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Patient-Specific Model of Left Heart Anatomy, Dynamics and Hemodynamics from 4D TEE: A First Validation Study

机译:特定于患者的左心解剖,动力学和血液动力学的4D TEE模型:首次验证研究

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摘要

Patient-specific models of the heart physiology have become powerful instruments able to improve the diagnosis and treatment of cardiac disease. A systemic representation of the whole organ is required to capture the complex functional and hemodynamical interdependencies among the anatomical structures. We propose a novel framework for personalized modeling of the left-side heart that integrates comprehensive data of the morphology, function and hemodynamics. Patient-specific fluid dynamics are computed over the entire cardiac cycle using embedded boundary and ghost fluid methods, constrained by the dynamics of highly detailed anatomical models. Personalized boundary conditions are determined by estimating cardiac shape and motion from 4D TEE images through robust discriminative learning methods. Qualitative and quantitative validation of the computed blood dynamics is performed against Doppler echocardiography measurements, following an original methodology. Results showed a high agreement between simulation and ground truth and a correlation of r = 0.85 (p < 0.0002675). To the best of our knowledge, this is the first time that computational fluid dynamics are simulated on a systemic and comprehensive patient-specific model of the heart and validated against routinely acquired clinical ground truth.
机译:特定于患者的心脏生理模型已成为能够改善心脏病诊断和治疗的强大工具。需要整个器官的系统表示来捕获解剖结构之间复杂的功能和血液动力学相互依赖性。我们为左侧心脏的个性化建模提出了一个新颖的框架,该框架整合了形态,功能和血液动力学的综合数据。使用嵌入式边界和幻影流体方法在整个心动周期中计算患者特定的流体动力学,并受高度详细的解剖模型的动力学约束。通过强大的判别学习方法,通过估计4D TEE图像的心脏形状和运动来确定个性化的边界条件。遵循原始方法,针对多普勒超声心动图测量进行计算的血液动力学的定性和定量验证。结果表明,仿真与基本事实之间的一致性很高,相关系数r = 0.85(p <0.0002675)。据我们所知,这是首次在系统且全面的特定于患者的心脏模型上模拟计算流体动力学,并针对常规获得的临床基础事实进行验证。

著录项

  • 来源
  • 会议地点 New York NY(US);New York NY(US)
  • 作者单位

    Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ, USA ,Pattern Recognition Lab, Priedrich-Alexander-University, Erlangen, Germany;

    Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ, USA;

    Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ, USA;

    Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ, USA;

    Davis Heart and Lung Research Institute, Ohio State University, Columbus, OH, USA;

    Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ, USA;

    Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ, USA;

    Ultrasound, Siemens Healthcare, Mountain View, CA, USA;

    Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ, USA;

    Pattern Recognition Lab, Priedrich-Alexander-University, Erlangen, Germany;

    Image Analytics and Informatics, Siemens Corporate Research, Princeton, NJ, USA;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 心脏、血管(循环系)疾病;
  • 关键词

  • 入库时间 2022-08-26 14:00:10

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