Collaborative Research: Enhanced 4D-Flow MRI through Deep Data Assimilation for Hemodynamic Analysis of Cardiovascular Flows

合作研究：通过深度数据同化增强 4D-Flow MRI 用于心血管血流的血流动力学分析

• 批准号: 2246916
• 负责人: Amirhossein Arzani
• 金额: $ 9.15万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2246916&HistoricalAwards=false
• 关键词: Collaborative; Research; Enhanced; 4D; Flow

Forces resulting from blood flow interaction with walls of blood vessels have major impact on the initiation and progression of vascular diseases such as aneurysms, atherosclerosis, and vasospasms. Consequently, detailed and accurate blood flow analysis could be key to prognosis and treatment of such diseases. There are two popular modalities that are currently used to study 3D blood flow. The first is based on computational fluid dynamic (CFD) simulations. The second is through direct non-invasive imaging using techniques such as phase contrast magnetic resonance imaging (a.k.a 4D-Flow MRI). CFD requires accurate vascular geometry, model parameters, and estimates of boundary flow and initial conditions. These are time consuming and very difficult, if not impossible to estimate. Furthermore, the fidelity of CFD is limited by model assumptions. On the other hand, 4D-Flow MRI directly measures in-vivo volumetric blood flow velocities, but has low spatio-temporal resolution and the scans are contaminated by noise and image artifacts. The proposed project overcomes the limitations of both CFD and 4D-Flow MRI through a novel technique called deep data-assimilation. Here deep neural nets are used to model the blood flow. The training process imposes data fidelity with 4D-Flow MRI and simultaneously ensures that the physics of fluid flow and magnetic resonance are satisfied. The neural nets are then used to generate accurate dense spatio-temporal flow fields and flow dependent parameters such as wall shear stresses, vorticity etc. The ability to enhance 4D-Flow MRI will enable clinical researchers to investigate the impact of hemodynamics on the initiation and progression of vascular diseases. This will lead to novel physics-based flow image analysis tools for disease management that will significantly reduce cost and optimize treatment plans.The goal of the proposed project is to enable accurate and reliable hemodynamic analysis of cardio-vascular flows from time resolved three dimensional phase contrast magnetic resonance imaging (4D-Flow MRI). The proposed approach uses physics informed deep learning wherein time-varying flow (velocity and pressure) and field (magnetic moment) variables are modeled as deep neural nets. The training process fits 4D-Flow MRI data and also imposes blood flow physics (Navier-Stokes equation) and MRI acquisition physics (Bloch equations) as constraints. Creative design of loss functions in the learning process will achieve super-resolution, attenuate noise, and eliminate various image artifacts. Automatic differentiation will facilitate truncation error-free computation of velocity-dependent higher order hemodynamic parameters. Carefully designed in-vitro experiments will be used to validate and optimize the method. The proposed hybrid experimental and deep learning approach will create a new paradigm in cardiovascular flow research wherein the governing equations will be directly applied to low quality imaging data using deep learning to raise the reliability and accuracy to the level needed for scientific discovery. The project will provide opportunities to train graduate students in the latest deep-learning based techniques in engineering, engage undergraduate students in research through numerous programs at UW-Milwaukee and Northern Arizona University, and outreach to high school students belonging to marginalized communities through summer programs at UW-Milwaukee.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.

血流与血管壁相互作用产生的力对动脉瘤、动脉粥样硬化和血管痉挛等血管疾病的发生和进展具有重大影响。因此，详细而准确的血流分析可能是此类疾病的预后和治疗的关键。目前有两种流行的方法用于研究 3D 血流。第一个基于计算流体动力学 (CFD) 模拟。第二种是使用相衬磁共振成像（又名 4D-Flow MRI）等技术进行直接非侵入性成像。 CFD 需要准确的血管几何形状、模型参数以及边界流和初始条件的估计。这些都是耗时且非常困难的，甚至是不可能估计的。此外，CFD 的保真度受到模型假设的限制。另一方面，4D-Flow MRI 直接测量体内体积血流速度，但时空分辨率较低，并且扫描受到噪声和图像伪影的污染。该项目通过一种称为深度数据同化的新技术克服了 CFD 和 4D-Flow MRI 的局限性。这里深度神经网络用于模拟血流。训练过程通过 4D-Flow MRI 实现数据保真度，同时确保满足流体流动和磁共振的物理原理。然后，神经网络用于生成精确的密集时空流场和流动相关参数，例如壁剪切应力、涡度等。增强 4D 流 MRI 的能力将使临床研究人员能够研究血流动力学对启动和流动的影响。血管疾病的进展。这将为疾病管理带来新型的基于物理的血流图像分析工具，从而显着降低成本并优化治疗计划。拟议项目的目标是从时间分辨的三维阶段对心血管血流进行准确可靠的血流动力学分析对比磁共振成像（4D-Flow MRI）。所提出的方法使用物理知识深度学习，其中时变流（速度和压力）和场（磁矩）变量被建模为深度神经网络。训练过程适合 4D-Flow MRI 数据，并且还施加血流物理学（纳维-斯托克斯方程）和 MRI 采集物理学（布洛赫方程）作为约束。在学习过程中创造性地设计损失函数将实现超分辨率、衰减噪声、消除各种图像伪影。自动微分将有助于速度相关的高阶血流动力学参数的无截断误差计算。精心设计的体外实验将用于验证和优化该方法。所提出的混合实验和深度学习方法将创建心血管血流研究的新范例，其中控制方程将使用深度学习直接应用于低质量成像数据，以将可靠性和准确性提高到科学发现所需的水平。该项目将提供机会培训研究生最新的基于深度学习的工程技术，通过威斯康星大学密尔沃基分校和北亚利桑那大学的众多项目让本科生参与研究，并通过暑期项目向边缘化社区的高中生进行推广该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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

Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation

用于解决奇异扰动边界层问题的理论指导物理信息神经网络

• DOI: 10.1016/j.jcp.2022.111768
• 发表时间: 2023-01
• 期刊: Journal of Computational Physics
• 影响因子: 4.1
• 作者: Arzani, Amirhossein;Cassel, Kevin W.;D'Souza, Roshan M.
• 通讯作者: D'Souza, Roshan M.

ENHANCING CORRUPT CARDIOVASCULAR FLOW DATA WITH MACHINE LEARNING

通过机器学习增强损坏的心血管流量数据

• 发表时间: 2023-01
• 期刊: Bioengineering and Biotransport Conference
• 作者: Csala, Hunor;Arzani, Amirhossein
• 通讯作者: Arzani, Amirhossein