Electrophysiological Simulations Research Articles

A hydrophobic funnel governs monovalent cation selectivity in the ion channel TRPM5

A key capability of ion channels is the facilitation of selective permeation of certain ionic species across cellular membranes at high rates. Due to their physiological significance, ion channels are of great pharmaceutical interest as drug targets. The polymodal signal-detecting transient receptor potential (TRP) superfamily of ion channels forms a particularly promising group of drug targets. While most members of this family permeate a broad range of cations including Ca2+, TRPM4 and TRPM5 are unique due to their strong monovalent selectivity and impermeability for divalent cations. Here, we investigated the mechanistic basis for their unique monovalent selectivity by in silico electrophysiology simulations of TRPM5. Our simulations reveal an unusual mechanism of cation selectivity, which is underpinned by the function of the central channel cavity alongside the selectivity filter. Our results suggest that a subtle hydrophobic barrier at the cavity entrance (“hydrophobic funnel”) enables monovalent but not divalent cations to pass and occupy the cavity at physiologically relevant membrane voltages. Monovalent cations then permeate efficiently by a cooperative, distant knockon mechanism between two binding regions in the extracellular pore vestibule and the central cavity. By contrast, divalent cations do not enter or interact favorably with the channel cavity due to its raised hydrophobicity. Hydrophilic mutations in the transition zone between the selectivity filter and the central channel cavity abolish the barrier for divalent cations, enabling both monovalent and divalent cations to traverse TRPM5.

A simulation study on the antiarrhythmic mechanisms of established agents in myocardial ischemia and infarction.

Patients with myocardial ischemia and infarction are at increased risk of arrhythmias, which in turn, can exacerbate the overall risk of mortality. Despite the observed reduction in recurrent arrhythmias through antiarrhythmic drug therapy, the precise mechanisms underlying their effectiveness in treating ischemic heart disease remain unclear. Moreover, there is a lack of specialized drugs designed explicitly for the treatment of myocardial ischemic arrhythmia. This study employs an electrophysiological simulation approach to investigate the potential antiarrhythmic effects and underlying mechanisms of various pharmacological agents in the context of ischemia and myocardial infarction (MI). Based on physiological experimental data, computational models are developed to simulate the effects of a series of pharmacological agents (amiodarone, telmisartan, E-4031, chromanol 293B, and glibenclamide) on cellular electrophysiology and utilized to further evaluate their antiarrhythmic effectiveness during ischemia. On 2D and 3D tissues with multiple pathological conditions, the simulation results indicate that the antiarrhythmic effect of glibenclamide is primarily attributed to the suppression of efflux of potassium ion to facilitate the restitution of [K+]o, as opposed to recovery of IKATP during myocardial ischemia. This discovery implies that, during acute cardiac ischemia, pro-arrhythmogenic alterations in cardiac tissue's excitability and conduction properties are more significantly influenced by electrophysiological changes in the depolarization rate, as opposed to variations in the action potential duration (APD). These findings offer specific insights into potentially effective targets for investigating ischemic arrhythmias, providing significant guidance for clinical interventions in acute coronary syndrome.

Digital twinning of cardiac electrophysiology for congenital heart disease.

In recent years, blending mechanistic knowledge with machine learning has had a major impact in digital healthcare. In this work, we introduce a computational pipeline to build certified digital replicas of cardiac electrophysiology in paediatric patients with congenital heart disease. We construct the patient-specific geometry by means of semi-automatic segmentation and meshing tools. We generate a dataset of electrophysiology simulations covering cell-to-organ level model parameters and using rigorous mathematical models based on differential equations. We previously proposed Branched Latent Neural Maps (BLNMs) as an accurate and efficient means to recapitulate complex physical processes in a neural network. Here, we employ BLNMs to encode the parametrized temporal dynamics of in silico 12-lead electrocardiograms (ECGs). BLNMs act as a geometry-specific surrogate model of cardiac function for fast and robust parameter estimation to match clinical ECGs in paediatric patients. Identifiability and trustworthiness of calibrated model parameters are assessed by sensitivity analysis and uncertainty quantification.

