Cargando…

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

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Tong, Lv, Zhao, Caiming, Fu, Zhenyin, Dong, Ruiqing, Wu, Zhenghong, Wang, Zefeng, Zhang, Nan, Wang, Xinlu, Cao, Boyang, Sun, Yutong, Zheng, Dingchang, Xia, Ling, Deng, Dongdong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8739986/
https://www.ncbi.nlm.nih.gov/pubmed/35002750
http://dx.doi.org/10.3389/fphys.2021.733500
_version_ 1784629218755215360
author Tong, Lv
Zhao, Caiming
Fu, Zhenyin
Dong, Ruiqing
Wu, Zhenghong
Wang, Zefeng
Zhang, Nan
Wang, Xinlu
Cao, Boyang
Sun, Yutong
Zheng, Dingchang
Xia, Ling
Deng, Dongdong
author_facet Tong, Lv
Zhao, Caiming
Fu, Zhenyin
Dong, Ruiqing
Wu, Zhenghong
Wang, Zefeng
Zhang, Nan
Wang, Xinlu
Cao, Boyang
Sun, Yutong
Zheng, Dingchang
Xia, Ling
Deng, Dongdong
author_sort Tong, Lv
collection PubMed
description 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.
format Online
Article
Text
id pubmed-8739986
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-87399862022-01-08 Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling Tong, Lv Zhao, Caiming Fu, Zhenyin Dong, Ruiqing Wu, Zhenghong Wang, Zefeng Zhang, Nan Wang, Xinlu Cao, Boyang Sun, Yutong Zheng, Dingchang Xia, Ling Deng, Dongdong Front Physiol Physiology 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. Frontiers Media S.A. 2021-12-24 /pmc/articles/PMC8739986/ /pubmed/35002750 http://dx.doi.org/10.3389/fphys.2021.733500 Text en Copyright © 2021 Tong, Zhao, Fu, Dong, Wu, Wang, Zhang, Wang, Cao, Sun, Zheng, Xia and Deng. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Tong, Lv
Zhao, Caiming
Fu, Zhenyin
Dong, Ruiqing
Wu, Zhenghong
Wang, Zefeng
Zhang, Nan
Wang, Xinlu
Cao, Boyang
Sun, Yutong
Zheng, Dingchang
Xia, Ling
Deng, Dongdong
Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling
title Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling
title_full Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling
title_fullStr Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling
title_full_unstemmed Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling
title_short Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling
title_sort preliminary study: learning the impact of simulation time on reentry location and morphology induced by personalized cardiac modeling
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8739986/
https://www.ncbi.nlm.nih.gov/pubmed/35002750
http://dx.doi.org/10.3389/fphys.2021.733500
work_keys_str_mv AT tonglv preliminarystudylearningtheimpactofsimulationtimeonreentrylocationandmorphologyinducedbypersonalizedcardiacmodeling
AT zhaocaiming preliminarystudylearningtheimpactofsimulationtimeonreentrylocationandmorphologyinducedbypersonalizedcardiacmodeling
AT fuzhenyin preliminarystudylearningtheimpactofsimulationtimeonreentrylocationandmorphologyinducedbypersonalizedcardiacmodeling
AT dongruiqing preliminarystudylearningtheimpactofsimulationtimeonreentrylocationandmorphologyinducedbypersonalizedcardiacmodeling
AT wuzhenghong preliminarystudylearningtheimpactofsimulationtimeonreentrylocationandmorphologyinducedbypersonalizedcardiacmodeling
AT wangzefeng preliminarystudylearningtheimpactofsimulationtimeonreentrylocationandmorphologyinducedbypersonalizedcardiacmodeling
AT zhangnan preliminarystudylearningtheimpactofsimulationtimeonreentrylocationandmorphologyinducedbypersonalizedcardiacmodeling
AT wangxinlu preliminarystudylearningtheimpactofsimulationtimeonreentrylocationandmorphologyinducedbypersonalizedcardiacmodeling
AT caoboyang preliminarystudylearningtheimpactofsimulationtimeonreentrylocationandmorphologyinducedbypersonalizedcardiacmodeling
AT sunyutong preliminarystudylearningtheimpactofsimulationtimeonreentrylocationandmorphologyinducedbypersonalizedcardiacmodeling
AT zhengdingchang preliminarystudylearningtheimpactofsimulationtimeonreentrylocationandmorphologyinducedbypersonalizedcardiacmodeling
AT xialing preliminarystudylearningtheimpactofsimulationtimeonreentrylocationandmorphologyinducedbypersonalizedcardiacmodeling
AT dengdongdong preliminarystudylearningtheimpactofsimulationtimeonreentrylocationandmorphologyinducedbypersonalizedcardiacmodeling