PIEMAP: Personalized Inverse Eikonal Model from cardiac Electro-Anatomical Maps

Electroanatomical mapping, a keystone diagnostic tool in cardiac electrophysiology studies, can provide high-density maps of the local electric properties of the tissue. It is therefore tempting to use such data to better individualize current patient-specific models of the heart through a data assimilation procedure and to extract potentially insightful information such as conduction properties. Parameter identification for state-of-the-art cardiac models is however a challenging task. In this work, we introduce a novel inverse problem for inferring the anisotropic structure of the conductivity tensor, that is fiber orientation and conduction velocity along and across fibers, of an eikonal model for cardiac activation. The proposed method, named PIEMAP, performed robustly with synthetic data and showed promising results with clinical data. These results suggest that PIEMAP could be a useful supplement in future clinical workflows of personalized therapies.Comment: 12 pages, 4 figures, 1 tabl

An Automatic Framework for the Non-rigid Alignment of Electroanatomical Maps and Preoperative Anatomical Scans in Atrial Fibrillation

In atrial fibrillation, electro-anatomical maps (EAM) are used for ablation guidance. Yet, the anatomy reconstructed by the navigation system is known to be poorly accurate. This makes catheter navigation challenging and, as such, might affects ablation’s outcome. To ease navigation, existing systems allow co-registering EAMs with pre-operative MR scans by rigidly matching a set of manual landmarks. Nevertheless, the deformation between the two datasets is highly non-rigid. The aim of this work was therefore to develop a framework for the non-rigid alignment of EAMs and anatomical scans to improve ablation guidance

Validation and optimization of omnipolar technology in ventricular ablation procedures

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutora/Directora: Garre, Paz, Vázquez, SaraThis project aims to improve the effectiveness of ventricular tachycardia (VT) ablation procedures by accurately mapping the reentrant channels responsible for the arrhythmia. The study compares different mapping techniques, including bipolar, orthogonal, and omnipolar signals, using the HD Grid catheter and the EnSite X system. Electro-Anatomical Maps (EAMs) created through these techniques are evaluated for their accuracy in identifying channels by comparing them with Cardiac Magnetic Resonance (CMR) maps. The project's findings demonstrate that the omnipolar map, optimized with specific thresholds, exhibits higher correlation with the CMR map, offering reliable and accurate information about the cardiac tissue. Moreover, the comparisons are done also between layers of the ventricle’s heart, the layer 50 (which is an average of the pixels of layers 10- 50) is identified as the most informative layer, and expanded maximum thresholds allow for a comprehensive understanding of ventricular tissue. The results validate the superiority of the omnipolar technology and emphasize the need for further research and collaboration to advance the field of VT ablation procedures

An automate pipeline for generating fiber orientation and region annotation in patient specific atrial models

Modeling the \u27digital twin\u27 of a patient\u27s heart has gained traction in the last years and helps to understand the pathogenic mechanisms of cardiovascular disease to pave the way for personalized therapies. Although a 3D patient-specific model (PSM) can be obtained from computed tomography (CT) or magnetic resonance imaging (MRI), the fiber orientation of cardiac muscle, which significantly affects the electrophysiological and mechanical characteristics of the heart, can hardly be obtained in vivo. Several approaches have been suggested to solve this problem. However, most of them require a considerable amount of human interaction, which is both time-consuming and a potential source of error. In this work, a highly automated pipeline based on a Laplace-Dirichlet-rule-based method (LDRBM) for annotating fibers and anatomical regions in both atria is introduced. The calculated fiber arrangement was regionally compared with anatomical observations from literature and faithfully reproduced clinical and experimental data

Personalized ablation vs. conventional ablation strategies to terminate atrial fibrillation and prevent recurrence

Aims The long-term success rate of ablation therapy is still sub-optimal in patients with persistent atrial fibrillation (AF), mostly due to arrhythmia recurrence originating from arrhythmogenic sites outside the pulmonary veins. Computational modelling provides a framework to integrate and augment clinical data, potentially enabling the patient-specific identification of AF mechanisms and of the optimal ablation sites. We developed a technology to tailor ablations in anatomical and functional digital atrial twins of patients with persistent AF aiming to identify the most successful ablation strategy. Methods and results Twenty-nine patient-specific computational models integrating clinical information from tomographic imaging and electro-anatomical activation time and voltage maps were generated. Areas sustaining AF were identified by a personalized induction protocol at multiple locations. State-of-the-art anatomical and substrate ablation strategies were compared with our proposed Personalized Ablation Lines (PersonAL) plan, which consists of iteratively targeting emergent high dominant frequency (HDF) regions, to identify the optimal ablation strategy. Localized ablations were connected to the closest non-conductive barrier to prevent recurrence of AF or atrial tachycardia. The first application of the HDF strategy had a success of >98% and isolated only 5–6% of the left atrial myocardium. In contrast, conventional ablation strategies targeting anatomical or structural substrate resulted in isolation of up to 20% of left atrial myocardium. After a second iteration of the HDF strategy, no further arrhythmia episode could be induced in any of the patient-specific models. Conclusion The novel PersonAL in silico technology allows to unveil all AF-perpetuating areas and personalize ablation by leveraging atrial digital twins

Inverse estimation of terminal connections in the cardiac conduction system

Modeling the cardiac conduction system is a challenging problem in the context of computational cardiac electrophysiology. Its ventricular section, the Purkinje system, is responsible for triggering tissue electrical activation at discrete terminal locations, which subsequently spreads throughout the ventricles. In this paper, we present an algorithm that is capable of estimating the location of the Purkinje system triggering points from a set of random measurements on tissue. We present the properties and the performance of the algorithm under controlled synthetic scenarios. Results show that the method is capable of locating most of the triggering points in scenarios with a fair ratio between terminals and measurements. When the ratio is low, the method can locate the terminals with major impact in the overall activation map. Mean absolute errors obtained indicate that solutions provided by the algorithm are useful to accurately simulate a complete patient ventricular activation map

Hidden Markov Models for Activity Detection in Atrial Fibrillation Electrograms

Proceeding of 2020 Computing in Cardiology (CinC 2020), 13-16 September 2020, Rimini, ItalyActivity detection in atrial fibrillation (AF) electrograms (EGMs) is a key concept to understand the mechanisms of this frequent arrhythmia and design new strategies for its treatment. We present a new method that employs Hidden Markov Models (HMMs) to identify activity presence in bipolar EGMs. The method is fully unsupervised and hence it does not require labeled training data. The HMM activity detection method was validated and compared to the non-linear energy operator (NLEO) method for a set of manually annotated EGMs. The HMM performed better than the NLEO and exhibited more robustness in the presence of low voltage fragmented EGMs.This study was supported by grants PI18/01895 from the Instituto de Salud Carlos III, and RD16/0011/0029 Red de Terapia Celular from the Instituto de Salud Carlos III, the projects RTI2018-099655-B-I00; TEC2017-92552-EXP; PID2019-108539RB-C22, Y2018/TCS-4705, and the support of NVIDIA Corporation with the donation of the Titan V GPU used during this research.Publicad