Literature DB >> 30721579

A rule-based method to model myocardial fiber orientation in cardiac biventricular geometries with outflow tracts.

Ruben Doste1, David Soto-Iglesias1, Gabriel Bernardino1, Alejandro Alcaine1, Rafael Sebastian2, Sophie Giffard-Roisin3, Maxime Sermesant3, Antonio Berruezo4, Damian Sanchez-Quintana5, Oscar Camara1.   

Abstract

Rule-based methods are often used for assigning fiber orientation to cardiac anatomical models. However, existing methods have been developed using data mostly from the left ventricle. As a consequence, fiber information obtained from rule-based methods often does not match histological data in other areas of the heart such as the right ventricle, having a negative impact in cardiac simulations beyond the left ventricle. In this work, we present a rule-based method where fiber orientation is separately modeled in each ventricle following observations from histology. This allows to create detailed fiber orientation in specific regions such as the endocardium of the right ventricle, the interventricular septum, and the outflow tracts. We also carried out electrophysiological simulations involving these structures and with different fiber configurations. In particular, we built a modeling pipeline for creating patient-specific volumetric meshes of biventricular geometries, including the outflow tracts, and subsequently simulate the electrical wavefront propagation in outflow tract ventricular arrhythmias with different origins for the ectopic focus. The resulting simulations with the proposed rule-based method showed a very good agreement with clinical parameters such as the 10 ms isochrone ratio in a cohort of nine patients suffering from this type of arrhythmia. The developed modeling pipeline confirms its potential for an in silico identification of the site of origin in outflow tract ventricular arrhythmias before clinical intervention.
© 2019 John Wiley & Sons, Ltd.

Entities:  

Keywords:  electrophysiological simulations; fiber orientation; outflow tract; outflow tract ventricular arrhythmia; rule-based method; septum

Mesh:

Year:  2019        PMID: 30721579     DOI: 10.1002/cnm.3185

Source DB:  PubMed          Journal:  Int J Numer Method Biomed Eng        ISSN: 2040-7939            Impact factor:   2.747


  9 in total

1.  Quantification of regional right ventricular strain in healthy rats using 3D spiral cine dense MRI.

Authors:  Zhan-Qiu Liu; Xiaoyan Zhang; Jonathan F Wenk
Journal:  J Biomech       Date:  2019-07-31       Impact factor: 2.712

Review 2.  How personalized heart modeling can help treatment of lethal arrhythmias: A focus on ventricular tachycardia ablation strategies in post-infarction patients.

Authors:  Natalia A Trayanova; Ashish N Doshi; Adityo Prakosa
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2020-01-09

3.  Characteristics and proposed meaning of intrinsic intracardiac electrogram morphology observed during the left bundle branch pacing procedure: A case report.

Authors:  Hao Wu; Longfu Jiang; Jiabo Shen
Journal:  HeartRhythm Case Rep       Date:  2022-04-14

4.  Comparison of interpolation methods of predominant cardiomyocyte orientation from in vivo and ex vivo cardiac diffusion tensor imaging data.

Authors:  Johanna Stimm; Christian Guenthner; Sebastian Kozerke; Christian T Stoeck
Journal:  NMR Biomed       Date:  2021-12-29       Impact factor: 4.478

5.  An Implementation of Patient-Specific Biventricular Mechanics Simulations With a Deep Learning and Computational Pipeline.

Authors:  Renee Miller; Eric Kerfoot; Charlène Mauger; Tevfik F Ismail; Alistair A Young; David A Nordsletten
Journal:  Front Physiol       Date:  2021-09-16       Impact factor: 4.566

6.  Analyzing the Role of Repolarization Gradients in Post-infarct Ventricular Tachycardia Dynamics Using Patient-Specific Computational Heart Models.

Authors:  Eric Sung; Adityo Prakosa; Natalia A Trayanova
Journal:  Front Physiol       Date:  2021-09-30       Impact factor: 4.566

7.  Impact of intraventricular septal fiber orientation on cardiac electromechanical function.

Authors:  Jairo Rodríguez-Padilla; Argyrios Petras; Julie Magat; Jason Bayer; Yann Bihan-Poudec; Dounia El Hamrani; Girish Ramlugun; Aurel Neic; Christoph M Augustin; Fanny Vaillant; Marion Constantin; David Benoist; Line Pourtau; Virginie Dubes; Julien Rogier; Louis Labrousse; Olivier Bernus; Bruno Quesson; Michel Haïssaguerre; Matthias Gsell; Gernot Plank; Valéry Ozenne; Edward Vigmond
Journal:  Am J Physiol Heart Circ Physiol       Date:  2022-03-18       Impact factor: 5.125

8.  Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias.

Authors:  Ruben Doste; Miguel Lozano; Guillermo Jimenez-Perez; Lluis Mont; Antonio Berruezo; Diego Penela; Oscar Camara; Rafael Sebastian
Journal:  Front Physiol       Date:  2022-08-12       Impact factor: 4.755

9.  Inference of ventricular activation properties from non-invasive electrocardiography.

Authors:  Julia Camps; Brodie Lawson; Christopher Drovandi; Ana Minchole; Zhinuo Jenny Wang; Vicente Grau; Kevin Burrage; Blanca Rodriguez
Journal:  Med Image Anal       Date:  2021-06-23       Impact factor: 8.545

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.