RAPID: A Computational Model for Multiscale Investigation of Regional Lung Dynamics

RAPID：区域肺动力学多尺度研究的计算模型

• 批准号: 2034964
• 负责人: Alex Vadati
• 金额: $ 11.52万
• 依托单位: East Carolina University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2034964&HistoricalAwards=false
• 关键词: RAPID; Computational; Model; Multiscale; Investigation

This Rapid Response Research (RAPID) grant will offer new fundamental insights into the mechanical aspects of breathing and mechanical ventilation based on imaging data from hospitalized COVID-19 patients. While mechanical ventilation is often used, our scientific understanding of how it works is sparse. A multidisciplinary team of engineers and physicians will work together to better understand how the lungs respond to mechanical ventilation. This work will use imaging data from COVID-19 patients to create detailed computer models of the infected and inflamed lungs. These patient-specific computer simulations will enable mechanistic understanding of lung function in healthy and diseased states. The virtual lungs created in this study will then be used to simulate a digital twin of the COVID-19 infected lungs on mechanical ventilation. Thus, our fundamental knowledge of the mechanical aspects of lung disease and the physical response to ventilation will be advanced. Furthermore, the computer models developed in this project will be used as instructional material for multiple university courses. This will introduce low-income and first-generation college students from rural communities to cutting-edge modeling and simulation techniques. This experience can have a profound impact on these students' interest in foundational research and career goals. Ventilation and respiration events in the lungs span multiple length and time scales. Furthermore, these events involve multiple physical phenomena, including deformation and motion of the lung parenchyme, fluid-solid interaction between air, airway walls and the alveoli, and finally diffusion and gas exchange with the blood flowing through the alveolar capillaries. While modest progress has been made towards understanding the mechanics of the lung at the macroscale, our knowledge of the microscale and mesoscale mechanics of the lung at the level of alveoli, the smallest functional unit of the lung, remains rudimentary. Our proposed research on multiscale and multiphysics computational modeling of the lung in a disease state, based on CT imaging in COVID-19 subjects, offers the potential to advance our fundamental and mechanistic knowledge of lung dynamics. Understanding and exploring the mechanisms of reduced alveolar diffusion in COVID-19 patients through patient-specific multi-physics and multiscale modeling can advance our knowledge base and provide new insights into the role of mechanics in lung inflammation. Furthermore, multiscale simulation of the lung using an acute inflammation model and CT data can inform and educate about the science of inflammation-induced changes in the lungs and the alveolar-capillary mechanics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项快速反应研究（RAPID）资助将根据住院COVID-19患者的成像数据，为呼吸和机械通气的机械方面提供新的基本见解。虽然经常使用机械通气，但我们对其工作原理的科学理解很少。 一个由工程师和医生组成的多学科团队将共同努力，以更好地了解肺部如何对机械通气作出反应。 这项工作将使用COVID-19患者的成像数据来创建感染和发炎肺部的详细计算机模型。 这些特定于患者的计算机模拟将能够从机制上理解健康和疾病状态下的肺功能。在这项研究中创建的虚拟肺将用于模拟机械通气的COVID-19感染肺的数字孪生。 因此，我们的基本知识的机械方面的肺部疾病和物理反应的通风将先进。此外，该项目开发的计算机模型将用作多个大学课程的教学材料。 这将向来自农村社区的低收入和第一代大学生介绍尖端的建模和模拟技术。这种经验可以对这些学生的基础研究和职业目标的兴趣产生深远的影响。肺中的通气和呼吸事件跨越多个长度和时间尺度。此外，这些事件涉及多种物理现象，包括肺实质的变形和运动，空气、气道壁和肺泡之间的流体-固体相互作用，以及最终与流过肺泡毛细血管的血液的扩散和气体交换。虽然在宏观尺度上对肺的力学的理解取得了适度的进展，但我们对肺的最小功能单元肺泡水平上的肺的微观和中尺度力学的知识仍然是基本的。我们提出的基于COVID-19受试者CT成像的疾病状态下肺的多尺度和多物理场计算建模研究，为推进我们对肺动力学的基础和机制知识提供了潜力。通过患者特定的多物理场和多尺度建模来理解和探索COVID-19患者肺泡扩散减少的机制，可以推进我们的知识基础，并为肺部炎症中力学的作用提供新的见解。此外，使用急性炎症模型和CT数据对肺部进行多尺度模拟，可以为炎症引起的肺部变化和肺泡毛细血管力学提供信息和教育。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Physics-Based Multiscale Model of Mechanical Ventilation of COVID-19-Infected Lungs

基于物理的 COVID-19 感染肺部机械通气多尺度模型

• 发表时间: 2022
• 期刊: BMES 2022 Annual Meeting
• 作者: Middleton S;Peach MS;Maddipati V;George S;Vadati A
• 通讯作者: Vadati A

Reconciling the Mechanical Properties of Lung Tissue at the Meso- and Microscale

协调肺组织在介观和微观尺度上的力学特性

• 发表时间: 2021
• 期刊: BMES Annual Meeting
• 作者: Dimbath, E;George, S;Vahdati, A
• 通讯作者: Vahdati, A

Physics-based in silico modelling of microvascular pulmonary perfusion in COVID-19

• DOI: 10.1177/09544119241241550
• 发表时间: 2024-04-02
• 期刊: PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE
• 影响因子: 1.8
• 作者: Dimbath，Elizabeth;Middleton，Shea;Vadati，Alex
• 通讯作者: Vadati，Alex

MULTI-SCALE PHYSICS-BASED COMPUTATIONAL MODEL OF MECHANICAL VENTILATION IN COVID-19 PATIENTS

COVID-19 患者机械通气的多尺度物理计算模型

• 发表时间: 2023
• 期刊: 28th Congress of the European Society of Biomechanics
• 作者: Middleton S;Peach MS;George S;Vadati A
• 通讯作者: Vadati A

VIRTUAL TENSILE TEST EXPERIMENTS TO RECONCILE THE MESO- AND MICRO-SCALE MECHANICAL PROPERTIES OF THE LUNG PARENCHYMA

虚拟拉伸测试实验以协调肺实质的细观和微观尺度机械特性

• 发表时间: 2023
• 期刊: Bioengineering and Biotransport Conference
• 作者: Dimbath E;de Castro Brás L;George S;Vadati A
• 通讯作者: Vadati A