Estimation of Purkinje trees from electro-anatomical mapping of the left ventricle using minimal cost geodesics

The electrical activation of the heart is a complex physiological process that is essential for the understanding of several cardiac dysfunctions, such as ventricular tachycardia (VT). Nowadays, patient-specific activation times on ventricular chambers can be estimated from electro-anatomical maps, providing crucial information to clinicians for guiding cardiac radio-frequency ablation treatment. However, some relevant electrical pathways such as those of the Purkinje system are very difficult to interpret from these maps due to sparsity of data and the limited spatial resolution of the system. We present here a novel method to estimate these fast electrical pathways from the local activations maps (LATs) obtained from electro-anatomical maps. The location of Purkinje-myocardial junctions (PMJs) is estimated considering them as critical points of a distance map defined by the activation maps, and then minimal cost geodesic paths are computed on the ventricular surface between the detected junctions. Experiments to validate the proposed method have been carried out in simplified and realistic simulated data, showing good performance on recovering the main characteristics of simulated Purkinje networks (e.g. PMJs). A feasibility study with real cases of fascicular VT was also performed, showing promising results.This work is partially funded by the Sub-programa de Proyectos de Investigación en Salud Instituto de Salud Carlos III, Spain (FIS - PI11/01709), by Spanish Ministry of Economy and Competitiveness (TIN2011-28067 and TIN2012-35874), and by eTorso project (GV/2013/094) from Generalitat Valenciana. The authors thank specially Dr. Bart Bijnens for its comments and suggestions, and Dr. David Andreu and Dr. Juan Fernandez-Armenta for their assistance in the clinical data collection

Effects of the Purkinje system and cardiac geometry on biventricular pacing: a model study

Heart failure leads to gross cardiac structural changes. While cardiac resynchronization therapy (CRT) is a recognized treatment for restoring synchronous activation, it is not clear how changes in cardiac shape and size affect the electrical pacing therapy. This study used a human heart computer model which incorporated anatomical structures such as myofiber orientation and a Purkinje system (PS) to study how pacing affected failing hearts. The PS was modeled as a tree structure that reproduced its retrograde activation feature. In addition to a normal geometry, two cardiomyopathies were modeled: dilatation and hypertrophy. A biventricular pacing protocol was tested in the context of atrio-ventricular block. The contribution of the PS was examined by removing it, as well as by increasing endocardial conductivity. Results showed that retrograde conduction into the PS was a determining factor for achieving intraventricular synchrony. Omission of the PS led to an overestimate of the degree of electrical dyssynchrony while assessing CRT. The activation patterns for the three geometries showed local changes in the order of activation of the lateral wall in response to the same pacing strategy. These factors should be carefully considered when determining lead placement and optimizing device parameters in clinical practice.This research has been partially funded by the Industrial and Technological Development Center (CDTI) under the CENIT Programme (CDTEAM Project) and the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 224495 (euHeart Project). Dr. R. Sebastian was funded by the Ministerio de Ciencia e Innovacion (Juan de la Cierva program). Dr. B. Bijnens is an ICREA Research Professor at Universitat Pompeu Fabra. Dr. E. Vigmond and P. Boyle were funded by The Natural Sciences and Engineering Research Council of Canada. P. Boyle is supported by the Alberta Ingenuity Fund

Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias

In order to determine the site of origin (SOO) in outflow tract ventricular arrhythmias (OTVAs) before an ablation procedure, several algorithms based on manual identification of electrocardiogram (ECG) features, have been developed. However, the reported accuracy decreases when tested with different datasets. Machine learning algorithms can automatize the process and improve generalization, but their performance is hampered by the lack of large enough OTVA databases. We propose the use of detailed electrophysiological simulations of OTVAs to train a machine learning classification model to predict the ventricular origin of the SOO of ectopic beats. We generated a synthetic database of 12-lead ECGs (2,496 signals) by running multiple simulations from the most typical OTVA SOO in 16 patient-specific geometries. Two types of input data were considered in the classification, raw and feature ECG signals. From the simulated raw 12-lead ECG, we analyzed the contribution of each lead in the predictions, keeping the best ones for the training process. For feature-based analysis, we used entropy-based methods to rank the obtained features. A cross-validation process was included to evaluate the machine learning model. Following, two clinical OTVA databases from different hospitals, including ECGs from 365 patients, were used as test-sets to assess the generalization of the proposed approach. The results show that V2 was the best lead for classification. Prediction of the SOO in OTVA, using both raw signals or features for classification, presented high accuracy values (>0.96). Generalization of the network trained on simulated data was good for both patient datasets (accuracy of 0.86 and 0.84, respectively) and presented better values than using exclusively real ECGs for classification (accuracy of 0.84 and 0.76 for each dataset). The use of simulated ECG data for training machine learning-based classification algorithms is critical to obtain good SOO predictions in OTVA compared to real data alone. The fast implementation and generalization of the proposed methodology may contribute towards its application to a clinical routine.This work has been funded by Generalitat Valenciana Grant AICO/2021/318 (Consolidables 2021) and Grant PID2020-114291RB-I00 funded by MCIN/10.13039/501100011033 and by “ERDF A way of making Europe”

