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<p>Experimental validation of dynamic left-ventricular (LV) models remains limited due to the difficulty of simultaneously resolving three-dimensional (3D) wall kinematics and volumetric intraventricular transport. We present a novel experimental framework that provides co-registered, time-resolved 3D reconstructions of both ventricular wall motion and Lagrangian flow in a compliant LV model. By integrating deep-learning object identification and segmentation (SAM2) with 3D Particle Tracking Velocimetry (3D-PTV) and multi-media ray tracing, we establish a high-fidelity “physical twin” capable of capturing fluid–structure interaction (FSI) across physiological heart rates. Using this framework, we quantify the spatial distributions of wall shear stress (WSS) based on FSI metrics, including time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and relative residence time (RRT). We observe a moderate inverse relationship between regional wall acceleration and TAWSS. In addition, RRT shows weak correspondence with directly measured Lagrangian residence time (LRT) derived from particle trajectories, indicating that surface-based Eulerian proxies may not reliably reflect volumetric transport in deforming chambers. The physical trends revealed in this experiment provide a benchmark for assessing computational FSI simulations and highlight the value of Lagrangian descriptors for characterizing intraventricular transport relevant to thrombogenic risk.</p>

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Simultaneous 3D Quantification of Fluid–Structure Interaction in a Patient-Averaged Left Ventricle: A Lagrangian Experimental Framework

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月24日

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  • Omer Hadar,
  • Boaz Meivar,
  • Shai Avidan,
  • Alex Liberzon

摘要

Experimental validation of dynamic left-ventricular (LV) models remains limited due to the difficulty of simultaneously resolving three-dimensional (3D) wall kinematics and volumetric intraventricular transport. We present a novel experimental framework that provides co-registered, time-resolved 3D reconstructions of both ventricular wall motion and Lagrangian flow in a compliant LV model. By integrating deep-learning object identification and segmentation (SAM2) with 3D Particle Tracking Velocimetry (3D-PTV) and multi-media ray tracing, we establish a high-fidelity “physical twin” capable of capturing fluid–structure interaction (FSI) across physiological heart rates. Using this framework, we quantify the spatial distributions of wall shear stress (WSS) based on FSI metrics, including time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and relative residence time (RRT). We observe a moderate inverse relationship between regional wall acceleration and TAWSS. In addition, RRT shows weak correspondence with directly measured Lagrangian residence time (LRT) derived from particle trajectories, indicating that surface-based Eulerian proxies may not reliably reflect volumetric transport in deforming chambers. The physical trends revealed in this experiment provide a benchmark for assessing computational FSI simulations and highlight the value of Lagrangian descriptors for characterizing intraventricular transport relevant to thrombogenic risk.