2000) Computational mechanics of the heart – from tissue structure to ventricular function. J Elast 61: 113–141

    Article  MathSciNet  MATH  Google Scholar 

  • Papademetris X, Sinusas AJ, Dione DP, Constable RT, Duncan JS (2002) Estimation of 3D left ventricular deformation from medical images using biomechanical models. IEEE Trans Med Imaging 21(7): 786–800

    Article  Google Scholar 

  • Peters J, Ecabert O, Meyer C, Kneser R, Weese J (2010) Optimizing boundary detection via simulated search with applications to multi-modal heart segmentation. Med Image Anal 14(70–84)

    Google Scholar 

  • Pham DT (2001) Stochastic methods for sequential data assimilation in strongly nonlinear systems. Mon Weather Rev 129(5): 1194–1207

    Article  MathSciNet  Google Scholar 

  • Raghavan K, Yagle A (1994) Forward and inverse problems in elasticity imaging of soft tissues. IEEE Trans Nucl Sci 41(4): 1639–1648

    Article  Google Scholar 

  • Rivlin RS, Ericksen JL (1955) Stress-deformation relations for isotropic materials. J Ration Mech Anal 4: 323–425

    MathSciNet  MATH  Google Scholar 

  • Sachse FB, Frech R, Werner CD, Dössel O (1999) A model based approach to assignment of myocardial fibre orientation. Comput Cardiol 26: 145–148

    Google Scholar 

  • Sainte-Marie J, Chapelle D, Cimrman R, Sorine M (2006) Modeling and estimation of the cardiac electromechanical activity. Comput Struct 84(28): 1743–1759

    Article  MathSciNet  Google Scholar 

  • Schaerer J, Casta C, Pousin J, Clarysse P (2010) A dynamic elastic model for segmentation and tracking of the heart in MR image sequences. Med Image Anal 14: 738–749

    Article  Google Scholar 

  • Sermesant M, Billet F, Chabiniok R, Mansi T, Chinchapatnam P, Moireau P, Peyrat JM, Rhode K, Ginks M, Lambiase P, Arridge S, Delingette H, Sorine M, Rinaldi A, Chapelle D, Razavi R, Ayache N (2009) Personalised electromechanical model of the heart for the prediction of the acute effects of cardiac resynchronisation therapy. In: Proceedings of functional imaging and modeling of the heart 2009 (FIMH’09), vol 5528 of LNCS. Springer, Berlin, pp 239–248

  • Sermesant M, Moireau P, Camara O, Sainte-Marie J, Andriantsimiavona R, Cimrman R, Hill DL, Chapelle D, Razavi R (2006) Cardiac function estimation from MRI using a heart model and data assimilation: advances and difficulties. Med Image Anal 10(4): 642–656

    Article  Google Scholar 

  • Simon D (2006) Optimal state estimation: Kalman, H ∞, and nonlinear approaches. Wiley, London

    Book  Google Scholar 

  • Sundnes J, Lines GT, Cai X, Nielsen BF, Mardal K-A, Tveito A (2006) Computing the electrical activity in the heart. Springer, Berlin

    MATH  Google Scholar 

  • Toussaint N, Mansi T, Delingette H, Ayache N, Sermesant M (2008) An integrated platform for dynamic cardiac simulation and image processing: application to personalised tetralogy of Fallot simulation. In: Proceedings of Eurographics workshop on visual computing for biomedicine (VCBM). Delft, The Netherlands

  • Toussaint N, Souplet JC, Fillard P (2007) MedINRIA: medical image navigation and research tool by INRIA. In: Proceedings of MICCAI’07 workshop on interaction in medical image analysis and visualization

  • Wang L, Zhang H, Wong KCL, Shi P (2009) A reduced-rank square root filtering framework for noninvasive functional imaging of volumetric cardiac electrical activity. In: Acoustics, speech and signal processing, 2009. IEEE Int Conf ICASSP 2009 pp 533–536

  • Xi J, Lamata P, Lee J, Moireau P, Chapelle D, Smith N (2011) Myocardial transversely isotropic material parameter estimation from in-silico measurements based on reduced-order unscented Kalman filter. J Mech Behav Biomed Mater (in press)

  • Zhang Q (2002) Adaptive observer for MIMO linear time varying systems. IEEE Trans Automat Contr 3: 525–529

    Article  Google Scholar 

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Authors and Affiliations

  1. INRIA, Rocquencourt, B.P. 105, 78153, Le Chesnay, France

    R. Chabiniok, P. Moireau & D. Chapelle

  2. AP-HP Hôpital Henri Mondor, Université Paris-Est Créteil, Créteil, France

    P.-F. Lesault, A. Rahmouni & J.-F. Deux

Authors
  1. R. Chabiniok
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  2. P. Moireau
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  3. P.-F. Lesault
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  4. A. Rahmouni
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  5. J.-F. Deux
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  6. D. Chapelle
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Correspondence to D. Chapelle.

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Chabiniok, R., Moireau, P., Lesault, PF. et al. Estimation of tissue contractility from cardiac cine-MRI using a biomechanical heart model. Biomech Model Mechanobiol 11, 609–630 (2012). https://doi.org/10.1007/s10237-011-0337-8

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  • DOI: https://doi.org/10.1007/s10237-011-0337-8

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