Estimation of electrical pathways finding minimal cost paths from electro-anatomical mapping of the left ventricle

The electrical activation of the heart is a complex physiological process that is essential for the understanding of several cardiac dysfunctions, such as ventricular tachycardia (VT). Nowadays, electro-anatomical mappings of patient-specific activation times on the left ventricle surface can be estimated, providing crucial information to the clinicians for guiding cardiac treatment. However, some electrical pathways of particular interest such as Purkinje or still viable conduction channels are difficult to interpret in these maps. We present here a novel method to find some of these electrical pathways using minimal cost paths computations on surface maps. Experiments to validate the proposed method have been carried out in simulated data, and also in clinical data, showing good performance on recovering the main characteristics of simulated Purkinje trees (e.g. end-terminals) and promising results on a real case of fascicular VT.This work is partially funded by the Sub-programa de Proyectos de Investigación en Salud Instituto de Salud Carlos III, Spain (FIS - PI11/01709), by Spanish Ministry of Science and Innovation (TIN2011-28067), and by eTorso project (2013-001404) from Generalitat de Valencia

In silico pace-mapping: prediction of left vs. right outflow tract origin in idiopathic ventricular arrhythmias with patient-specific electrophysiological simulations

Aims A pre-operative non-invasive identification of the site of origin (SOO) of outflow tract ventricular arrhythmias (OTVAs) is important to properly plan radiofrequency ablation procedures. Although some algorithms based on electrocardiograms (ECGs) have been developed to predict left vs. right ventricular origins, their accuracy is still limited, especially in complex anatomies. The aim of this work is to use patient-specific electrophysiological simulations of the heart to predict the SOO in OTVA patients. Methods and results An in silico pace-mapping procedure was designed and used on 11 heart geometries, generating for each case simulated ECGs from 12 clinically plausible SOO. Subsequently, the simulated ECGs were compared with patient ECG data obtained during the clinical tachycardia using the 12-lead correlation coefficient (12-lead ρ). Left ventricle (LV) vs. right ventricle (RV) SOO was estimated by computing the LV/RV ratio for each patient, obtained by dividing the average 12-lead ρ value of the LV- and RV-SOO simulated ECGs, respectively. Simulated ECGs that had virtual sites close to the ablation points that stopped the arrhythmia presented higher correlation coefficients. The LV/RV ratio correctly predicted LV vs. RV SOO in 10/11 cases; 1.07 vs. 0.93 P < 0.05 for 12-lead ρ. Conclusion The obtained results demonstrate the potential of the developed in silico pace-mapping technique to complement standard ECG for the pre-operative planning of complex ventricular arrhythmias.This work was supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme [MDM-2015-0502]

Estimation of electrical pathways finding minimal cost paths from electro-anatomical mapping of the left ventricle

The electrical activation of the heart is a complex physiological process that is essential for the understanding of several cardiac dysfunctions, such as ventricular tachycardia (VT). Nowadays, electro-anatomical mappings of patient-specific activation times on the left ventricle surface can be estimated, providing crucial information to the clinicians for guiding cardiac treatment. However, some electrical pathways of particular interest such as Purkinje or still viable conduction channels are difficult to interpret in these maps. We present here a novel method to find some of these electrical pathways using minimal cost paths computations on surface maps. Experiments to validate the proposed method have been carried out in simulated data, and also in clinical data, showing good performance on recovering the main characteristics of simulated Purkinje trees (e.g. end-terminals) and promising results on a real case of fascicular VT.This work is partially funded by the Sub-programa de Proyectos de Investigación en Salud Instituto de Salud Carlos III, Spain (FIS - PI11/01709), by Spanish Ministry of Science and Innovation (TIN2011-28067), and by eTorso project (2013-001404) from Generalitat de Valencia

A rule-based method to model myocardial fiber orientation in cardiac biventricular geometries with outflow tracts

