Piersanti Roberto, Bradley Ryan, Ali Syed Yusuf, Quarteroni Alfio, Dede' Luca, Trayanova Natalia A
MOX - Laboratory of Modeling and Scientific Computing, Dipartimento di Matematica, Politecnico di Milano, Milano, Italy.
ADVANCE - Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, USA.
ArXiv. 2024 Oct 15:arXiv:2410.11601v1.
A key component in developing atrial digital twins (ADT) - virtual representations of patients' atria - is the accurate prescription of myocardial fibers which are essential for the tissue characterization. Due to the difficulty of reconstructing atrial fibers from medical imaging, a widely used strategy for fiber generation in ADT relies on mathematical models. Existing methodologies utilze semi-automatic approaches, are tailored to specific morphologies, and lack rigorous validation against imaging fiber data. In this study, we introduce a novel atrial Laplace-Dirichlet-Rule-Based Method (LDRBM) for prescribing highly detailed myofiber orientations and providing robust regional annotation in bi-atrial morphologies of any complexity. The robustness of our approach is verified in eight extremely detailed bi-atrial geometries, derived from a sub-millimiter Diffusion-Tensor-Magnetic-Resonance Imaging (DTMRI) human atrial fiber dataset. We validate the LDRBM by quantitatively recreating each of the DTMRI fiber architectures: a comprehensive comparison with DTMRI ground truth data is conducted, investigating differences between electrophysiology (EP) simulations provided by either LDRBM and DTMRI fibers. Finally, we demonstrate that the novel LDRBM outperforms current state-of-the-art fiber models, confirming the exceptional accuracy of our methodology and the critical importance of incorporating detailed fiber orientations in EP simulations. Ultimately, this work represents a fundamental step toward the development of physics-based digital twins of the human atria, establishing a new standard for prescribing fibers in ADT.
开发心房数字孪生模型（ADT，即患者心房的虚拟表示）的一个关键组成部分是精确规定心肌纤维，这对于组织特征描述至关重要。由于从医学影像重建心房纤维存在困难，ADT中广泛使用的纤维生成策略依赖于数学模型。现有方法采用半自动方法，针对特定形态进行定制，并且缺乏针对成像纤维数据的严格验证。在本研究中，我们引入了一种新颖的基于拉普拉斯 - 狄利克雷规则的心房方法（LDRBM），用于规定高度详细的肌纤维方向，并在任何复杂程度的双心房形态中提供可靠的区域注释。我们的方法的稳健性在从亚毫米扩散张量磁共振成像（DTMRI）人类心房纤维数据集中导出的八个极其详细的双心房几何模型中得到了验证。我们通过定量重现每个DTMRI纤维结构来验证LDRBM：与DTMRI真实数据进行了全面比较，研究了由LDRBM和DTMRI纤维提供的电生理（EP）模拟之间的差异。最后，我们证明了新颖的LDRBM优于当前的先进纤维模型，证实了我们方法的卓越准确性以及在EP模拟中纳入详细纤维方向的至关重要性。最终，这项工作代表了朝着基于物理的人类心房数字孪生模型发展迈出的重要一步，为ADT中的纤维规定建立了新的标准。
