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: 2103560
• 负责人: Roshan D'souza
• 金额: $ 29.85万
• 依托单位: University of Wisconsin-Milwaukee
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103560&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引起的力对血管疾病（例如动脉瘤，动脉粥样硬化和Vasospasms）的起始和进展产生重大影响。因此，详细和准确的血流分析可能是预后和治疗此类疾病的关键。目前有两种流行方式用于研究3D血流。第一个基于计算流体动力学（CFD）模拟。第二个是通过使用诸如相对比磁共振成像（又称4D-Flow MRI）的技术直接非侵入成像。 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。这些很耗时，而且非常困难，即使不是不可能估计。此外，CFD的保真度受模型假设的限制。另一方面，4D流MRI直接测量体内体积的血流速度，但时空分辨率较低，并且扫描被噪声和图像伪影污染。提出的项目通过一种称为深度数据相似的新技术克服了CFD和4D-Flow MRI的局限性。在这里，深神网用于对血流进行建模。训练过程通过4D流MRI施加数据保真度，并同时确保满足流体流和磁共振的物理。然后，神经网用于生成准确的密集时空流动场和诸如壁剪应力，涡度等方面的依赖性参数等。血管疾病的进展。这将导致疾病管理的新型基于物理的流程图像分析工具将大大降低成本并优化治疗计划。拟议项目的目的是从时间解决三维阶段对心血管流动的准确可靠的血液动力学分析对比度磁共振成像（4D流MRI）。所提出的方法使用物理知情的深度学习，其中随时间变化的流动（速度和压力）以及场（磁矩）变量被建模为深神经网。训练过程符合4D流MRI数据，还构成了血流物理（Navier-Stokes方程）和MRI获取物理（BLOCH方程）作为约束。学习过程中损失功能的创造性设计将实现超分辨率，减轻噪音并消除各种图像工件。自动分化将促进速度依赖性高阶血液动力学参数的无截断计算。精心设计的体外实验将用于验证和优化该方法。拟议的混合实验和深度学习方法将在心血管流动研究中创建一个新的范式，其中管理方程将直接使用深度学习直接应用于低质量的成像数据，以提高科学发现所需水平的可靠性和准确性。该项目将提供机会，以培训研究生的最新基于深度学习的工程技术，通过UW-Milwaukee和北亚利桑那大学的众多课程吸引本科生研究，并通过夏季计划向属于边缘化社区的高中生推广在UW-Milwaukee上，该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来评估值得支持的。