MRI-Based Heart and Torso Personalization for Computer Modeling and Simulation of Cardiac Electrophysiology

Zacur, Ernesto; Mincholé, Ana; Villard, Benjamin; Carapella, Valentina; Ariga, Rina; Rodríguez, Blanca; Grau, Vicente

doi:10.1007/978-3-319-67552-7_8

Cited by 7 publications

(12 citation statements)

References 20 publications

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“…Spatial misalignments in slice images and spatial discrepancies between the contours due to acquisitions at different breath holds were corrected by aligning intensity profiles of intersecting slices using a 3D rigid transformation for each image (Villard et al, 2017) (see Figure 1A, Contours alignment). Bi-ventricular geometries were built from the aligned contours using the end-diastole frames from the standard CINE acquisition as in Villard et al (2018) and Zacur et al (2017).…”

Section: Methodsmentioning

confidence: 99%

“…For the construction of the torso geometries, semi-automatic tools were developed and used to delineate the torso skin and lungs (Zacur et al, 2017). In brief, on each subject, the scout images (localizers), as well as, most of the MRI images (SAX and LAX images) with a large enough field of view were used and contoured.…”

Section: Methodsmentioning

confidence: 99%

“…The sparse 3D geometrical information from the torso images is insufficient for the use of classical segmentation to surface methods such as isocontouring tools (Figure 1A, 3-Dimensional torso arrangement). Thus, we developed and applied a methodology to fit a statistical shape model of the human body to the skin contours (Zacur et al, 2017). The torso contours together with subject height, weight and gender information were used to reconstruct a body surface belonging to a learned class of plausible body shapes from the statistical shape model (Pishchulin et al, 2017; Zacur et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

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MRI-Based Computational Torso/Biventricular Multiscale Models to Investigate the Impact of Anatomical Variability on the ECG QRS Complex

et al. 2019

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Aims Patient-to-patient anatomical differences are an important source of variability in the electrocardiogram, and they may compromise the identification of pathological electrophysiological abnormalities. This study aims at quantifying the contribution of variability in ventricular and torso anatomies to differences in QRS complexes of the 12-lead ECG using computer simulations. Methods A computational pipeline is presented that enables computer simulations using human torso/biventricular anatomically based electrophysiological models from clinically standard magnetic resonance imaging (MRI). The ventricular model includes membrane kinetics represented by the biophysically detailed O’Hara Rudy model modified for tissue heterogeneity and includes fiber orientation based on the Streeter rule. A population of 265 torso/biventricular models was generated by combining ventricular and torso anatomies obtained from clinically standard MRIs, augmented with a statistical shape model of the body. 12-lead ECGs were simulated on the 265 human torso/biventricular electrophysiology models, and QRS morphology, duration and amplitude were quantified in each ECG lead for each of the human torso-biventricular models. Results QRS morphologies in limb leads are mainly determined by ventricular anatomy, while in the precordial leads, and especially V1 to V4, they are determined by heart position within the torso. Differences in ventricular orientation within the torso can explain morphological variability from monophasic to biphasic QRS complexes. QRS duration is mainly influenced by myocardial volume, while it is hardly affected by the torso anatomy or position. An average increase of 0.12 ± 0.05 ms in QRS duration is obtained for each cm 3 of myocardial volume across all the leads while it hardly changed due to changes in torso volume. Conclusion Computer simulations using populations of human torso/biventricular models based on clinical MRI enable quantification of anatomical causes of variability in the QRS complex of the 12-lead ECG. The human models presented also pave the way toward their use as testbeds in silico clinical trials.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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MRI-Based Computational Torso/Biventricular Multiscale Models to Investigate the Impact of Anatomical Variability on the ECG QRS Complex

et al. 2019

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“…Vigmond et al [100] for example showed the importance of superior vena cava and pulmonary vein openings in rotors by using a model of the atria built with two spheres containing holes as anatomical elements. However, despite being very useful because of their simplicity, Computational pipeline for ECG simulation from magnetic resonance images through 3D meshes and cellular electrophysiological models (left, from Zacur et al [87]) and the obtained simulation of the cardiac electrical activity (right, from Cardone-Noott et al [13]). Personalized cardiac magnetic resonance images (1) are segmented and preprocessed (2).…”

Section: Ecg Computer Simulationsmentioning

confidence: 99%

Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances

Lyon

Mincholé

Martínez

et al. 2018

J. R. Soc. Interface.

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167

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Widely developed for clinical screening, electrocardiogram (ECG) recordings capture the cardiac electrical activity from the body surface. ECG analysis can therefore be a crucial first step to help diagnose, understand and predict cardiovascular disorders responsible for 30% of deaths worldwide. Computational techniques, and more specifically machine learning techniques and computational modelling are powerful tools for classification, clustering and simulation, and they have recently been applied to address the analysis of medical data, especially ECG data. This review describes the computational methods in use for ECG analysis, with a focus on machine learning and 3D computer simulations, as well as their accuracy, clinical implications and contributions to medical advances. The first section focuses on heartbeat classification and the techniques developed to extract and classify abnormal from regular beats. The second section focuses on patient diagnosis from whole recordings, applied to different diseases. The third section presents real-time diagnosis and applications to wearable devices. The fourth section highlights the recent field of personalized ECG computer simulations and their interpretation. Finally, the discussion section outlines the challenges of ECG analysis and provides a critical assessment of the methods presented. The computational methods reported in this review are a strong asset for medical discoveries and their translation to the clinical world may lead to promising advances.

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“…Generating anatomically accurate 3D surface meshes has shown promising uses in a wide range of applications including cardiac function analysis, interventional guidance and diagnosis [1,2,3]. Personalization of cardiac surfaces in 3D is also the first step required for computational simulations of cardiac electromechanics using the finite element method [4,5,6]. Cardiac MR (CMR) imaging provides accurate shape information of the heart non-invasively [2].…”

Section: Introductionmentioning

confidence: 99%

Ventricle Surface Reconstruction from Cardiac MR Slices Using Deep Learning

Zacur

Schneider

et al. 2019

Functional Imaging and Modeling of the Heart

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MRI-Based Heart and Torso Personalization for Computer Modeling and Simulation of Cardiac Electrophysiology

Cited by 7 publications

References 20 publications

MRI-Based Computational Torso/Biventricular Multiscale Models to Investigate the Impact of Anatomical Variability on the ECG QRS Complex

MRI-Based Computational Torso/Biventricular Multiscale Models to Investigate the Impact of Anatomical Variability on the ECG QRS Complex

Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances

Ventricle Surface Reconstruction from Cardiac MR Slices Using Deep Learning

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