Leadless biventricular left bundle and endocardial lateral wall pacing versus left bundle only pacing in left bundle branch block patients

Biventricular endocardial (BIV-endo) pacing and left bundle pacing (LBP) are novel delivery methods for cardiac resynchronization therapy (CRT). Both pacing methods can be delivered through leadless pacing, to avoid risks associated with endocardial or transvenous leads. We used computational modelling to quantify synchrony induced by BIV-endo pacing and LBP through a leadless pacing system, and to investigate how the right-left ventricle (RV-LV) delay, RV lead location and type of left bundle capture affect response. We simulated ventricular activation on twenty-four four-chamber heart meshes inclusive of His-Purkinje networks with left bundle branch block (LBBB). Leadless biventricular (BIV) pacing was simulated by adding an RV apical stimulus and an LV lateral wall stimulus (BIV-endo lateral) or targeting the left bundle (BIV-LBP), with an RV-LV delay set to 5 ms. To test effect of prolonged RV-LV delays and RV pacing location, the RV-LV delay was increased to 35 ms and/or the RV stimulus was moved to the RV septum. BIV-endo lateral pacing was less sensitive to increased RV-LV delays, while RV septal pacing worsened response compared to RV apical pacing, especially for long RV-LV delays. To investigate how left bundle capture affects response, we computed 90% BIV activation times (BIVAT-90) during BIV-LBP with selective and non-selective capture, and left bundle branch area pacing (LBBAP), simulated by pacing 1 cm below the left bundle. Non-selective LBP was comparable to selective LBP. LBBAP was worse than selective LBP (BIVAT-90: 54.2 ± 5.7 ms vs. 62.7 ± 6.5, p < 0.01), but it still significantly reduced activation times from baseline. Finally, we compared leadless LBP with RV pacing against optimal LBP delivery through a standard lead system by simulating BIV-LBP and selective LBP alone with and without optimized atrioventricular delay (AVD). Although LBP alone with optimized AVD was better than BIV-LBP, when AVD optimization was not possible BIV-LBP outperformed LBP alone, because the RV pacing stimulus shortened RV activation (BIVAT-90: 54.2 ± 5.7 ms vs. 66.9 ± 5.1 ms, p < 0.01). BIV-endo lateral pacing or LBP delivered through a leadless system could potentially become an alternative to standard CRT. RV-LV delay, RV lead location and type of left bundle capture affect leadless pacing efficacy and should be considered in future trial designs

Highly scalable parallelization techniques for iterative PDE solvers

Die vorliegende Dissertation untersucht die starke Skalierbarkeit einer Implementierung eines AMG-PCG Lösers für lineare Gleichungssysteme. Es werden Eigenschaften identifiziert, die die Skalierbarkeit des Lösers limitieren und von allgemeinem Interesse sind, da sie entweder auf Eigenschaften typischer Gebietszerlegungen oder auf jene des Algebraischen Mehrgitterverfahrens zurückzuführen sind. Anschließend werden Verbesserungen, die darauf abzielen die identifizierten Limitierungen aufzuheben, vorgestellt und ihre Effektivität analysiert. Sie bestehen im Wesentlichen aus einem, bezüglich der Rechenlast, ausbalancierten Kommunikations-Algorithmus, einer systematischen Umverteilung der Rechenlast des Mehrgitter-Lösers entlang der Gitter-Hierarchie und einer hybriden Parallelisierungs-Strategie von MPI und OpenMP. Die Algorithmen dieser Dissertation wurden in CARP, einem Simulationsprogramm für kardiale Elektromechanik, integriert. Dadurch können aufwendige Simulationen auf physiologisch realistischen Modellen für die Validierung der behandelten Algorithmen verwendet werden. Die Validierung zeigt, dass die besprochenen Verbesserungen die Anzahl der Prozesse, auf denen noch Skalierbarkeit erreicht wird, um eine Größenordnung erhöht haben.The dissertation analyzes the strong parallel scaling properties of a specific parallel implementation of an AMG-PCG solver. It identifies scaling bottlenecks which are of general interest, because they originate from the properties of the solver algorithms themselves and from properties of commonly used domain partitioning methods. Improvements to the original solver implementation are presented and their impact on the solver performance and scalability is analyzed. The improvements consist of a communication algorithm that accumulates subdomain interface values in a balanced way, a redistribution scheme for the Algebraic Multigrid coarse-grid hierarchy and a hybrid MPI-OpenMP parallelization of the computational kernels. The discussed numerical algorithms have been integrated into a cardiac electromechanics simulation framework named CARP. Therefore, the strong scaling properties of the numerical algorithms can be validated with physiologically realistic simulations of cardiac electromechanics. The validation shows that the proposed improvements extend the number of processes that show strong scaling for a given problem by approximately one order of magnitude.von Aurel-Vasile NeicAbweichender Titel laut Übersetzung der Verfasserin/des VerfassersZsfassungen in dt. und in engl. SpracheGraz, Univ., Diss., 2015OeBB(VLID)84800

