To enable large in silico trials and personalized model predictions on clinical timescales, it is imperative that models can be constructed quickly and reproducibly. First, we aimed to overcome the challenges of constructing cardiac models at scale through developing a robust, open-source pipeline for bilayer and volumetric atrial models. Second, we aimed to investigate the effects of fibres, fibrosis and model representation on fibrillatory dynamics. To construct bilayer and volumetric models, we extended our previously developed coordinate system to incorporate transmurality, atrial regions and fibres (rule-based or data driven diffusion tensor magnetic resonance imaging (MRI)). We created a cohort of 1000 biatrial bilayer and volumetric models derived from computed tomography (CT) data, as well as models from MRI, and electroanatomical mapping. Fibrillatory dynamics diverged between bilayer and volumetric simulations across the CT cohort (correlation coefficient for phase singularity maps: left atrial (LA) 0.27 ± 0.19, right atrial (RA) 0.41 ± 0.14). Adding fibrotic remodelling stabilized re-entries and reduced the impact of model type (LA: 0.52 ± 0.20, RA: 0.36 ± 0.18). The choice of fibre field has a small effect on paced activation data (less than 12 ms), but a larger effect on fibrillatory dynamics. Overall, we developed an open-source user-friendly pipeline for generating atrial models from imaging or electroanatomical mapping data enabling in silico clinical trials at scale ( https://github.com/pcmlab/atrialmtk ).
Cardiac conduction properties exhibit large variability, and affect patient-specific arrhythmia mechanisms. However, it is challenging to clinically measure conduction velocity (CV), anisotropy and fibre direction. Our aim is to develop a technique to estimate conduction anisotropy and fibre direction from clinically available electrical recordings.We developed and validated automated algorithms for estimating cardiac CV anisotropy, from any distribution of recording locations on the atrial surface. The first algorithm is for elliptical wavefront fitting to a single activation map (method 1), which works well close to the pacing location, but decreases in accuracy further from the pacing location (due to spatial heterogeneity in the conductivity and fibre fields). As such, we developed a second methodology for measuring local conduction anisotropy, using data from two or three activation maps (method 2: ellipse fitting to wavefront propagation velocity vectors from multiple activation maps).Ellipse fitting to CV vectors from two activation maps (method 2) leads to an improved estimation of longitudinal and transverse CV compared to method 1, but fibre direction estimation is still relatively poor. Using three activation maps with method 2 provides accurate estimation, with approximately 70% of atrial fibres estimated within 20∘. We applied the technique to clinical activation maps to demonstrate the presence of heterogeneous conduction anisotropy, and then tested the effects of this conduction anisotropy on predicted arrhythmia dynamics using computational simulation.We have developed novel algorithms for calculating CV and measuring the direction dependency of atrial activation to estimate atrial fibre direction, without the need for specialised pacing protocols, using clinically available electrical recordings.
Predicting patient-specific responses to treatment and guiding therapy requires a framework that integrates personalised anatomy, fibrosis and electrophysiological data. However, it is challenging to perform on clinical timescales and at scale.
Cardiac resynchronization therapy (CRT) is typically delivered via quadripolar leads that allow stimulation using either true bipolar pacing, where stimulation occurs between two electrodes (BP) on the quadripolar lead, or extended bipole (EBP) left ventricular (LV) pacing, with the quadripolar electrodes and right ventricular coil acting as the cathode and anode, respectively. True bipolar pacing is associated with reductions in mortality and it has been postulated that these differences are the result of enhanced electrical activation.Patients undergoing a CRT underwent an electrocardiographic imaging study where electrical activation data were recorded while different LV pacing vectors were temporarily programmed.There were no differences in the total electrical activation times or dispersion of electrical activation between biventricular pacing with bipolar or corresponding EBP LV vector configurations (left ventricular total activation time [LVtat] BP 74.70 ± 18.07 vs EBP 72.4 ± 22.64; P = 0.45). When dichotomized according to etiology, no difference was observed in the activation time with either BP or EBP pacing (LVtat BP ischemic cardiomyopathy 72.2 ± 17.4 vs BP dilated cardiomyopathy 79.9 ± 18.9; P = 0.38).Bipolar pacing alters the mechanical activation sequence of the LV and is associated with reductions in all-cause mortality. It has been postulated these benefits derive from improvements in electromechanical activation of the LV. Our study would suggest that true bipolar pacing does not necessarily result in more favorable activation of the LV or improved electrical resynchronization and other mechanisms should be explored.