Rule-based methods are often used for assigning fiber orientation to cardiac anatomical models. However, existing methods have been developed using data mostly from the left ventricle. As a consequence, fiber information obtained from rule-based methods often does not match histological data in other areas of the heart such as the right ventricle, having a negative impact in cardiac simulations beyond the left ventricle. In this work, we present a rule-based method where fiber orientation is separately modeled in each ventricle following observations from histology. This allows to create detailed fiber orientation in specific regions such as the endocardium of the right ventricle, the interventricular septum and the outflow tracts. We also carried out electrophysiological simulations involving these structures and with different fiber configurations. In particular, we built a modelling pipeline for creating patient-specific volumetric meshes of biventricular geometries, including the outflow tracts, and subsequently simulate the electrical wavefront propagation in outflow tract ventricular arrhythmias with different origins for the ectopic focus. The resulting simulations with the proposed rule-based method showed a very good agreement with clinical parameters such as the 10 ms isochrone ratio in a cohort of nine patients suffering from this type of arrhythmia. The developed modelling pipeline confirms its potential for an in silico identification of the site of origin in outflow tract ventricular arrhythmias before clinical intervention.This work was partially funded by the European Union under the Horizon 2020 Programme for Research, Innovation (grant agreement No. 642676 CardioFunXion)

A Rule-Based method to model myocardial fiber orientation for simulating ventricular outflow tract arrhythmias

Comunicació presentada a: FIMH 2017 9th International Conference, celebrada a Toronto, Canadà, de l'11 al 13 de juny de 2017.Myocardial fiber orientation determines the propagation of electrical waves in the heart and the contraction of cardiac tissue. One common approach for assigning fiber orientation to cardiac anatomi- cal models are Rule-Based Methods (RBM). However, RBM have been developed to assimilate data mostly from the Left Ventricle. In conse- quence, fiber information from RBM does not match with histological data in other areas of the heart, having a negative impact in cardiac simulations beyond the LV. In this work, we present a RBM where fiber orientation is separately modeled in each ventricle following observations from histology. This allows to create detailed fiber orientation in specific regions such as the right ventricle endocardium, the interventricular sep- tum and the outow tracts. Electrophysiological simulations including these anatomical structures were then performed, with patient-specific data of outow tract ventricular arrhythmias (OTVA) cases. A compar- ison between the obtained simulations and electro-anatomical data of these patients confirm the potential for in silico identification of the site of origin in OTVAs before the intervention.This work was partially funded by the European Union under the Horizon 2020 Programme for Research, Innovation (grant agreement No. 642676 CardioFunX- ion

A rule-based method to model myocardial fiber orientation in cardiac biventricular geometries with outflow tracts

Rule-based methods are often used for assigning fiber orientation to cardiac anatomical models. However, existing methods have been developed using data mostly from the left ventricle. As a consequence, fiber information obtained from rule-based methods often does not match histological data in other areas of the heart such as the right ventricle, having a negative impact in cardiac simulations beyond the left ventricle. In this work, we present a rule-based method where fiber orientation is separately modeled in each ventricle following observations from histology. This allows to create detailed fiber orientation in specific regions such as the endocardium of the right ventricle, the interventricular septum and the outflow tracts. We also carried out electrophysiological simulations involving these structures and with different fiber configurations. In particular, we built a modelling pipeline for creating patient-specific volumetric meshes of biventricular geometries, including the outflow tracts, and subsequently simulate the electrical wavefront propagation in outflow tract ventricular arrhythmias with different origins for the ectopic focus. The resulting simulations with the proposed rule-based method showed a very good agreement with clinical parameters such as the 10 ms isochrone ratio in a cohort of nine patients suffering from this type of arrhythmia. The developed modelling pipeline confirms its potential for an in silico identification of the site of origin in outflow tract ventricular arrhythmias before clinical intervention.This work was partially funded by the European Union under the Horizon 2020 Programme for Research, Innovation (grant agreement No. 642676 CardioFunXion

A Rule-Based method to model myocardial fiber orientation for simulating ventricular outflow tract arrhythmias

Comunicació presentada a: FIMH 2017 9th International Conference, celebrada a Toronto, Canadà, de l'11 al 13 de juny de 2017.Myocardial fiber orientation determines the propagation of electrical waves in the heart and the contraction of cardiac tissue. One common approach for assigning fiber orientation to cardiac anatomi- cal models are Rule-Based Methods (RBM). However, RBM have been developed to assimilate data mostly from the Left Ventricle. In conse- quence, fiber information from RBM does not match with histological data in other areas of the heart, having a negative impact in cardiac simulations beyond the LV. In this work, we present a RBM where fiber orientation is separately modeled in each ventricle following observations from histology. This allows to create detailed fiber orientation in specific regions such as the right ventricle endocardium, the interventricular sep- tum and the outow tracts. Electrophysiological simulations including these anatomical structures were then performed, with patient-specific data of outow tract ventricular arrhythmias (OTVA) cases. A compar- ison between the obtained simulations and electro-anatomical data of these patients confirm the potential for in silico identification of the site of origin in OTVAs before the intervention.This work was partially funded by the European Union under the Horizon 2020 Programme for Research, Innovation (grant agreement No. 642676 CardioFunX- ion