Effects of different types of fibrotic remodelling in areas of low voltage on atrial fibrillation dynamics: an in-silico study

Abstract Background Personalized computational models include the effects of personalised anatomy and atrial fibrillation (AF) remodelling in a framework that can be used to investigate patient-specific AF mechanisms and test treatment response, but modelling fibrosis remains an unresolved matter. The interpretation of regions exhibiting low voltage in electroanatomic mapping data at different pacing rates also remain unclear. However, these occurrences are correlated with fibrotic remodelling. We aimed to use patient-specific simulations to investigate the effects of fibrosis type on AF properties by modelling conduction velocity slowing, changes in ionic channels, and replacement fibrosis, incorporated in regions of low voltage identified through omnipolar mapping at different pacing rates. Methods Local activation times (LATs) voltage and geometry data were obtained from patients undergoing ablation for persistent AF. LATs were collected in sinus rhythm with coronary sinus pacing at pacing interval (PIs) of 250ms and 600ms. For a total of 10 cases, personalised anatomical models were constructed for electrophysiology simulations, and analysed using an automated pipeline in python. For conductivity models, areas with peak omnipolar voltage (OV) below 0.5mV were assigned low conduction velocity. To incorporate ionic changes, the ionic properties were altered in fibrotic regions (peak OV < 0.5mV). In the simulations where replacement fibrosis was included through a probabilistic percolation method, percolation was modelled by using random number generator to probabilistically remove elements of low voltage. Pulmonary vein isolation was simulated in each model after 5s of AF, and wavefront propagation patterns were analysed to quantify the number of rotational areas or areas of wavefront break-up in 7 anatomical segments. Results The models with conductivity fibrotic remodelling demonstrated rotational drivers in more than two LA anatomical segments for both CS250ms pacing rate and CS600ms pacing rate across all 10 cases. The anterior wall exhibited the largest degree of rotational activity in 7 out of 10 cases for CS250ms pacing rate and in 9 out of 10 cases for CS600ms pacing rate (Figure A). The combination of percolation and conductivity remodelling also exhibited rotational activity on the anterior wall in 9 out of 10 cases at CS250ms pacing rate, and 7 out of 10 cases for CS600ms pacing rate. For the simulations where replacement fibrosis was included (percolation), both the roof and anterior wall exhibited the largest degree of rotational activity in 5 out of 10 cases at CS250ms pacing rate; while at CS600ms pacing rate, the posterior walled exhibited the majority of rotational activity in 7 cases (Figure B). All fibrosis models containing ionic changes terminated in more than half of the 10 cases for both pacing rates (CS250ms and 600). Conclusion The impact of low voltage on fibrotic remodelling causes complex effects on AF dynamics.

ForCEPSS—A framework for cardiac electrophysiology simulations standardization

Background and Objective:Simulation of cardiac electrophysiology (CEP) is an important research tool that is increasingly being adopted in industrial and clinical applications. Typical workflows for CEP simulation consist of a sequence of processing stages starting with building an anatomical model and then calibrating its electrophysiological properties to match observable data. While the calibration stages are common and generalizable, most CEP studies re-implement these steps in complex and highly variable workflows. This lack of standardization renders the execution of computational CEP studies in an efficient, robust, and reproducible manner a significant challenge. Here, we propose ForCEPSS as an efficient and robust, yet flexible, software framework for standardizing CEP simulation studies. Methods and Results:Key processing stages of CEP simulation studies are identified and implemented in a standardized workflow that builds on openCARP11https://opencarp.org/. Plank et al. (2021) and the Python-based carputils22https://git.opencarp.org/openCARP/carputils. framework. Stages include (i) the definition and initialization of action potential phenotypes, (ii) the tissue scale calibration of conduction properties, (iii) the functional initialization to approximate a limit cycle corresponding to the dynamic reference state according to an experimental protocol, and, (iv) the execution of the CEP study where the electrophysiological response to a perturbation of the limit cycle is probed. As an exemplar application, we employ ForCEPSS to prepare a CEP study according to the Virtual Arrhythmia Risk Prediction protocol used for investigating the arrhythmogenic risk of developing infarct-related ventricular tachycardia (VT) in ischemic cardiomyopathy patients. We demonstrate that ForCEPSS enables a fully automated execution of all stages of this complex protocol. Conclusion:ForCEPSS offers a novel comprehensive, standardized, and automated CEP simulation workflow. The high degree of automation accelerates the execution of CEP simulation studies, reduces errors, improves robustness, and makes CEP studies reproducible. Verification of simulation studies within the CEP modeling community is thus possible. As such, ForCEPSS makes an important contribution towards increasing transparency, standardization, and reproducibility of in silico CEP experiments.

