Geitner Carolin M, Becher Tobias, Frerichs Inéz, Weiler Norbert, Bates Jason H T, Wall Wolfgang A
Institute for Computational Mechanics, Department of Engineering Physics & Computation, TUM School of Engineering and Design, Technical University of Munich, Garching b. Muenchen, Germany.
Department of Anesthesiology and Intensive Care Medicine, University Medical Center Schleswig-Holstein, Campus Kiel, Kiel, Germany.
Int J Numer Method Biomed Eng. 2023 Sep;39(9):e3745. doi: 10.1002/cnm.3745. Epub 2023 Jul 4.
We present a new approach for physics-based computational modeling of diseased human lungs. Our main object is the development of a model that takes the novel step of incorporating the dynamics of airway recruitment/derecruitment into an anatomically accurate, spatially resolved model of respiratory system mechanics, and the relation of these dynamics to airway dimensions and the biophysical properties of the lining fluid. The importance of our approach is that it potentially allows for more accurate predictions of where mechanical stress foci arise in the lungs, since it is at these locations that injury is thought to arise and propagate from. We match the model to data from a patient with acute respiratory distress syndrome (ARDS) to demonstrate the potential of the model for revealing the underlying derangements in ARDS in a patient-specific manner. To achieve this, the specific geometry of the lung and its heterogeneous pattern of injury are extracted from medical CT images. The mechanical behavior of the model is tailored to the patient's respiratory mechanics using measured ventilation data. In retrospective simulations of various clinically performed, pressure-driven ventilation profiles, the model adequately reproduces clinical quantities measured in the patient such as tidal volume and change in pleural pressure. The model also exhibits physiologically reasonable lung recruitment dynamics and has the spatial resolution to allow the study of local mechanical quantities such as alveolar strains. This modeling approach advances our ability to perform patient-specific studies in silico, opening the way to personalized therapies that will optimize patient outcomes.
我们提出了一种基于物理学的患病人体肺部计算建模新方法。我们的主要目标是开发一种模型，该模型迈出了新的一步，即将气道开放/关闭的动力学纳入呼吸系统力学的解剖学精确、空间分辨模型中，并将这些动力学与气道尺寸以及衬里液的生物物理特性联系起来。我们方法的重要性在于，它有可能更准确地预测肺部机械应力集中点的位置，因为人们认为损伤正是在这些位置产生并扩散的。我们将该模型与一名急性呼吸窘迫综合征（ARDS）患者的数据进行匹配，以证明该模型以患者特异性方式揭示ARDS潜在紊乱情况的潜力。为实现这一目标，从医学CT图像中提取肺部的特定几何形状及其异质性损伤模式。利用测量的通气数据，使模型的力学行为与患者的呼吸力学相匹配。在对各种临床实施的压力驱动通气曲线进行回顾性模拟时，该模型能够充分再现患者测量的临床指标，如潮气量和胸膜压力变化。该模型还展现出生理上合理的肺开放动力学，并且具有空间分辨率，能够研究局部力学量，如肺泡应变。这种建模方法提高了我们在计算机上进行患者特异性研究的能力，为优化患者治疗效果的个性化疗法开辟了道路。
