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Field-Based Parameterisation of Cardiac Muscle Structure from Diffusion Tensors

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Functional Imaging and Modeling of the Heart (FIMH 2015)

Abstract

This paper presents a robust method to directly construct parametric representations of myocardial structure using a left ventricular (LV) finite element model customised to diffusion tensors derived from cardiac diffusion tensor magnetic resonance images (DTMRI). This method avoids the need to solve the eigenvector problem, and therefore avoids issues due to ambiguous eigenvector directions, and the non-uniqueness of eigenvectors in regions of isotropic diffusion. Finite element parameters describing the fibre orientations of a geometric model of the LV are directly fitted to diffusion tensors using non-linear least squares optimisation. The method was tested using ex vivo DTMRI data from a Wistar-Kyoto rat and compared against the conventional eigenvector analysis. Close agreement was found in most regions, except at some boundary locations, and in regions with low fractional anisotropy.

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Notes

  1. 1.

    The MathWorks, Inc., Natick, Massachusetts, United States.

  2. 2.

    OpenCMISS-Cmgui application, www.opencmiss.org.

References

  1. Sermesant, M., Chabiniok, R., Chinchapatnam, P., Mansi, T., Billet, F., Moireau, P., Peyrat, J.M., Wong, K., Relan, J., Rhode, K., et al.: Patient-specific electromechanical models of the heart for the prediction of pacing acute effects in CRT: a preliminary clinical validation. Med. Image Anal. 16(1), 201–215 (2012)

    Article  Google Scholar 

  2. Sermesant, M., Delingette, H., Ayache, N.: An electromechanical model of the heart for image analysis and simulation. IEEE Trans. Med. Imaging 25(5), 612–625 (2006)

    Article  Google Scholar 

  3. Vadakkumpadan, F., Gurev, V., Constantino, J., Arevalo, H., Trayanova, N.: Modeling of whole-heart electrophysiology and mechanics: toward patient-specific simulations. In: Kerckhoffs, R.C. (ed.) Patient-Specific Modeling of the Cardiovascular System, pp. 145–165. Springer, New York (2010)

    Chapter  Google Scholar 

  4. Krishnamurthy, A., Villongco, C.T., Chuang, J., Frank, L.R., Nigam, V., Belezzuoli, E., Stark, P., Krummen, D.E., Narayan, S., Omens, J.H., et al.: Patient-specific models of cardiac biomechanics. J. Comput. Phys. 244, 4–21 (2013)

    Article  Google Scholar 

  5. Walker, J.C., Ratcliffe, M.B., Zhang, P., Wallace, A.W., Hsu, E.W., Saloner, D.A., Guccione, J.M.: Magnetic resonance imaging-based finite element stress analysis after linear repair of left ventricular aneurysm. J. Thorac. Cardiovasc. Surg. 135(5), 1094–1102 (2008)

    Article  Google Scholar 

  6. Wang, V.Y., Lam, H., Ennis, D.B., Cowan, B.R., Young, A.A., Nash, M.P.: Modelling passive diastolic mechanics with quantitative MRI of cardiac structure and function. Med. Image Anal. 13(5), 773–784 (2009)

    Article  Google Scholar 

  7. Xi, J., Lamata, P., Niederer, S., Land, S., Shi, W., Zhuang, X., Ourselin, S., Duckett, S.G., Shetty, A.K., Rinaldi, C.A., et al.: The estimation of patient-specific cardiac diastolic functions from clinical measurements. Med. Image Anal. 17(2), 133–146 (2013)

    Article  Google Scholar 

  8. Niederer, S.A., Smith, N.P.: The role of the frank-starling law in the transduction of cellular work to whole organ pump function: a computational modeling analysis. PLoS Comput. Biol. 5(4), e1000371 (2009)

    Article  Google Scholar 

  9. Wang, V.Y., Ennis, D.B., Cowan, B.R., Young, A.A., Nash, M.P.: Myocardial contractility and regional work throughout the cardiac cycle using FEM and MRI. In: Camara, O., Konukoglu, E., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2011. LNCS, vol. 7085, pp. 149–159. Springer, Heidelberg (2012)

    Google Scholar 

  10. Basser, P.J., Mattiello, J., LeBihan, D.: Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson. Ser. B 103(3), 247–254 (1994)

    Article  Google Scholar 

  11. Hsu, E., Muzikant, A., Matulevicius, S., Penland, R., Henriquez, C.: Magnetic resonance myocardial fiber-orientation mapping with direct histological correlation. Am. J. Physiol. Heart Circ. Physiol. 274(5), H1627–H1634 (1998)

    Google Scholar 

  12. Scollan, D.F., Holmes, A., Winslow, R., Forder, J.: Histological validation of myocardial microstructure obtained from diffusion tensor magnetic resonance imaging. Am. J. Physiol. Heart Circ. Physiol. 275(6), H2308–H2318 (1998)

