Cao Boyang, Zhang Nan, Fu Zhenyin, Dong Ruiqing, Chen Tan, Zhang Weiguo, Tong Lv, Wang Zefeng, Ma Mingxia, Song Zhanchun, Pan Fuzhi, Bai Jinghui, Wu Yongquan, Deng Dongdong, Xia Ling
College of Biomedical Engineering & Instrument Science, Zhejiang University, 310058 Hangzhou, Zhejiang, China.
School of Biomedical Engineering, Dalian University of Technology, 116024 Dalian, Liaoning, China.
Rev Cardiovasc Med. 2023 Dec 13;24(12):351. doi: 10.31083/j.rcm2412351. eCollection 2023 Dec.
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.
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.
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.
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.
室性心动过速(VT)是一种常见于心肌梗死(MI)患者的危及生命的心脏疾病。尽管个性化计算建模已被用于无创地理解室性心动过速及其治疗方法,但这种方法计算量可能很大且耗时。因此,在网格大小和计算效率之间找到平衡很重要。本研究旨在找到一种最佳网格分辨率,在保持数值精度的同时尽量减少对计算资源的需求,并研究网格分辨率变化对模拟结果的影响。
我们根据六名心肌梗死患者的对比增强磁共振成像数据构建心室模型。使用商业软件Mimics为每位患者创建七个不同的模型,平均边长范围从315到645微米。通过编程电刺激评估每个心脏模型中19个部位的室性心动过速诱发性。
采用自适应四面体网格的平板模型(与患者特异性模型相同)的模拟结果表明,平均网格大小为142和600微米时,传导速度(CV)的绝对差异和相对差异分别为6.1厘米/秒和7.8%。然而,六个患者特异性模型的模拟结果表明,平均网格大小为350微米时,对于临床相关的室性心动过速,准确率超过85%。尽管平均网格大小为417和478微米时,对于临床相关的室性心动过速也能达到约80%的准确率,但错误预测的室性心动过速的百分比会增加。当修改电导率以匹配具有最精细网格大小的模型中的传导速度时,阳性预测室性心动过速的总体比例增加。
当使用平均边长约为350微米的自适应四面体网格进行离散时,所提出的个性化心脏模型可以在模拟时间和室性心动过速预测准确性之间实现最佳平衡。