Assessment of hemodynamic conditions in the aorta following root replacement with composite valve-conduit graft

This paper presents the analysis of detailed hemodynamics in the aortas of four patients following replacement with a composite bio-prosthetic valve-conduit. Magnetic resonance image-based computational models were set up for each patient with boundary conditions comprising subject-specific three-dimensional inflow velocity profiles at the aortic root and central pressure waveform at the model outlet. Two normal subjects were also included for comparison. The purpose of the study was to investigate the effects of the valve-conduit on flow in the proximal and distal aorta. The results suggested that following the composite valve-conduit implantation, the vortical flow structure and hemodynamic parameters in the aorta were altered, with slightly reduced helical flow index, elevated wall shear stress and higher non-uniformity in wall shear compared to normal aortas. Inter-individual analysis revealed different hemodynamic conditions among the patients depending on the conduit configuration in the ascending aorta, which is a key factor in determining post-operative aortic flow. Introducing a natural curvature in the conduit to create a smooth transition between the conduit and native aorta may help prevent the occurrence of retrograde and recirculating flow in the aortic arch, which is particularly important when a large portion or the entire ascending aorta needs to be replaced

An Approach to Catheter Ablation of Cavotricuspid Isthmus Dependent Atrial Flutter

Much of our understanding of the mechanisms of macro re-entrant atrial tachycardia comes from study of cavotricuspid isthmus (CTI) dependent atrial flutter. In the majority of cases, the diagnosis can be made from simple analysis of the surface ECG. Endocardial mapping during tachycardia allows confirmation of the macro re-entrant circuit within the right atrium while, at the same time, permitting curative catheter ablation targeting the critical isthmus of tissue located between the tricuspid annulus and the inferior vena cava. The procedure is short, safe and by demonstration of an electrophysiological endpoint - bidirectional conduction block across the CTI - is associated with an excellent outcome following ablation. It is now fair to say that catheter ablation should be considered as a first line therapy for patients with documented CTI-dependent atrial flutter

Interactive Training System for Interventional Electrocardiology Procedures

International audienceRecent progress in cardiac catheterization and devices al-lowed to develop new therapies for severe cardiac diseases like arrhyth-mias and heart failure. The skills required for such interventions are still very challenging to learn, and typically acquired over several years. Vir-tual reality simulators can reduce this burden by allowing to practice such procedures without consequences on patients. In this paper, we propose the first training system dedicated to cardiac electrophysiology, includ-ing pacing and ablation procedures. Our framework involves an efficient GPU-based electrophysiological model. Thanks to an innovative mul-tithreading approach, we reach high computational performances that allow to account for user interactions in real-time. Based on a scenario of cardiac arrhythmia, we demonstrate the ability of the user-guided simulator to navigate inside vessels and cardiac cavities with a catheter and to reproduce an ablation procedure involving: extra-cellular poten-tial measurements, endocardial surface reconstruction, electrophysiology mapping, radio-frequency (RF) ablation, as well as electrical stimulation. This works is a step towards computerized medical learning curriculum

DeepVoxels: Learning Persistent 3D Feature Embeddings

In this work, we address the lack of 3D understanding of generative neural networks by introducing a persistent 3D feature embedding for view synthesis. To this end, we propose DeepVoxels, a learned representation that encodes the view-dependent appearance of a 3D scene without having to explicitly model its geometry. At its core, our approach is based on a Cartesian 3D grid of persistent embedded features that learn to make use of the underlying 3D scene structure. Our approach combines insights from 3D geometric computer vision with recent advances in learning image-to-image mappings based on adversarial loss functions. DeepVoxels is supervised, without requiring a 3D reconstruction of the scene, using a 2D re-rendering loss and enforces perspective and multi-view geometry in a principled manner. We apply our persistent 3D scene representation to the problem of novel view synthesis demonstrating high-quality results for a variety of challenging scenes.Comment: Video: https://www.youtube.com/watch?v=HM_WsZhoGXw Supplemental material: https://drive.google.com/file/d/1BnZRyNcVUty6-LxAstN83H79ktUq8Cjp/view?usp=sharing Code: https://github.com/vsitzmann/deepvoxels Project page: https://vsitzmann.github.io/deepvoxels

Multimodal Characterization of the Atrial Substrate - Risks and Rewards of Electrogram and Impedance Mapping

The treatment of atrial rhythm disorders such as atrial fibrillation has remained a major challenge predominantly for patients with severely remodeled substrate. Individualized ablation strategies beyond pulmonary vein isolation in combination with real-time assess- ment of ablation lesion formation have been striven for insistently. Current approaches for identifying arrhythmogenic regions predominantly rely on electrogram-based features such as activation time and voltage or electrogram fractionation as a surrogate for tissue pathology. Despite bending every effort, large-scale clinical trials have yielded ambiguous results on the efficacy of various substrate mapping approaches without significant improvement of patient outcomes. This work focuses on enhancing the understanding of electrogram features and local impedance measurements in the atria towards the extraction of clinically relevant and predic- tive substrate characteristics. Features were extracted from intra-atrial electrograms with particular reference to the un- derlying excitation patterns to address morphological alterations caused by structural and functional changes. The noise level of unipolar electrograms was estimated and reduced by tailored filtering to enhance unipolar signal quality. Electrogram features exhibited nar- row distributions for healthy substrate across patients while a wide range was observed for pathologically altered excitation. Additionally, local impedance was investigated as a novel parameter and mapping modality. Having been introduced to the medical device market recently for monitoring ablative lesion formation, initial clinical experiences with local impedance-enabled catheters lack comple- mentary systematic investigations. Confounding factors and the potential for application as a tool for substrate mapping need elucidation. This work pursued a trimodal approach combining in human, in vitro, and in silico experiments to quantitatively understand the effect of distinct ambient conditions on the measured local impedance. Forward simulations of the spread of the electrical field with a finite element approach as well as the application of inverse solution methods to reconstruct tissue conductivity were implemented in silico. Adequate preprocessing steps were developed for measurements in human to eliminate artefacts automatically. Two clinical studies on local impedance as an indicator for ablation lesion formation and on local impedance based substrate mapping were conducted. Local impedance recordings identified both previously ablated and native scar areas irrespective of local excitation. A highly detailed in silico environment for local impedance measurements was validated with in vitro recordings and provided quantitative insights into the influence of changes in clinically relevant scenarios. Inverse reconstruction of relative tissue conductivity yielded promising results in silico. This work demonstrates that local impedance mapping shows great potential to comple- ment electrogram-based substrate mapping. A validated in silico environment for local impedance measurements can facilitate and optimize the development of next generation local impedance-enabled catheters. Conduction velocity, electrogram features, and recon- structed tissue conductivity suggest to be promising candidates for enhancing future clinical mapping systems