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Tong L, Zhao C, Fu Z, et al. Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling. Front Physiol. 2021;12:733500doi: 10.3389/fphys.2021.733500.
Tong, L., Zhao, C., Fu, Z., Dong, R., Wu, Z., Wang, Z., Zhang, N., Wang, X., Cao, B., Sun, Y., Zheng, D., Xia, L., & Deng, D. (2021). Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling. Frontiers in physiology, 12733500. https://doi.org/10.3389/fphys.2021.733500
Tong, Lv, et al. "Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling." Frontiers in physiology vol. 12 (2021): 733500. doi: https://doi.org/10.3389/fphys.2021.733500
Tong L, Zhao C, Fu Z, Dong R, Wu Z, Wang Z, Zhang N, Wang X, Cao B, Sun Y, Zheng D, Xia L, Deng D. Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling. Front Physiol. 2021 Dec 24;12:733500. doi: 10.3389/fphys.2021.733500. eCollection 2021. PMID: 35002750; PMCID: PMC8739986.
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Front Physiol. 2021 Dec 24;12:733500. doi: 10.3389/fphys.2021.733500. eCollection 2021.

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

Frontiers in physiology

Lv Tong, Caiming Zhao, Zhenyin Fu, Ruiqing Dong, Zhenghong Wu, Zefeng Wang, Nan Zhang, Xinlu Wang, Boyang Cao, Yutong Sun, Dingchang Zheng, Ling Xia, Dongdong Deng

Affiliations

  1. School of Biomedical Engineering, Dalian University of Technology, Dalian, China.
  2. Department of Cardiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
  3. College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.
  4. Department of Cardiology, Dushu Lake Hospital Affiliated to Soochow University, Suzhou, China.
  5. Department of Cardiology, Beijing Anzhen Hospital Affiliated to Capital Medical University, Beijing, China.
  6. Department of Radiology, Beijing Anzhen Hospital Affiliated to Capital Medical University, Beijing, China.
  7. Research Centre for Intelligent Healthcare, Faculty of Health and Life Science, Coventry University, Coventry, United Kingdom.

PMID: 35002750 PMCID: PMC8739986 DOI: 10.3389/fphys.2021.733500

Abstract

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.

Copyright © 2021 Tong, Zhao, Fu, Dong, Wu, Wang, Zhang, Wang, Cao, Sun, Zheng, Xia and Deng.

Keywords: Gaussian mixture model method; arrhythmias; computational modeling; reentry; simulation time

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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