Automatic mapping of atrial fiber orientations for patient‐specific modeling of cardiac electromechanics using image registration

Hoermann, Julia; Pfaller, Martin R.; Avena, Linda; Bertoglio, C; Wall, Wolfgang A.

doi:10.1002/cnm.3190

Cited by 16 publications

(13 citation statements)

References 45 publications

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“…This would require information of the fiber orientations in the atria and ventricles. There are several methodologies to incorporate this information with ruled-based approaches [38,39] and mapping techniques [40,41]. On the estimation of uncertainties, we see two limitations: First, we have not included the noise that is generated by the acquisition of the activation times with the electrode.…”

Section: Discussionmentioning

confidence: 99%

Physics-Informed Neural Networks for Cardiac Activation Mapping

et al. 2020

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A critical procedure in diagnosing atrial fibrillation is the creation of electro-anatomic activation maps. Current methods generate these mappings from interpolation using a few sparse data points recorded inside the atria; they neither include prior knowledge of the underlying physics nor uncertainty of these recordings. Here we propose a physics-informed neural network for cardiac activation mapping that accounts for the underlying wave propagation dynamics and we quantify the epistemic uncertainty associated with these predictions. These uncertainty estimates not only allow us to quantify the predictive error of the neural network, but also help to reduce it by judiciously selecting new informative measurement locations via active learning. We illustrate the potential of our approach using a synthetic benchmark problem and a personalized electrophysiology model of the left atrium. We show that our new method outperforms linear interpolation and Gaussian process regression for the benchmark problem and linear interpolation at clinical densities for the left atrium. In both cases, the active learning algorithm achieves lower error levels than random allocation. Our findings open the door toward physics-based electro-anatomic mapping with the ultimate goals to reduce procedural time and improve diagnostic predictability for patients affected by atrial fibrillation. Open source code is available at https://github.com/fsahli/EikonalNet.

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Section: Discussionmentioning

confidence: 99%

Physics-Informed Neural Networks for Cardiac Activation Mapping

et al. 2020

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“…The sheet vector s 0 is then obtained from s 0 = n 0 × f 0 . The atrial fiber architecture is obtained using a semi-automatic registration method based on the fiber definition in atlas atria [47,48]. See figure 5b for atrial fibers and ventricular ±60 • fibers visualized at the endocardium.…”

Section: Modeling the Pericardiummentioning

confidence: 99%

The importance of the pericardium for cardiac biomechanics: from physiology to computational modeling

Pfaller

Hörmann

Weigl

et al. 2018

Biomech Model Mechanobiol

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101

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The human heart is enclosed in the pericardial cavity. The pericardium consists of a layered thin sac and is separated from the myocardium by a thin film of fluid. It provides a fixture in space and frictionless sliding of the myocardium. The influence of the pericardium is essential for predictive mechanical simulations of the heart. However, there is no consensus on physiologically correct and computationally tractable pericardial boundary conditions. Here we propose to model the pericardial influence as a parallel spring and dashpot acting in normal direction to the epicardium. Using a four-chamber geometry, we compare a model with pericardial boundary conditions to a model with fixated apex. The influence of pericardial stiffness is demonstrated in a parametric study. Comparing simulation results to measurements from cine magnetic resonance imaging reveals that adding pericardial boundary conditions yields a better approximation with respect to atrioventricular plane displacement, atrial filling, and overall spatial approximation error. We demonstrate that this simple model of pericardial-myocardial inter-M. R. Pfaller * joint last authors action can correctly predict the pumping mechanisms of the heart as previously assessed in clinical studies. Utilizing a pericardial model can not only provide much more realistic cardiac mechanics simulations but also allows new insights into pericardial-myocardial interaction which cannot be assessed in clinical measurements yet.

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“…Structural anisotropy affects electrical propagation patterns 22 locations of atrial reentrant drivers, 34 and atrial mechanics. 19 However, current imaging modalities are not suitable for global in vivo measurements of atrial fibre directions; 48 as such it is challenging to personalise atrial fibre distributions for patient specific assessment or computational models. There are multiple methodologies for including atrial fibres in either 2D surface or 3D volumetric computational meshes.…”

Section: Introductionmentioning

confidence: 99%

Constructing a Human Atrial Fibre Atlas

et al. 2020

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Atrial anisotropy affects electrical propagation patterns, anchor locations of atrial reentrant drivers, and atrial mechanics. However, patient-specific atrial fibre fields and anisotropy measurements are not currently available, and consequently assigning fibre fields to atrial models is challenging. We aimed to construct an atrial fibre atlas from a high-resolution DTMRI dataset that optimally reproduces electrophysiology simulation predictions corresponding to patient-specific fibre fields, and to develop a methodology for automatically assigning fibres to patient-specific anatomies. We extended an atrial coordinate system to map the pulmonary veins, vena cava and appendages to standardised positions in the coordinate system corresponding to the average location across the anatomies. We then expressed each fibre field in this atrial coordinate system and calculated an average fibre field. To assess the effects of fibre field on patient-specific modelling predictions, we calculated paced activation time maps and electrical driver locations during AF. In total, 756 activation time maps were calculated (7 anatomies with 9 fibre maps and 2 pacing locations, for the endocardial, epicardial and bilayer surface models of the LA and RA). Patient-specific fibre fields had a relatively small effect on average paced activation maps (range of mean local activation time difference for LA fields: 2.67–3.60 ms, and for RA fields: 2.29–3.44 ms), but had a larger effect on maximum LAT differences (range for LA 12.7–16.6%; range for RA 11.9–15.0%). A total of 126 phase singularity density maps were calculated (7 anatomies with 9 fibre maps for the LA and RA bilayer models). The fibre field corresponding to anatomy 1 had the highest median PS density map correlation coefficient for LA bilayer simulations (0.44 compared to the other correlations, ranging from 0.14 to 0.39), while the average fibre field had the highest correlation for the RA bilayer simulations (0.61 compared to the other correlations, ranging from 0.37 to 0.56). For sinus rhythm simulations, average activation time is robust to fibre field direction; however, maximum differences can still be significant. Patient specific fibres are more important for arrhythmia simulations, particularly in the left atrium. We propose using the fibre field corresponding to DTMRI dataset 1 for LA simulations, and the average fibre field for RA simulations as these optimally predicted arrhythmia properties.

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Automatic mapping of atrial fiber orientations for patient‐specific modeling of cardiac electromechanics using image registration

Cited by 16 publications

References 45 publications

Physics-Informed Neural Networks for Cardiac Activation Mapping

Physics-Informed Neural Networks for Cardiac Activation Mapping

The importance of the pericardium for cardiac biomechanics: from physiology to computational modeling

Constructing a Human Atrial Fibre Atlas

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