Abstract
Fatal scar-related arrhythmias are caused by an abnormal electrical wave propagation around non conductive scarred tissue and through viable channels of reduced conductivity. Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is the gold-standard procedure used to differentiate the scarred tissue from the healthy, highlighting the dead cells. The border regions responsible for creating the feeble channels are visible as gray zones. Identifying and monitoring (as they may evolve) these areas may predict the risk of arrhythmias that may lead to cardiac arrest. The main goal of this project is the development of a system able to aid the user in the extraction of geometrical and physiological information from LGE images and the replication of myocardial heterogeneities onto a three-dimensional (3D) structure, built by the methods described by our team in another publication, able to undergo electro-physiologic simulations. The system components were developed in MATLAB R2019b the first is a semi-automatic tool, to identify and segment the myocardial scars and gray zones in every two-dimensional (2D) slice of a LGE CMR dataset. The second component takes these results and assembles different sections while setting different conductivity values for each. At this point, the resulting parts are incorporated into the functional 3D model of the left ventricle, and therefore the chosen values and regions can be validated and redefined until a satisfactory result is obtained. As preliminary results we present the first steps of building one functional Left ventricle (LV) model with scarred zones.
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Flett, A.S., et al.: Evaluation of techniques for the quantification of myocardial scar of differing etiology using cardiac magnetic resonance. JACC Cardiovascular Imag. 4(2), 150–156 (2011). https://doi.org/10.1016/j.jcmg.2010.11.015
Gho, J.M.I.H., et al.: A systematic comparison of cardiovascular magnetic resonance and high resolution histological fibrosis quantification in a chronic porcine infarct model. Int. J. Cardiovascular Imag. 33(11), 1797–1807 (2017). https://doi.org/10.1007/s10554-017-1187-y
Ghugre, N.R., et al.: Evolution of gray zone after acute myocardial infarction: influence of microvascular obstruction. J. Cardiovascular Magnetic Reson. 13(S1), 151 (2011). https://doi.org/10.1186/1532-429x-13-s1-p151
Lin, L.Y., et al.: Conductive channels identified with contrast-enhanced MR imaging predict ventricular tachycardia in systolic heart failure. JACC Cardiovascular Imag. 6(11), 1152–1159 (2013). https://doi.org/10.1016/j.jcmg.2013.05.017
Mikami, Y., et al.: Accuracy and reproducibility of semi-automated late gadolinium enhancement quantification techniques in patients with hypertrophic cardiomyopathy. J. Cardiovasc. Magn. Resonance 16(1), 85 (2014). https://doi.org/10.1186/s12968-014-0085-x
Mirams, G.R., et al.: Chaste: an open source C++ library for computational physiology and biology. PLoS Comput. Biol. 9(3), e1002970 (2013). https://doi.org/10.1371/journal.pcbi.1002970
Narciso., M., Sousa., A.I., Crivellaro., F., de Almeida., R.V., Ferreira., A., Vieira., P.: Left ventricle computational model based on patients three-dimensional MRI. In: Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies, vol. 2 BIOIMAGING: BIOIMAGING, pp. 156–163. INSTICC, SciTePress (2020). https://doi.org/10.5220/0008961601560163
Sachse, F.B., Moreno, A.P., Seemann, G., Abildskov, J.A.: A model of electrical conduction in cardiac tissue including fibroblasts. Ann. Biomed. Eng. 37(5), 874–889 (2009). https://doi.org/10.1007/s10439-009-9667-4
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Narciso, M., Ferreira, A., Vieira, P. (2020). Semi-automatic Tool to Identify Heterogeneity Zones in LGE-CMR and Incorporate the Result into a 3D Model of the Left Ventricle. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12132. Springer, Cham. https://doi.org/10.1007/978-3-030-50516-5_21
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DOI: https://doi.org/10.1007/978-3-030-50516-5_21
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