Virtual Pulmonary Valve Replacement Interventions with a Personalised Cardiac Electromechanical Model

Abstract

Pulmonary valve replacement (PVR) is a pivotal treatment for patients who suffer from chronic pulmonary valve regurgitations. Two PVR techniques are becoming prevalent: a minimally invasive approach and an open-heart surgery with direct right ventricle volume reduction. However, there is no common agreement about the postoperative outcomes of these PVR techniques and choosing the right therapy for a specific patient remains a clinical challenge. We explore in this paper how image processing algorithms, electromechanical models of the heart and real-time surgical simulation platforms can be adapted and combined together to perform patient-specific simulations of these two PVR therapies. We propose a framework where 1) an electromechanical model of the heart is personalised from clinical MR images and used to simulate the effects of PVR upon the cardiac function; 2) volume reduction surgery is simulated in real-time by interactively cutting, moving and joining parts of the anatomical model. The framework is tested on a young patient. The results are promising and suggest that such advanced biomedical technologies may help in decision support and surgery planning for PVR.

SOFA: A Multi-Model Framework for Interactive Physical Simulation

“…The complete multi-physics model can therefore be successfully implemented on SOFA, the simulation time is short enough so that numerous parameter estimation techniques can be though of. Moreover, several cardiac therapy simulations are made possible with SOFA interactivity, such as Cardiac Resynchronization Therapy or cardiac Radio-Frequency Ablation [20], [23].…”

Methodology for image-based reconstruction of ventricular geometry for patient-specific modeling of cardiac electrophysiology

“…Our method is a modification of the approaches presented by Ringenberg et al and Mansi et al (Mansi et al 2009, Ringenberg et al 2014) who utilized the so-called variational implicit functions (Turk and O’Brien 1999) to reconstruct patient-specific ventricular geometries from Lo-res clinical images. In brief, to reconstruct a surface from n landmark points c j , j = 1,2, …, n , the variational implicit functions method builds a smooth function f in 3D space such that f = 0 at these landmark points.…”

“…We performed the reconstruction separately for the epi- and endocardial left and right ventricles (LV/RV) and then merged the results to create the full ventricular geometry. Compared to the approach by Mansi et al (Mansi et al 2009), we automatically detected the landmark points inside and outside of each contour, rather than manually placing those points. In contrast with the technique by Ringenberg et al (Ringenberg et al 2014), we did not contour the same part of myocardial boundary more than once.…”

“…Indeed, efforts to reconstruct the ventricular geometry based on manual delineation of myocardial boundaries have been undertaken for patient-specific modeling. Specifically, Mansi et al developed a software tool to reconstruct ventricular geometry based on manual landmarking and variational implicit functions interpolation, for patient-specific simulations of pulmonary valve replacement interventions (Mansi et al 2009). Image-based patient-specific cardiac modeling studies referred to earlier (Relan et al 2011, Ashikaga et al 2013, Vadakkumpadan et al 2013a) also used a combination of manual contouring and various interpolation methods for ventricular geometry reconstruction methods.…”

Vers un patient numérique personnalisé pour le diagnostic et la thérapie guidés par l’image

“…Ici encore, le modèle, bien que relativement simple, permet de simuler un mouvement réaliste du coeur, reproduisant notamment le mouvement de torsion caractéristique du muscle. La personnalisation des paramè-tres électriques (principalement la conductivité locale) et mécaniques (principalement la contractilité locale du myocarde) sur des zones correspondant à des territoires vasculaires est encore un sujet de recherche actif, mais des premiers résultats prometteurs sont obtenus à partir de méthodes itératives qui comparent les simulations électriques et méca-niques aux observations (images IRM et électrophysiologie 3D) et font évoluer progressivement les paramètres des modèles pour rapprocher simulations et observations à chaque itération [17,18]. Un modèle cardiaque ainsi personnalisé (Figure 4) permettra de prédire le bénéfice attendu de différentes thérapies, notamment de l'ablation radiofréquence de tissus cardiaques pour traiter certains troubles du rythme.…”