Informing 4D flow MRI haemodynamic outputs with data science, mathematical models and scale-resolving computational fluid dynamics

通过数据科学、数学模型和尺度解析计算流体动力学为 4D 流 MRI 血液动力学输出提供信息

• 批准号: EP/X028321/1
• 负责人: Emily Manchester
• 金额: $ 44.73万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX028321%2F1
• 关键词: Informing; 4D; flow; MRI; haemodynamic

In the UK, heart diseases cause around a quarter of all deaths and related healthcare costs are estimated at £9 billion annually. The aorta is the largest artery in the body, connected directly to the heart. Aortic disease is one of the most common types of cardiovascular disease and can be extremely life threatening. Wall shear stress is the shearing force exerted by blood flow on the inner surface of the arterial wall and is an important biomarker for arterial wall diseases. Similarly, blood flow disturbances in the blood like turbulence are also linked with heart disease. 4D flow magnetic resonance imaging (MRI) is a type of MRI sequence which measures blood flow velocities in arteries and is used both in clinic and in cardiovascular research. In clinic, MRI is used to diagnose heart disease and evaluate treatments. Currently, important metrics like wall shear stress and turbulence cannot be accurately evaluated from 4D flow MRI, although it may be possible with the development of new methods.In this research fellowship I will develop new methods to radically improve the haemodynamic output capabilities of 4D flow MRI. This will be achieved using computational fluid dynamics, data science and machine learning methods. MRI sequences have various settings determined by the user and currently, settings are optimised for velocity field acquisition, not wall shear stress or turbulence. To enable accurate measurement these parameters, optimal MRI settings need to be established. I will develop a tool for 'virtual' 4D flow MRI which can replicate real 4D flow MRI sequences. The tool will then be used to optimise 4D flow MRI sequences virtually. Next, I will develop a super resolution-based machine learning model which can improve 4D flow MRI haemodynamic outputs. The model will be trained using a combination of high-resolution and low-resolution computational fluid dynamic simulations of various aortas. Developed models will be validated using new multi-resolution 4D flow MRI scans and high-resolution computational fluid dynamic aorta simulations.This research will be developed via collaboration with experts and state-of-the-art facilities found within the Department for Mechanical, Aerospace and Civil Engineering and the Division of Cardiovascular Sciences at the University of Manchester. The multidisciplinary project will draw expertise from researchers, clinicians and life scientists in the UK, as well as make use of MRI facilities at the BHF Manchester Centre for Heart & Lung Magnetic Resonance Research enabling new scan acquisitions. Broadly, this research will advance data-driven fluid dynamics techniques and MR imaging methods, directly impacting healthcare sectors as well as benefitting fluid dynamics, data-science and cardiovascular research communities. In research, improved 4D flow MRI would enable larger-scale studies and likely improve computational fluid dynamic studies informed with MRI data. In clinical settings, access to a wider range of haemodynamic parameters from MRI would enable development of new diagnostic tools and new treatment tools for aortic disease, improving patient outcomes. At the end of the project, I will have developed novel methods to improve 4D flow MRI outputs at different stages - from MRI acquisition through to image post-processing.

在英国，大约四分之一的死亡是由心脏病引起的，每年相关的医疗费用估计为 90 亿英镑。 主动脉是体内最大的动脉，与心脏直接相关，是最常见的类型之一。壁剪切应力是血流对动脉壁内表面施加的剪切力，是动脉壁疾病的重要生物标志物，类似地，血液中的血流紊乱（如湍流）也是如此。还4D 血流磁共振成像 (MRI) 是一种测量动脉血流速度的 MRI 序列，用于临床和心血管研究。在临床上，MRI 用于诊断心脏病和评估治疗。目前，诸如壁剪切应力和湍流等重要指标无法通过 4D 流 MRI 进行准确评估，尽管随着新方法的开发，这可能成为可能。在这项研究奖学金中，我将开发新方法，从根本上提高 4D 流 MRI 的血液动力学输出能力。 4D 流 MRI。这将通过计算流体动力学、数据科学和机器学习方法来实现，MRI 序列具有由用户确定的各种设置，目前，设置针对速度场采集进行了优化，而不是壁剪切应力或湍流。测量这些参数后，需要建立最佳 MRI 设置，我将开发一种用于“虚拟”4D 流式 MRI 的工具，该工具可以复制真实的 4D 流式 MRI 序列，然后使用该工具来优化 4D 流式 MRI。接下来，我将开发一个基于超分辨率的机器学习模型，该模型可以结合使用各种主动脉的高分辨率和低分辨率计算流体动力学模拟来训练该模型。模型将使用新的多分辨率 4D 流 MRI 扫描和高分辨率计算流体动力学主动脉模拟进行验证。这项研究将通过与该部门内的专家和最先进的设施合作进行开发该多学科项目将吸收英国研究人员、新移民和生命科学家的专业知识，并利用 BHF 曼彻斯特中心的 MRI 设施进行研究。心肺磁共振研究促进了新的扫描采集，广泛而言，这项研究将推进数据驱动的流体动力学技术和 MR 成像方法，直接影响医疗保健领域，并造福于流体动力学、数据科学和心血管研究界。改进的4D 流 MRI 将实现更大规模的研究，并可能改善基于 MRI 数据的计算流体动力学研究。在临床环境中，从 MRI 获取更广泛的血流动力学参数将有助于开发主动脉疾病的新诊断工具和新治疗工具。在项目结束时，我将开发出新的方法来改善不同阶段（从 MRI 采集到图像后处理）的 4D 流 MRI 输出。