• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

使用Meshtool在心脏建模工作流程中自动化基于图像的网格生成和操作任务。

Automating image-based mesh generation and manipulation tasks in cardiac modeling workflows using Meshtool.

作者信息

Neic Aurel, Gsell Matthias A F, Karabelas Elias, Prassl Anton J, Plank Gernot

机构信息

Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria.

NumeriCor GmbH, Graz, Austria.

出版信息

SoftwareX. 2020 Jan-Jun;11:100454. doi: 10.1016/j.softx.2020.100454. Epub 2020 Mar 20.

DOI:10.1016/j.softx.2020.100454
PMID:32607406
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7326605/
Abstract

Advanced cardiac modeling studies rely on the ability to generate and functionalize personalized models from tomographic multi-label image stacks. Eventually, this is used for building virtual cohorts that capture the variability in size, shape, and morphology of individual hearts. Typical modeling workflows involve a multitude of interactive mesh manipulation steps, rendering model generation expensive. Meshtool is software specifically designed for automating all complex mesh manipulation tasks emerging in such workflows by implementing algorithms for tasks describable as operations on label fields and/or geometric features. We illustrate how Meshtool increases efficiency and reduces costs by offering an automatable, high performance mesh manipulation toolbox.

摘要

先进的心脏建模研究依赖于从断层多标签图像堆栈生成个性化模型并使其功能化的能力。最终，这被用于构建虚拟队列，以捕捉个体心脏在大小、形状和形态上的变异性。典型的建模工作流程涉及大量交互式网格操作步骤，使得模型生成成本高昂。Meshtool是一款专门设计的软件，通过为可描述为标签字段和/或几何特征操作的任务实现算法，自动执行此类工作流程中出现的所有复杂网格操作任务。我们展示了Meshtool如何通过提供一个可自动化的高性能网格操作工具箱来提高效率并降低成本。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bb0/7326605/bda69f708b6e/EMS86604-f005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bb0/7326605/82bb6c0df406/EMS86604-f001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bb0/7326605/6e2789db20e5/EMS86604-f002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bb0/7326605/de263d2f7fe9/EMS86604-f003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bb0/7326605/35bcf71bb554/EMS86604-f004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bb0/7326605/bda69f708b6e/EMS86604-f005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bb0/7326605/82bb6c0df406/EMS86604-f001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bb0/7326605/6e2789db20e5/EMS86604-f002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bb0/7326605/de263d2f7fe9/EMS86604-f003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bb0/7326605/35bcf71bb554/EMS86604-f004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7bb0/7326605/bda69f708b6e/EMS86604-f005.jpg

相似文献

1
Automating image-based mesh generation and manipulation tasks in cardiac modeling workflows using Meshtool.使用Meshtool在心脏建模工作流程中自动化基于图像的网格生成和操作任务。
SoftwareX. 2020 Jan-Jun;11:100454. doi: 10.1016/j.softx.2020.100454. Epub 2020 Mar 20.
2
Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function.多边形曲面处理和网格生成工具，用于心脏功能的数值模拟。
Int J Numer Method Biomed Eng. 2021 Apr;37(4):e3435. doi: 10.1002/cnm.3435. Epub 2021 Jan 28.
3
New software developments for quality mesh generation and optimization from biomedical imaging data.用于生物医学成像数据的质量网格生成和优化的新软件发展。
Comput Methods Programs Biomed. 2014;113(1):226-40. doi: 10.1016/j.cmpb.2013.08.009. Epub 2013 Sep 4.
4
Interpretable cardiac anatomy modeling using variational mesh autoencoders.使用变分网格自动编码器的可解释心脏解剖建模。
Front Cardiovasc Med. 2022 Dec 22;9:983868. doi: 10.3389/fcvm.2022.983868. eCollection 2022.
5
An approach for patient-specific multi-domain vascular mesh generation featuring spatially varying wall thickness modeling.一种针对特定患者的多域血管网格生成方法，其特点是具有空间变化的壁厚建模。
J Biomech. 2015 Jul 16;48(10):1972-81. doi: 10.1016/j.jbiomech.2015.04.006. Epub 2015 Apr 16.
6
ROBOT: A Tool for Automating Ontology Workflows.机器人：自动化本体工作流程的工具。
BMC Bioinformatics. 2019 Jul 29;20(1):407. doi: 10.1186/s12859-019-3002-3.
7
A universal algorithm for an improved finite element mesh generation Mesh quality assessment in comparison to former automated mesh-generators and an analytic model.一种用于改进有限元网格生成的通用算法：与先前的自动网格生成器和解析模型相比的网格质量评估。
Med Eng Phys. 2005 Jun;27(5):383-94. doi: 10.1016/j.medengphy.2004.10.004. Epub 2005 Jan 19.
8
SDF4CHD: Generative modeling of cardiac anatomies with congenital heart defects.SDF4CHD：带有先天性心脏缺陷的心脏解剖结构生成模型。
Med Image Anal. 2024 Oct;97:103293. doi: 10.1016/j.media.2024.103293. Epub 2024 Aug 8.
9
SDF4CHD: Generative Modeling of Cardiac Anatomies with Congenital Heart Defects.SDF4CHD：先天性心脏病心脏解剖结构的生成式建模
ArXiv. 2023 Nov 8:arXiv:2311.00332v2.
10
Multi-objective Parameter Auto-tuning for Tissue Image Segmentation Workflows.用于组织图像分割工作流程的多目标参数自动调整
J Digit Imaging. 2019 Jun;32(3):521-533. doi: 10.1007/s10278-018-0138-z.