Algebraic multigrid solver on clusters of CPUs and GPUs

Solvers for elliptic partial differential equations are needed in a wide area of scientific applications. We will present a highly parallel CPU and GPU implementation of a conjugate gradient solver with an algebraic multigrid pre-conditioner in a package called Parallel Toolbox. The solvers operates on fully unstructured discretizations of the PDE. The algorithmic specialities are investigated with respect to many-core architectures and the code is applied to one current application. Benchmark results of computations on clusters of CPUs and GPUs will be presented. They will show that a linear equation system with 25million unknowns can be solved in about 1 second

Anatomically accurate high resolution modeling of human whole heart electromechanics: A strongly scalable algebraic multigrid solver method for nonlinear deformation

AbstractElectromechanical (EM) models of the heart have been used successfully to study fundamental mechanisms underlying a heart beat in health and disease. However, in all modeling studies reported so far numerous simplifications were made in terms of representing biophysical details of cellular function and its heterogeneity, gross anatomy and tissue microstructure, as well as the bidirectional coupling between electrophysiology (EP) and tissue distension. One limiting factor is the employed spatial discretization methods which are not sufficiently flexible to accommodate complex geometries or resolve heterogeneities, but, even more importantly, the limited efficiency of the prevailing solver techniques which is not sufficiently scalable to deal with the incurring increase in degrees of freedom (DOF) when modeling cardiac electromechanics at high spatio-temporal resolution.This study reports on the development of a novel methodology for solving the nonlinear equation of finite elasticity using human whole organ models of cardiac electromechanics, discretized at a high para-cellular resolution. Three patient-specific, anatomically accurate, whole heart EM models were reconstructed from magnetic resonance (MR) scans at resolutions of 220 μm, 440 μm and 880 μm, yielding meshes of approximately 184.6, 24.4 and 3.7 million tetrahedral elements and 95.9, 13.2 and 2.1 million displacement DOF, respectively. The same mesh was used for discretizing the governing equations of both electrophysiology (EP) and nonlinear elasticity. A novel algebraic multigrid (AMG) preconditioner for an iterative Krylov solver was developed to deal with the resulting computational load. The AMG preconditioner was designed under the primary objective of achieving favorable strong scaling characteristics for both setup and solution runtimes, as this is key for exploiting current high performance computing hardware.Benchmark results using the 220 μm, 440 μm and 880 μm meshes demonstrate efficient scaling up to 1024, 4096 and 8192 compute cores which allowed the simulation of a single heart beat in 44.3, 87.8 and 235.3 minutes, respectively. The efficiency of the method allows fast simulation cycles without compromising anatomical or biophysical detail

Efficient computation of electrograms and ECGs in human whole heart simulations using a reaction-eikonal model

Anatomically accurate and biophysically detailed bidomain models of the human heart have proven a powerful tool for gaining quantitative insight into the links between electrical sources in the myocardium and the concomitant current flow in the surrounding medium as they represent their relationship mechanistically based on first principles. Such models are increasingly considered as a clinical research tool with the perspective of being used, ultimately, as a complementary diagnostic modality. An important prerequisite in many clinical modeling applications is the ability of models to faithfully replicate potential maps and electrograms recorded from a given patient. However, while the personalization of electrophysiology models based on the gold standard bidomain formulation is in principle feasible, the associated computational expenses are significant, rendering their use incompatible with clinical time frames. In this study we report on the development of a novel computationally efficient reaction-eikonal (R-E) model for modeling extracellular potential maps and electrograms. Using a biventricular human electrophysiology model, which incorporates a topologically realistic His-Purkinje system (HPS), we demonstrate by comparing against a high-resolution reaction-diffusion (R-D) bidomain model that the R-E model predicts extracellular potential fields, electrograms as well as ECGs at the body surface with high fidelity and offers vast computational savings greater than three orders of magnitude. Due to their efficiency R-E models are ideally suitable for forward simulations in clinical modeling studies which attempt to personalize electrophysiological model features

Anatomically accurate high resolution modeling of human whole heart electromechanics: A strongly scalable algebraic multigrid solver method for nonlinear deformation

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