Cardiac anatomy plays a crucial role in determining cardiac function. However, there is a poor understanding of how specific and localised anatomical changes affect different cardiac functional outputs. In this work, we test the hypothesis that in a statistical shape model (SSM), the modes that are most relevant for describing anatomy are also most important for determining the output of cardiac electromechanics simulations. We made patient-specific four-chamber heart meshes ( n = 20) from cardiac CT images in asymptomatic subjects and created a SSM from 19 cases. Nine modes captured 90% of the anatomical variation in the SSM. Functional simulation outputs correlated best with modes 2, 3 and 9 on average ( R = 0.49 ± 0.17, 0.37 ± 0.23 and 0.34 ± 0.17 respectively). We performed a global sensitivity analysis to identify the different modes responsible for different simulated electrical and mechanical measures of cardiac function. Modes 2 and 9 were the most important for determining simulated left ventricular mechanics and pressure-derived phenotypes. Mode 2 explained 28.56 ± 16.48% and 25.5 ± 20.85, and mode 9 explained 12.1 ± 8.74% and 13.54 ± 16.91% of the variances of mechanics and pressure-derived phenotypes, respectively. Electrophysiological biomarkers were explained by the interaction of 3 ± 1 modes. In the healthy adult human heart, shape modes that explain large portions of anatomical variance do not explain equivalent levels of electromechanical functional variation. As a result, in cardiac models, representing patient anatomy using a limited number of modes of anatomical variation can cause a loss in accuracy of simulated electromechanical function.
Abstract Aims Standard methods of heart chamber volume estimation in cardiovascular magnetic resonance (CMR) typically utilize simple geometric formulae based on a limited number of slices. We aimed to evaluate whether an automated deep learning neural network prediction of 3D anatomy of all four chambers would show stronger associations with cardiovascular risk factors and disease than standard volume estimation methods in the UK Biobank. Methods and results A deep learning network was adapted to predict 3D segmentations of left and right ventricles (LV, RV) and atria (LA, RA) at ∼1 mm isotropic resolution from CMR short- and long-axis 2D segmentations obtained from a fully automated machine learning pipeline in 4723 individuals with cardiovascular disease (CVD) and 5733 without in the UK Biobank. Relationships between volumes at end-diastole (ED) and end-systole (ES) and risk/disease factors were quantified using univariate, multivariate, and logistic regression analyses. Strength of association between deep learning volumes and standard volumes was compared using the area under the receiving operator characteristic curve (AUC). Univariate and multivariate associations between deep learning volumes and most risk and disease factors were stronger than for standard volumes (higher R2 and more significant P-values), particularly for sex, age, and body mass index. AUCs for all logistic regressions were higher for deep learning volumes than standard volumes (P < 0.001 for all four chambers at ED and ES). Conclusion Neural network reconstructions of whole heart volumes had significantly stronger associations with CVD and risk factors than standard volume estimation methods in an automatic processing pipeline.
Introduction: Cardiac resynchronization therapy (CRT) delivered via left ventricular (LV) endocardial pacing (ENDO-CRT) is associated with improved acute haemodynamic response compared to LV epicardial pacing (EPI-CRT). The mechanisms underlying this improved response remain controversial. Hypothesis: We used computational electrophysiological models to test the hypotheses that the following ordered cardiac properties are advantageous to ENDO-CRT: fast endocardial conduction (FEC), inherent geometry, asymmetric transmural conduction, and tissue anisotropy. Methods: Cardiac activation was simulated using the mono-domain tissue excitation model in 2D canine and human, and 3D canine biventricular models. The latest activation times (LATs) for LV endocardial and biventricular epicardial tissue were calculated (LVLAT and TLAT), as well the percentage decrease in LATs for endocardial (en) vs epicardial (ep) LV pacing (defined as %dLV=100*(LVLATep- LVLATen)/LVLATep and %dT=100*( TLATep- TLATen)/TLATep respectively). Results: Normal canine cardiac anatomy is responsible for %dLV and %dT values of 7.4% and 5.5%, respectively. Concentric and eccentric remodelled anatomies resulted in %dT values of 15.6% and 1.3%, respectively. The 3D biventricular paced canine model resulted in %dLV and %dT values of -7.1% and 1.5%, in contrast to the experimental observations of 16% and 11%, respectively. Adding FEC to this model altered %dLV and %dT to 13.1% and 10.1%, respectively. Conclusions: Our results provide a physiological explanation for improved response to ENDO-CRT. We predict that patients with viable fast-conducting endocardial tissue and/or distal Purkinje network, as well as concentric remodelling, are more likely to benefit from reduced ATs and increased synchrony arising from endocardial pacing.
Coronary computed tomography angiography (CCTA) provides detailed an-atomical information on all chambers of the heart. Existing segmentation tools can label the gross anatomy, but addition of application-specific labels can require detailed and often manual refinement. We developed a U-Net based framework to i) extrapolate a new label from existing labels, and ii) parcellate one label into multiple labels, both using label-to-label mapping, to create a desired segmentation that could then be learnt directly from the image (image- to-label mapping). This approach only required manual correction in a small subset of cases (80 for extrapolation, 50 for parcella-tion, compared with 260 for initial labels). An initial 6-label segmentation (left ventricle, left ventricular myocardium, right ventricle, left atrium, right atrium and aorta) was refined to a 10-label segmentation that added a label for the pulmonary artery and divided the left atrium label into body, left and right veins and appendage components. The final method was tested using 30 cases, 10 each from Philips, Siemens and Toshiba scanners. In addition to the new labels, the median Dice scores were improved for all the initial 6 labels to be above 95% in the 10-label segmentation, e.g. from 91% to 97% for the left atrium body and from 92% to 96% for the right ventricle. This method provides a simple framework for flexible refinement of anatomical labels. The code and executables are available at cemrg.com.
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