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

AI Article Synopsis

  • Ventricular tachycardia (VT) is a dangerous heart condition often seen in patients with heart attacks, and this study focuses on optimizing computational modeling to analyze it more effectively and efficiently.
  • Researchers created various heart models based on MRI data from six patients and tested these models to see how different mesh sizes affected the accuracy of VT predictions.
  • Results showed that a mesh size of 350 µm provided over 85% accuracy for predicting clinically relevant VT, while larger mesh sizes decreased prediction accuracy, emphasizing the importance of balancing computational resources with model precision.

Article Abstract

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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11272846PMC
http://dx.doi.org/10.31083/j.rcm2412351DOI Listing

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