    Google Scholar 

  13. Lekadir, K., Hoogendoorn, C., Pereanez, M., Albà, X., Pashaei, A., Frangi, A.F.: Statistical personalization of ventricular fiber orientation using shape predictors. IEEE Trans. Med. Imaging 33(4), 882–890 (2014)

    Article  Google Scholar 

  14. Toussaint, N., Stoeck, C.T., Schaeffter, T., Kozerke, S., Sermesant, M., Batchelor, P.G.: In vivo human cardiac fibre architecture estimation using shape-based diffusion tensor processing. Med. Image Anal. 17(8), 1243–1255 (2013)

    Article  Google Scholar 

  15. Jones, D.K., Pierpaoli, C.: Confidence mapping in diffusion tensor magnetic resonance imaging tractography using a bootstrap approach. Magn. Reson. Med. 53(5), 1143–1149 (2005)

    Article  Google Scholar 

  16. Jbabdi, S., Bellec, P., Toro, R., Daunizeau, J., Pélégrini-Issac, M., Benali, H.: Accurate anisotropic fast marching for diffusion-based geodesic tractography. J. Biomed. Imaging 2008, 2 (2008)

    Google Scholar 

  17. Bayer, J., Blake, R., Plank, G., Trayanova, N.: A novel rule-based algorithm for assigning myocardial fiber orientation to computational heart models. Ann. Biomed. Eng. 40(10), 2243–2254 (2012)

    Article  Google Scholar 

  18. Streeter, D.D., Spotnitz, H.M., Patel, D.P., Ross, J., Sonnenblick, E.H.: Fiber orientation in the canine left ventricle during diastole and systole. Circ. Res. 24(3), 339–347 (1969)

    Article  Google Scholar 

  19. Vadakkumpadan, F., Arevalo, H., Prassl, A.J., Chen, J., Kickinger, F., Kohl, P., Plank, G., Trayanova, N.: Image-based models of cardiac structure in health and disease. Wiley Interdisc. Rev. Syst. Biol. Med. 2(4), 489–506 (2010)

    Article  Google Scholar 

  20. Basser, P.J., Pierpaoli, C.: Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J. Magn. Reson. 111, 209–219 (1996)

    Article  Google Scholar 

  21. Farrell, J.A., Landman, B.A., Jones, C.K., Smith, S.A., Prince, J.L., van Zijl, P., Mori, S.: Effects of signal-to-noise ratio on the accuracy and reproducibility of diffusion tensor imaging-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5 t. J. Magn. Reson. Imaging 26(3), 756–767 (2007)

    Article  Google Scholar 

  22. Fomovsky, G.M., Rouillard, A.D., Holmes, J.W.: Regional mechanics determine collagen fiber structure in healing myocardial infarcts. J. Mol. Cell. Cardiol. 52(5), 1083–1090 (2012)

    Article  Google Scholar 

  23. LeGrice, I.J., Hunter, P.J., Smaill, B.: Laminar structure of the heart: a mathematical model. Am. J. Physiol. Heart Circ. Physiol. 272, H2466–H2476 (1997)

    Google Scholar 

  24. Christie, G., Bullivant, D., Blackett, S., Hunter, P.J.: Modelling and visualising the heart. Comput. Vis. Sci. 4(4), 227–235 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  25. Bradley, C., Bowery, A., Britten, R., Budelmann, V., Camara, O., Christie, R., Cookson, A., Frangi, A., Gamage, T., Heidlauf, T., Krittian, S., Ladd, D., Little, C., Mithraratne, K., Nash, M., Nickerson, D., Nielsen, P., Nordbø, T., Omholt, S., Pashaei, A., Paterson, D., Rajagopal, V., Reeve, A., Röhrle, O., Safaei, S., Sebastián, R., Steghfer, M., Wu, T., Yu, T., Zhang, H., Hunter, P.: OpenCMISS: a multi-physics & multi-scale computational infrastructure for the VPH/Physiome project. Prog. Biophys. Mol. Biol. 107(1), 32–47 (2011)

    Article  Google Scholar 

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Correspondence to Vicky Y. Wang .

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Freytag, B. et al. (2015). Field-Based Parameterisation of Cardiac Muscle Structure from Diffusion Tensors. In: van Assen, H., Bovendeerd, P., Delhaas, T. (eds) Functional Imaging and Modeling of the Heart. FIMH 2015. Lecture Notes in Computer Science(), vol 9126. Springer, Cham. https://doi.org/10.1007/978-3-319-20309-6_17

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  • DOI: https://doi.org/10.1007/978-3-319-20309-6_17

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  • Online ISBN: 978-3-319-20309-6

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