A comparison of Algebraic Multigrid Bidomain solvers on hybrid CPU–GPU architectures

The numerical simulation of cardiac electrophysiology is a highly challenging problem in scientific computing. The Bidomain system is the most complete mathematical model of cardiac bioelectrical activity. It consists of an elliptic and a parabolic partial differential equation (PDE), of reaction–diffusion type, describing the spread of electrical excitation in the cardiac tissue. The two PDEs are coupled with a stiff system of ordinary differential equations (ODEs), representing ionic currents through the cardiac membrane. Developing efficient and scalable preconditioners for the linear systems arising from the discretization of such computationally challenging model is crucial in order to reduce the computational costs required by the numerical simulations of cardiac electrophysiology. In this work, focusing on the Bidomain system as a model problem, we have benchmarked two popular implementations of the Algebraic Multigrid (AMG) preconditioner embedded in the PETSc library and we have studied the performance on the calibration of specific parameters. We have conducted our analysis on modern HPC architectures, performing scalability tests on multi-core and multi-GPUs settings. The results have shown that, for our problem, although scalability is verified on CPUs, GPUs are the optimal choice, since they yield the best performance in terms of solution time.

Computational Modeling and Simulation of the Fibrotic Human Atria.

Patient-specific modeling of atrial electrical activity enables the execution of simulations that can provide mechanistic insights and provide novel solutions to vexing clinical problems. The geometry and fibrotic remodeling of the heart can be reconstructed from clinical-grade medical scans and used to inform personalized models with detail incorporated at the cell- and tissue-scale to represent changes in image-identified diseased regions. Here, we provide a rubric for the reconstruction of realistic atrial models from pre-segmented 3D renderings of the left atrium with fibrotic tissue regions delineated, which are the output from clinical-grade systems for quantifying fibrosis. We then provide a roadmap for using those models to carry out patient-specific characterization of the fibrotic substrate in terms of its potential to harbor reentrant drivers via cardiac electrophysiology simulations.

Cardiac Electrophysiology Meshfree Modeling through the Mixed Collocation Method

We present the meshfree mixed collocation method (MCM) for cardiac electrophysiology simulation. Capitalizing on the meshfree property of MCM, we introduce an immersed grid approach for automated generation of meshfree node grids from medical image data. This approach allows us to avoid the time-consuming mesh generation and processing that mesh-based methods like the finite element method (FEM) require. We employ the MCM to solve the cardiac monodomain model considering electrical propagation in 2D tissue sheets, 3D tissue slabs, and a realistic biventricular anatomy. We demonstrate that the solutions obtained by the MCM are in good agreement with the FEM, particularly when immersed grid is used. These findings confirm the suitability of the MCM for cardiac electrophysiology simulation and make the MCM a promising alternative to the FEM for cardiac electrical investigations.

Lifex-ep: a robust and efficient software for cardiac electrophysiology simulations

BackgroundSimulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors of various research teams, comprehensive and user-friendly software programs for cardiac simulations, capable of accurately replicating both normal and pathological conditions, are still in the process of achieving full maturity within the scientific community.ResultsThis work introduces texttt {life}^{text{x}}-ep, a publicly available software for numerical simulations of the electrophysiology activity of the cardiac muscle, under both normal and pathological conditions. texttt {life}^{text{x}}-ep employs the monodomain equation to model the heart’s electrical activity. It incorporates both phenomenological and second-generation ionic models. These models are discretized using the Finite Element method on tetrahedral or hexahedral meshes. Additionally, texttt {life}^{text{x}}-ep integrates the generation of myocardial fibers based on Laplace–Dirichlet Rule-Based Methods, previously released in Africa et al., 2023, within texttt {life}^{text{x}}-fiber. As an alternative, users can also choose to import myofibers from a file. This paper provides a concise overview of the mathematical models and numerical methods underlying texttt {life}^{text{x}}-ep, along with comprehensive implementation details and instructions for users. texttt {life}^{text{x}}-ep features exceptional parallel speedup, scaling efficiently when using up to thousands of cores, and its implementation has been verified against an established benchmark problem for computational electrophysiology. We showcase the key features of texttt {life}^{text{x}}-ep through various idealized and realistic simulations conducted in both normal and pathological scenarios. Furthermore, the software offers a user-friendly and flexible interface, simplifying the setup of simulations using self-documenting parameter files.Conclusionstexttt {life}^{text{x}}-ep provides easy access to cardiac electrophysiology simulations for a wide user community. It offers a computational tool that integrates models and accurate methods for simulating cardiac electrophysiology within a high-performance framework, while maintaining a user-friendly interface. texttt {life}^{text{x}}-ep represents a valuable tool for conducting in silico patient-specific simulations.

Branched Latent Neural Maps

We introduce Branched Latent Neural Maps (BLNMs) to learn finite dimensional input–output maps encoding complex physical processes. A BLNM is defined by a simple and compact feedforward partially-connected neural network that structurally disentangles inputs with different intrinsic roles, such as the time variable from model parameters of a differential equation, while transferring them into a generic field of interest. BLNMs leverage latent outputs to enhance the learned dynamics and break the curse of dimensionality by showing excellent in-distribution generalization properties with small training datasets and short training times on a single processor. Indeed, their in-distribution generalization error remains comparable regardless of the adopted discretization during the testing phase. Moreover, the partial connections, in place of a fully-connected structure, significantly reduce the number of tunable parameters. We show the capabilities of BLNMs in a challenging test case involving biophysically detailed electrophysiology simulations in a biventricular cardiac model of a pediatric patient with hypoplastic left heart syndrome. The model includes a 1D Purkinje network for fast conduction and a 3D heart-torso geometry. Specifically, we trained BLNMs on 150 in silico generated 12-lead electrocardiograms (ECGs) while spanning 7 model parameters, covering cell-scale, organ-level and electrical dyssynchrony. Although the 12-lead ECGs manifest very fast dynamics with sharp gradients, after automatic hyperparameter tuning the optimal BLNM, trained in less than 3 h on a single CPU, retains just 7 hidden layers and 19 neurons per layer. The resulting mean square error is on the order of 10−4 on an independent test dataset comprised of 50 additional electrophysiology simulations. In the online phase, the BLNM allows for 5000x faster real-time simulations of cardiac electrophysiology on a single core standard computer and can be employed to solve inverse problems via global optimization in a few seconds of computational time. This paper provides a novel computational tool to build reliable and efficient reduced-order models for digital twinning in engineering applications. The Julia implementation is publicly available under MIT License at https://github.com/StanfordCBCL/BLNM.jl.

Adaptive octree meshes for simulation of extracellular electrophysiology

Objective. The interaction between neural tissues and artificial electrodes is crucial for understanding and advancing neuroscientific research and therapeutic applications. However, accurately modeling this space around the neurons rapidly increases the computational complexity of neural simulations. Approach. This study demonstrates a dynamically adaptive simulation method that greatly accelerates computation by adjusting spatial resolution of the simulation as needed. Use of an octree structure for the mesh, in combination with the admittance method for discretizing conductivity, provides both accurate approximation and ease of modification on-the-fly. Main results. In tests of both local field potential estimation and multi-electrode stimulation, dynamically adapted meshes achieve accuracy comparable to high-resolution static meshes in an order of magnitude less time. Significance. The proposed simulation pipeline improves model scalability, allowing greater detail with fewer computational resources. The implementation is available as an open-source Python module, providing flexibility and ease of reuse for the broader research community.

