A Review of Personalised Cardiac Computational Modelling Using Electroanatomical Mapping Data

O. A. Jaffery, Lea Melki, Gregory Slabaugh, Wilson W. Good, C. Roney
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Abstract

Computational models of cardiac electrophysiology have gradually matured during the past few decades and are now being personalised to provide patient-specific therapy guidance for improving suboptimal treatment outcomes. The predictive features of these personalised electrophysiology models hold the promise of providing optimal treatment planning, which is currently limited in the clinic owing to reliance on a population-based or average patient approach. The generation of a personalised electrophysiology model entails a sequence of steps for which a range of activation mapping, calibration methods and therapy simulation pipelines have been suggested. However, the optimal methods that can potentially constitute a clinically relevant in silico treatment are still being investigated and face limitations, such as uncertainty of electroanatomical data recordings, generation and calibration of models within clinical timelines and requirements to validate or benchmark the recovered tissue parameters. This paper is aimed at reporting techniques on the personalisation of cardiac computational models, with a focus on calibrating cardiac tissue conductivity based on electroanatomical mapping data.
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利用电解剖图数据建立个性化心脏计算模型综述
过去几十年来,心脏电生理学计算模型已逐渐成熟,目前正在进行个性化设计,以提供针对患者的治疗指导,从而改善不理想的治疗效果。这些个性化电生理学模型的预测功能有望提供最佳治疗计划,而目前在临床上,由于依赖于基于人群或普通患者的方法,这种治疗计划是有限的。个性化电生理模型的生成需要一系列步骤,为此提出了一系列激活映射、校准方法和治疗模拟管道。然而,有可能构成临床相关硅学治疗的最佳方法仍在研究之中,并面临诸多限制,如电解剖数据记录的不确定性、在临床时限内生成和校准模型以及验证或基准恢复组织参数的要求。本文旨在报告心脏计算模型的个性化技术,重点是根据电解剖图数据校准心脏组织电导率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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