引用本文的文献

1
Cardiac digital twins at scale from MRI: Open tools and representative models from ~ 55000 UK Biobank participants.基于MRI的大规模心脏数字孪生：来自约55000名英国生物银行参与者的开放工具和代表性模型。
PLoS One. 2025 Jul 15;20(7):e0327158. doi: 10.1371/journal.pone.0327158. eCollection 2025.
2
Developing cardiac digital twin populations powered by machine learning provides electrophysiological insights in conduction and repolarization.利用机器学习开发心脏数字孪生群体可提供有关传导和复极化的电生理见解。
Nat Cardiovasc Res. 2025 May;4(5):624-636. doi: 10.1038/s44161-025-00650-0. Epub 2025 May 16.
3
Left atrial spatial entropy: a novel tool for electrophysiological substrate characterization in atrial fibrillation.

本文引用的文献

1
Generation of a cohort of whole-torso cardiac models for assessing the utility of a novel computed shock vector efficiency metric for ICD optimisation.生成一组全胸心脏模型，用于评估新型计算电击向量效率指标在 ICD 优化中的应用。
Comput Biol Med. 2019 Sep;112:103368. doi: 10.1016/j.compbiomed.2019.103368. Epub 2019 Jul 24.
2
Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia.用于指导梗死相关室性心动过速消融的个性化虚拟心脏技术。
Nat Biomed Eng. 2018 Oct;2(10):732-740. doi: 10.1038/s41551-018-0282-2. Epub 2018 Sep 3.
3
Assessment of wall stresses and mechanical heart power in the left ventricle: Finite element modeling versus Laplace analysis.
左心房空间熵：一种用于心房颤动电生理基质特征分析的新工具。
Front Physiol. 2024 Nov 27;15:1474568. doi: 10.3389/fphys.2024.1474568. eCollection 2024.
4
Multiphysics simulations reveal haemodynamic impacts of patient-derived fibrosis-related changes in left atrial tissue mechanics.多物理场模拟揭示了患者来源的左心房组织力学中纤维化相关变化的血流动力学影响。
J Physiol. 2024 Dec;602(24):6789-6812. doi: 10.1113/JP287011. Epub 2024 Nov 8.
5
A computational study on the influence of antegrade accessory pathway location on the 12-lead electrocardiogram in Wolff-Parkinson-White syndrome.关于顺行性旁路位置对预激综合征12导联心电图影响的计算研究
Europace. 2025 Feb 5;27(2). doi: 10.1093/europace/euae223.
6
Predicting postinfarct ventricular tachycardia by integrating cardiac MRI and advanced computational reentrant pathway analysis.通过整合心脏 MRI 和先进的复发性通路分析来预测心肌梗死后室性心动过速。
Heart Rhythm. 2024 Oct;21(10):1962-1969. doi: 10.1016/j.hrthm.2024.04.077. Epub 2024 Apr 24.
7
Hybrid Neural State-Space Modeling for Supervised and Unsupervised Electrocardiographic Imaging.混合神经网络状态空间建模在心电图像的有监督和无监督应用。
IEEE Trans Med Imaging. 2024 Aug;43(8):2733-2744. doi: 10.1109/TMI.2024.3377094. Epub 2024 Aug 1.
8
Constructing bilayer and volumetric atrial models at scale.构建大规模的双层和三维心房模型。
Interface Focus. 2023 Dec 15;13(6):20230038. doi: 10.1098/rsfs.2023.0038. eCollection 2023 Dec 6.
9
A Semi-automatic Pipeline for Generation of Large Cohorts of Four-Chamber Heart Meshes.半自动生成大规模四腔心网格的流水线。
Methods Mol Biol. 2024;2735:117-127. doi: 10.1007/978-1-0716-3527-8_7.
10
MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs from electrophysiological simulations.MedalCare-XL：来自电生理模拟的 16900 份健康和病理的 12 导联合成 ECG。
Sci Data. 2023 Aug 8;10(1):531. doi: 10.1038/s41597-023-02416-4.
评估左心室壁应力和机械心功率：有限元建模与拉普拉斯分析。
Int J Numer Method Biomed Eng. 2018 Dec;34(12):e3147. doi: 10.1002/cnm.3147. Epub 2018 Sep 30.
4
Towards a Computational Framework for Modeling the Impact of Aortic Coarctations Upon Left Ventricular Load.建立一个用于模拟主动脉缩窄对左心室负荷影响的计算框架。
Front Physiol. 2018 May 28;9:538. doi: 10.3389/fphys.2018.00538. eCollection 2018.
5
Efficient computation of electrograms and ECGs in human whole heart simulations using a reaction-eikonal model.使用反应-程函模型在人体全心模拟中高效计算心内电图和心电图
J Comput Phys. 2017 Oct 1;346:191-211. doi: 10.1016/j.jcp.2017.06.020.
6
Patient-specific modeling of left ventricular electromechanics as a driver for haemodynamic analysis.作为血流动力学分析驱动因素的左心室机电的患者特异性建模。
Europace. 2016 Dec;18(suppl 4):iv121-iv129. doi: 10.1093/europace/euw369.
7
Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models.心肌梗死后患者心律失常风险分层的个体化心脏模型研究。
Nat Commun. 2016 May 10;7:11437. doi: 10.1038/ncomms11437.
8
Image-Based Personalization of Cardiac Anatomy for Coupled Electromechanical Modeling.基于图像的心脏解剖个性化用于耦合机电建模。
Ann Biomed Eng. 2016 Jan;44(1):58-70. doi: 10.1007/s10439-015-1474-5. Epub 2015 Sep 30.
9
Automatically generated, anatomically accurate meshes for cardiac electrophysiology problems.针对心脏电生理问题自动生成的、解剖结构精确的网格。
IEEE Trans Biomed Eng. 2009 May;56(5):1318-30. doi: 10.1109/TBME.2009.2014243. Epub 2009 Feb 6.