Abstract
In the last decade, electrophysiological models for in-silico simulations of cardiac electrophysiology have gained much attention in the research field. However, to translate them to clinical uses, the models need personalization based on recordings from the patient. In this work, we explore methodologies for the patient-specific personalization of torso and heart geometric models based on standard clinical cardiac magnetic resonance acquisitions to enable simulations. The inclusion of the torso and its internal structures allows simulations of the human ventricular electrophysiological activity from the ionic level to the body surface potentials and to the electrocardiogram.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Arevalo, H., et al.: Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models. Nature Commun. 7, 11437 (2016)
Zettinig, O., et al.: From medical images to fast computational models of heart electromechanics: an integrated framework towards clinical use. In: Ourselin, S., Rueckert, D., Smith, N. (eds.) FIMH 2013. LNCS, vol. 7945, pp. 249–258. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38899-6_30
Peng, P., et al.: A review of heart chamber segmentation for structural and functional analysis using cardiac MRI. Magn. Reson. Mater. Phys. 29(2), 155–195 (2016)
Heiberg, E., et al.: Design and validation of segment - freely available software for cardiovascular image analysis. BMC Med. Imaging 10(1), 1 (2010)
Schulz-Menger, J., et al.: Standardized image interpretation and post processing in cardiovascular magnetic resonance: Society for cardiovascular magnetic resonance (SCMR) board of trustees task force on standardized post processing. J. Cardiovasc. Magn. Reson. 15(1), 35 (2013)
Prakash, R.: Determination of right ventricular wall thickness in systole and diastole. Echocardiographic and necropsy correlation in 32 patients. Heart 40(11), 1257–1261 (1978)
Villard, B., Zacur, E., Dall’Armellina, E., Grau, V.: Correction of slice misalignment in multi-breath-hold cardiac MRI scans. In: Mansi, T., McLeod, K., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2016. LNCS, vol. 10124, pp. 30–38. Springer, Cham (2017). doi:10.1007/978-3-319-52718-5_4
Villard, B., et al.: Cardiac mesh reconstruction from sparse, heterogeneous contours. In: Valdés Hernández, M., González-Castro, V. (eds.) MIUA 2017. CCIS, vol. 723, pp. 169–181. Springer, Cham (2017)
Zhu, S., et al.: An efficient human model customization method based on orthogonal view monocular photos. Comput. Aided Des. 45(11), 1314–1332 (2013)
Zettinig, O., et al.: Data-driven estimation of cardiac electrical diffusivity from 12-lead ECG signals. Med. Image Anal. 18(8), 1361–1376 (2014)
Gillette, K., et al.: Generation of combined-modality tetrahedral meshes. In: Proceedings CinC 2015 (2015)
Trayanova, N., et al.: How computer simulations of the human heart can improve anti-arrhythmia therapy. J. Physiol. 594(9), 2483–2502 (2016)
Pishchulin, L., et al.: Building statistical shape spaces for 3D human modeling. Pattern Recogn. 67, 276–286 (2017)
Rohr, K., et al.: Landmark-based elastic registration using approximating thin-plate splines. IEEE Trans. Med. Imaging 20(6), 526–534 (2001)
Amberg, B., et al.: Optimal step nonrigid ICP algorithms for surface registration. In: Proceedings IEEE CVPR 2007 (2007)
Geneser, S., et al.: Application of stochastic FEM to study the sensitivity of ECG forward modeling to organ conductivity. IEEE Trans. Biomed. Eng. 55(1), 31–40 (2008)
Keller, D., et al.: Ranking the influence of tissue conductivities on forward-calculated ECGs. IEEE Trans. Biomed. Eng. 57(7), 1568–1576 (2010)
Bernabeu, M., et al.: Shock-induced arrhythmogenesis in the human heart: a computational modelling study. In: Proceedings IEEE EMBS 2010 (2010)
Engwirda, D.: Locally-optimal Delaunay-refinement and optimisation-based mesh generation. Ph.D. thesis, The University of Sydney (2014)
Hang, S.: TetGen, a Delaunay-based quality tetrahedral mesh generator. ACM Trans. Math. Softw. 41(2), 11:1–11:36 (2015)
Pitt-Francis, J., et al.: Chaste: a test-driven approach to software development for biological modelling. Comput. Phys. Commun. 180(12), 2452–2471 (2009)
Cardone-Noott, L., et al.: Human ventricular activation sequence and the simulation of the electrocardiographic QRS complex and its variability in healthy and intraventricular block conditions. EP Europace 18(suppl. 4), iv4–iv15 (2016)
Streeter, D.: Gross Morphology and Fiber Geometry of the Heart. Johns Hopkins Press, Baltimore (1979)
Acknowledgments
EZ acknowledges the Marie Sklodowska-Curie Individual Fellowship from the H2020 EU Framework Programme for Research and Innovation (Proposal No: 655020-DTI4micro-MSCA-IF-EF-ST). AM and BR are supported by BR’s Wellcome Trust Senior Research Fellowship in Basic Biomedical Sciences, the CompBiomed project (grant agreement No 675451) and the NC3R Infrastructure for Impact award (NC/P001076/1). BV acknowledges the support of the RCUK Digital Economy Programme grant number EP/G036861/1 (Oxford Centre for Doctoral Training in Healthcare Innovation). VC was supported by ERACoSysMed through a grant to the project SysAFib - Systems medicine for diagnosis and stratification of atrial fibrillation. RA is supported by a British Heart Foundation Clinical Research Training Fellowship. VG is supported by a BBSRC grant (BB/I012117/1), an EPSRC grant (EP/J013250/1), by BHF New Horizon Grant NH/13/30238 and by the CompBiomed project (grant agreement No 675451).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Zacur, E. et al. (2017). MRI-Based Heart and Torso Personalization for Computer Modeling and Simulation of Cardiac Electrophysiology. In: Cardoso, M., et al. Imaging for Patient-Customized Simulations and Systems for Point-of-Care Ultrasound. BIVPCS POCUS 2017 2017. Lecture Notes in Computer Science(), vol 10549. Springer, Cham. https://doi.org/10.1007/978-3-319-67552-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-67552-7_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67551-0
Online ISBN: 978-3-319-67552-7
eBook Packages: Computer ScienceComputer Science (R0)