Model Order Reduction with Dynamically Transformed Modes for Electrophysiological Simulations

The numerical simulation of neuromuscular processes in skeletal muscles typically imposes high computational costs, resulting in the need for techniques to reduce these costs. One submodel in these types of multiphysics multiscale simulations is the electrophysiological model, describing the propagation of an action potential (AP) in a muscle fiber. Involved in the simulation of the propagation are two traveling waves, resulting in difficulties for classical model order reduction (MOR) techniques based on linear subspaces.
 Instead, we apply the nonlinear MOR method shifted Proper Orthogonal Decomposition (sPOD) to construct dynamically transformed reduced basis functions depending on time-dependent paths spanning the adaptive reduced ansatz space.
 Our numerical experiments demonstrate that the constructed reduced ansatz space can accurately capture the dynamics of two fully separated wavefronts and reduce the degrees of freedom of the whole simulation. However, it cannot represent the activation of the AP in the center of the fiber and overlapping wave parts. The constructed reduced order model outperforms the high-dimensional full order model in terms of the computational costs while the accuracy is maintained and reaches speedup factors between 2 and 73 depending on the time discretization.

HPC Framework for Performing in Silico Trials Using a 3D Virtual Human Cardiac Population as Means to Assess Drug-Induced Arrhythmic Risk.

Following the 3 R's principles of animal research-replacement, reduction, and refinement-a high-performance computational framework was produced to generate a platform to perform human cardiac in-silico clinical trials as means to assess the pro-arrhythmic risk after the administrations of one or combination of two potentially arrhythmic drugs. The drugs assessed in this study were hydroxychloroquine and azithromycin. The framework employs electrophysiology simulations on high-resolution three-dimensional, biventricular human heart anatomies including phenotypic variabilities, so as to determine if differential QT-prolongation responds to drugs as observed clinically. These simulations also reproduce sex-specific ionic channel characteristics. The derived changes in the pseudo-electrocardiograms, calcium concentrations, as well as activation patterns within 3D geometries were evaluated for signs of induced arrhythmia. The virtual subjects could be evaluated at two different cycle lengths: at a normal heart rate and at a heart rate associated with stress as means to analyze the proarrhythmic risks after the administrations of hydroxychloroquine and azithromycin. Additionally, a series of experiments performed on reanimated swine hearts utilizing Visible Heart® methodologies in a four-chamber working heart model were performed to verify the arrhythmic behaviors observed in the in silico trials.The obtained results indicated similar pro-arrhythmic risk assessments within the virtual population as compared to published clinical trials (21% clinical risk vs 21.8% in silico trial risk). Evidence of transmurally heterogeneous action potential prolongations after providing a large dose of hydroxychloroquine was found as the observed mechanisms for elicited arrhythmias, both in the in vitro and the in silico models. The proposed workflow for in silico clinical drug cardiotoxicity trials allows for reproducing the complex behavior of cardiac electrophysiology in a varied population, in a matter of a few days as compared to themonths or years it requires for most in vivo human clinical trials. Importantly, our results provided evidence of the common phenotype variants that produce distinct drug-induced arrhythmogenic outcomes.

MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs from electrophysiological simulations

Mechanistic cardiac electrophysiology models allow for personalized simulations of the electrical activity in the heart and the ensuing electrocardiogram (ECG) on the body surface. As such, synthetic signals possess known ground truth labels of the underlying disease and can be employed for validation of machine learning ECG analysis tools in addition to clinical signals. Recently, synthetic ECGs were used to enrich sparse clinical data or even replace them completely during training leading to improved performance on real-world clinical test data. We thus generated a novel synthetic database comprising a total of 16,900 12 lead ECGs based on electrophysiological simulations equally distributed into healthy control and 7 pathology classes. The pathological case of myocardial infraction had 6 sub-classes. A comparison of extracted features between the virtual cohort and a publicly available clinical ECG database demonstrated that the synthetic signals represent clinical ECGs for healthy and pathological subpopulations with high fidelity. The ECG database is split into training, validation, and test folds for development and objective assessment of novel machine learning algorithms.

Assessment of Risk for Ventricular Tachycardia based on Extensive Electrophysiology Simulations.

