Influence of image artifacts on image-based computer simulations of the cardiac electrophysiology

Kruithof, Evianne; Amirrajab, Sina; Cluitmans, Matthijs; Lau, K C; Breeuwer, Marcel

doi:10.1016/j.compbiomed.2021.104773

Cited by 7 publications

(6 citation statements)

References 14 publications

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“…This kind of VT is more likely sustained by anatomical reentry (Aguilar and Nattel, 2015), this means that the VT is sustained by reentries through conduction channels surrounded by scar on all sides. This kind of reentry was reported in other personalized heart modeling (Deng et al, 2019a;Kruithof et al, 2021) and clinical measurements (Anter et al, 2016;Nishimura et al, 2020). Our simulation results showed that type 3 VT was very stable, its morphology and location did not change during the entire 10 s simulation after reentry was initiated.…”

Section: Discussionsupporting

confidence: 82%

Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling

Tong

Zhao

et al. 2021

Front. Physiol.

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Personalized cardiac modeling is widely used for studying the mechanisms of cardiac arrythmias. Due to the high demanding of computational resource of modeling, the arrhythmias induced in the models are usually simulated for just a few seconds. In clinic, it is common that arrhythmias last for more than several minutes and the morphologies of reentries are not always stable, so it is not clear that whether the simulation of arrythmias for just a few seconds is long enough to match the arrhythmias detected in patients. This study aimed to observe how long simulation of the induced arrhythmias in the personalized cardiac models is sufficient to match the arrhythmias detected in patients. A total of 5 contrast enhanced MRI datasets of patient hearts with myocardial infarction were used in this study. Then, a classification method based on Gaussian mixture model was used to detect the infarct tissue. For each reentry, 3 s and 10 s were simulated. The characteristics of each reentry simulated for different duration were studied. Reentries were induced in all 5 ventricular models and sustained reentries were induced at 39 stimulation sites in the model. By analyzing the simulation results, we found that 41% of the sustained reentries in the 3 s simulation group terminated in the longer simulation groups (10 s). The second finding in our simulation was that only 23.1% of the sustained reentries in the 3 s simulation did not change location and morphology in the extended 10 s simulation. The third finding was that 35.9% reentries were stable in the 3 s simulation and should be extended for the simulation time. The fourth finding was that the simulation results in 10 s simulation matched better with the clinical measurements than the 3 s simulation. It was shown that 10 s simulation was sufficient to make simulation results stable. The findings of this study not only improve the simulation accuracy, but also reduce the unnecessary simulation time to achieve the optimal use of computer resources to improve the simulation efficiency and shorten the simulation time to meet the time node requirements of clinical operation on patients.

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Section: Discussionsupporting

confidence: 82%

Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling

Tong

Zhao

et al. 2021

Front. Physiol.

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“…This is something that needs to be critically analyzed and discussed. The fiber orientation, single-cell models, and the conductive were already validated by other groups or our previous studies [12,13,25,36]. Deng et al [47] analyzed the sensitivity of ablation targets to electrophysiological parameter variability.…”

Section: Discussionmentioning

confidence: 96%

Studying the Influence of Finite Element Mesh Size on the Accuracy of Ventricular Tachycardia Simulation

Cao,

Zhang,

et al. 2023

Rev. Cardiovasc. Med.

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Background: Ventricular tachycardia (VT) is a life-threatening heart condition commonly seen in patients with myocardial infarction (MI). Although personalized computational modeling has been used to understand VT and its treatment noninvasively, this approach can be computationally intensive and time consuming. Therefore, finding a balance between mesh size and computational efficiency is important. This study aimed to find an optimal mesh resolution that minimizes the need for computational resources while maintaining numerical accuracy and to investigate the effect of mesh resolution variation on the simulation results. Methods: We constructed ventricular models from contrast-enhanced magnetic resonance imaging data from six patients with MI. We created seven different models for each patient, with average edge lengths ranging from 315 to 645 µm using commercial software, Mimics. Programmed electrical stimulation was used to assess VT inducibility from 19 sites in each heart model. Results: The simulation results in the slab model with adaptive tetrahedral mesh (same as in the patient-specific model) showed that the absolute and relative differences in conduction velocity (CV) were 6.1 cm/s and 7.8% between average mesh sizes of 142 and 600 µm, respectively. However, the simulation results in the six patient-specific models showed that average mesh sizes with 350 µm yielded over 85% accuracy for clinically relevant VT. Although average mesh sizes of 417 and 478 µm could also achieve approximately 80% accuracy for clinically relevant VT, the percentage of incorrectly predicted VTs increases. When conductivity was modified to match the CV in the model with the finest mesh size, the overall ratio of positively predicted VT increased. Conclusions: The proposed personalized heart model could achieve an optimal balance between simulation time and VT prediction accuracy when discretized with adaptive tetrahedral meshes with an average edge length about 350 µm.

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“…To evaluate VT risk, a virtual stress test is performed. In such a test, electrical stimuli are applied at six different sites, A through F, around the BZ at the endocardium ( Figure 2E ) ( Kruithof et al, 2021 ). First, the Ten Tusscher cell model was stimulated 500 times prior to tissue simulations to reach steady-state.…”

Section: Methodsmentioning

confidence: 99%

Strain-controlled electrophysiological wave propagation alters in silico scar-based substrate for ventricular tachycardia

Willems,

Janssens,

Dekker

et al. 2024

Front. Physiol.

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Introduction: Assessing a patient’s risk of scar-based ventricular tachycardia (VT) after myocardial infarction is a challenging task. It can take months to years after infarction for VT to occur. Also, if selected for ablation therapy, success rates are low.Methods: Computational ventricular models have been presented previously to support VT risk assessment and to provide ablation guidance. In this study, an extension to such virtual-heart models is proposed to phenomenologically incorporate tissue remodeling driven by mechanical load. Strain amplitudes in the heart muscle are obtained from simulations of mechanics and are used to adjust the electrical conductivity. Results: The mechanics-driven adaptation of electrophysiology resulted in a more heterogeneous distribution of propagation velocities than that of standard models, which adapt electrophysiology in the structural substrate from medical images only. Moreover, conduction slowing was not only present in such a structural substrate, but extended in the adjacent functional border zone with impaired mechanics. This enlarged the volumes with high repolarization time gradients (≥10 ms/mm). However, maximum gradient values were not significantly affected. The enlarged volumes were localized along the structural substrate border, which lengthened the line of conduction block. The prolonged reentry pathways together with conduction slowing in functional regions increased VT cycle time, such that VT was easier to induce, and the number of recommended ablation sites increased from 3 to 5 locations.Discussion: Sensitivity testing showed an accurate model of strain-dependency to be critical for low ranges of conductivity. The model extension with mechanics-driven tissue remodeling is a potential approach to capture the evolution of the functional substrate and may offer insight into the progression of VT risk over time.

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Influence of image artifacts on image-based computer simulations of the cardiac electrophysiology

Cited by 7 publications

References 14 publications

Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling

Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling

Studying the Influence of Finite Element Mesh Size on the Accuracy of Ventricular Tachycardia Simulation

Strain-controlled electrophysiological wave propagation alters in silico scar-based substrate for ventricular tachycardia

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