Patients that have suffered a myocardial infarction are at high risk of developing ventricular tachycardia. Patient stratification is often determined by characterization of the underlying myocardial substrate by cardiac imaging methods. In this study, we show that computer modeling of cardiac electrophysiology based on personalized fast 3D simulations can help to assess patient risk to arrhythmia. We perform a large simulation study on 21 patient digital twins and reproduce successfully the clinical outcomes. In addition, we provide the sites which are prone to sustain ventricular tachycardias, i.e, onset sites around the scar region, and validate if they colocalize with exit sites from slow conduction channels.Clinical relevance- Fast electrophysiological simulations can provide advanced patient stratification indices and predict arrhythmic susceptibility to suffer from ventricular tachycardia in patients that have suffered a myocardial infarction.

Efficient, cell-based simulations of cardiac electrophysiology; The Kirchhoff Network Model (KNM)

Mathematical models based on homogenized representation of cardiac tissue have greatly improved our understanding of cardiac electrophysiology. However, these models are too coarse to investigate the dynamics at the level of the myocytes since the cells are not present in homogenized models. Recently, fine scale models have been proposed to allow for cell-level resolution of the dynamics, but these models are too computationally expensive to be used in applications like whole heart simulations of large animals. To address this issue, we propose a model that balances computational demands and physiological accuracy. The model is founded on Kirchhoff’s current law, and represents every myocyte in the tissue. This allows specific properties to be assigned to individual cardiomyocytes, and other cell types like fibroblasts can be added to the model in an accurate manner while keeping the computing efforts reasonable.

ID: 221013 Optimizing Spinal Cord Stimulation Using a Novel Green's Function-Based Generalized Activating Function

Prior work has shown that patient-specific computational modeling and optimization of epidural electrical stimulation (EES) of the spine can be used to rapidly restore locomotion in paraplegics [1]. To this end, Rowald et al. describe a pipeline for generating detailed spine models from medical imaging data. For a given model, electromagnetic field distributions are calculated for the activation of each electrode contact and coupled to electrophysiological simulations of axon populations within each root and rootlet. Optimal exposure conditions for the safe and selective activation of target roots may thus be identified.

Adaptive closed-loop paradigm of electrophysiology for neuron models

The traditional electrophysiological experiments based on an open-loop paradigm are relatively complicated and limited when facing an individual neuron with uncertain nonlinear factors. Emerging neural technologies enable tremendous growth in experimental data leading to the curse of high-dimensional data, which obstructs the mechanism exploration of spiking activities in the neurons. In this work, we propose an adaptive closed-loop electrophysiology simulation experimental paradigm based on a Radial Basis Function neural network and a highly nonlinear unscented Kalman filter. On account of the complex nonlinear dynamic characteristics of the real neurons, the proposed simulation experimental paradigm could fit the unknown neuron models with different channel parameters and different structures (i.e. single or multiple compartments), and further compute the injected stimulus in time according to the arbitrary desired spiking activities of the neurons. However, the hidden electrophysiological states of the neurons are difficult to be measured directly. Thus, an extra Unscented Kalman filter modular is incorporated in the closed-loop electrophysiology experimental paradigm. The numerical results and theoretical analyses demonstrate that the proposed adaptive closed-loop electrophysiology simulation experimental paradigm achieves desired spiking activities arbitrarily and the hidden dynamics of the neurons are visualized by the unscented Kalman filter modular. The proposed adaptive closed-loop simulation experimental paradigm can avoid the inefficiency of data at increasingly greater scales and enhance the scalability of electrophysiological experiments, thus speeding up the discovery cycle on neuroscience.

Left atrium surface mesh reconstruction from cardiac MRI

Abstract Funding Acknowledgements Type of funding sources: None. Background The strive towards personalised medicine is universal across all aspects of healthcare. In the management of atrial fibrillation (AF), this can take the form of shape analysis tools to predict an individual’s outcome from catheter ablation (1–3), or electrophysiological simulations to plan an ablation strategy (4–6). Fundamental to their application is our ability to derive accurate three-dimensional (3D) anatomical models of a patient’s left atrial geometry from cross-sectional imaging. MRI is widely utilised in this regard due to its excellent tissue characterisation. However, constructing surface meshes can be challenging given its relatively sparse two-dimensional (2D) image planes. A particular challenge exists in producing a mesh that balances concordance with left atrium segmentation and smoothing terms to eliminate sharp edges and artifact from the contouring process. Objective Here, we describe a novel method for left atrium surface mesh reconstruction from MRI contours. Methods Cardiac MRI was performed on ten patients with paroxysmal or persistent AF, on a 3T clinical MR scanner using phased-array receiver coils. Image sequences included respiratory and ECG-gated 15-minute post contrast late gadolinium enhancement, with a 1.3mm slice thickness and in-plane resolution of 0.66×0.66mm2. Seven individuals were in sinus rhythm, and three in AF at the time of MRI. A single, custom, graphical interface tool implemented in Matlab R2014a enabled 3D mesh reconstruction from 2D MRI slices (7,8). Endocardial surfaces of the left atrium were manually segmented by an experienced clinician, with the pulmonary veins and left atrial appendage excluded at their ostia (Figure 1A). Our tool produced an initial sparse representation with vertices equally spaced along the contours (Figure 1B). Attractor points were defined along the contours, with subdivision and Laplacian smoothing applied to the mesh. This was balanced with a deformation step, which iteratively pulled the mesh towards the attractor points (Figure 1C), resulting in its final shape (Figure 1D). Results Reconstruction performance was evaluated by measuring the distance between heart contours and the reconstructed 3D surface for each case, producing the value for average misalignment 0.71 ± 0.58mm, which compares favourably to the in-plane resolution. Conclusions Our approach offers a highly accurate method for producing 3D anatomical models of the left atrium from MRI, which is fully automated following segmentation through an interactive graphical interface. The iterative optimisation between attractor-based deformation towards the original contours and the Laplacian smoothing enables us to create smooth geometries which remain true to their segmentations. Furthermore, our approach does not require the use of a template mesh, which can result in final geometries being biased towards the template.

Evaluation of an open-source pipeline to create patient-specific left atrial models: A reproducibility study

This work presents an open-source software pipeline to create patient-specific left atrial models with fibre orientations and a fibrDEFAULTosis map, suitable for electrophysiology simulations, and quantifies the intra and inter observer reproducibility of the model creation. The semi-automatic pipeline takes as input a contrast enhanced magnetic resonance angiogram, and a late gadolinium enhanced (LGE) contrast magnetic resonance (CMR). Five operators were allocated 20 cases each from a set of 50 CMR datasets to create a total of 100 models to evaluate inter and intra-operator variability. Each output model consisted of: (1) a labelled surface mesh open at the pulmonary veins and mitral valve, (2) fibre orientations mapped from a diffusion tensor MRI (DTMRI) human atlas, (3) fibrosis map extracted from the LGE-CMR scan, and (4) simulation of local activation time (LAT) and phase singularity (PS) mapping. Reproducibility in our pipeline was evaluated by comparing agreement in shape of the output meshes, fibrosis distribution in the left atrial body, and fibre orientations. Reproducibility in simulations outputs was evaluated in the LAT maps by comparing the total activation times, and the mean conduction velocity (CV). PS maps were compared with the structural similarity index measure (SSIM). The users processed in total 60 cases for inter and 40 cases for intra-operator variability. Our workflow allows a single model to be created in 16.72 ± 12.25 min. Similarity was measured with shape, percentage of fibres oriented in the same direction, and intra-class correlation coefficient (ICC) for the fibrosis calculation. Shape differed noticeably only with users’ selection of the mitral valve and the length of the pulmonary veins from the ostia to the distal end; fibrosis agreement was high, with ICC of 0.909 (inter) and 0.999 (intra); fibre orientation agreement was high with 60.63% (inter) and 71.77% (intra). The LAT showed good agreement, where the median ± IQR of the absolute difference of the total activation times was 2.02 ± 2.45 ms for inter, and 1.37 ± 2.45 ms for intra. Also, the average ± sd of the mean CV difference was -0.00404 ± 0.0155 m/s for inter, and 0.0021 ± 0.0115 m/s for intra. Finally, the PS maps showed a moderately good agreement in SSIM for inter and intra, where the mean ± sd SSIM for inter and intra were 0.648 ± 0.21 and 0.608 ± 0.15, respectively. Although we found notable differences in the models, as a consequence of user input, our tests show that the uncertainty caused by both inter and intra-operator variability is comparable with uncertainty due to estimated fibres, and image resolution accuracy of segmentation tools.