SCH: A physics-informed machine learning approach to dynamic blood flow analysis from static subtraction computed tomographic angiography imaging

最近的研究表明，血流与血管壁的相互作用在心血管疾病的进展中起着重要作用。准确量化血流或血流动力学相互作用可能导致患者特异性治疗的方法，从而导致更好的治疗和降低死亡率。在该项目中，研究人员将开发非侵入性推断复杂动态血流动力学行为的技术，使用常用的医学成像模式，通常用于生成静态解剖图像以分析血管结构。在这个项目中，研究人员建议使用基于深度学习的处理方法开发一种新的血流物理模型。这将使研究人员推断出动态的、时间分辨的三维血流速度和相对压力场。结果将用于准确计算相关血流动力学因子。该项目将培养一批掌握最新数据驱动的工程领域深度学习技术的研究生。它将通过威斯康星大学密尔沃基分校和北亚利桑那大学的成熟项目吸引本科生参与研究。面向高中生，特别是那些来自弱势群体的高中生，将通过威斯康星大学密尔沃基分校的暑期项目来实现。这个项目的目标是准确的图像为基础的血流动力学分析使用常用的图像。对比浓度、三维血流速度和相对压力将被建模为深度神经网络。训练神经网络将涉及一个损失函数，该函数将来自时间戳sCTA信号图的实际数据与使用用于模拟对比度浓度的神经网络的前向评估计算的线积分生成的预测信号图相匹配。此外，血流和对比平流扩散物理将被用作溶液过程中的约束。系统噪声将通过贝叶斯公式的深度学习算法来处理。神经网络公式将允许高分辨率采样血液速度和相对压力场，并使用自动分化精确计算速度衍生的血液动力学参数。这些方法将通过粒子图像测速法的数值和体外流动实验进行验证。通过从迄今为止被认为是静态的数据中估计血流动力学数据，所提出的研究最大限度地提高了从sCTA成像数据中得出的推断，而无需额外的计算机断层扫描硬件或新的扫描协议。该奖项反映了美国国家科学基金会的法定使命，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。

Recent investigations have shown that interactions of blood flow with blood vessel walls plays an important role in the progression of cardiovascular diseases. Accurately quantifying blood flow or hemodynamic interactions could lead to methods for patient-specific therapies that result in better treatments and reduced mortality. In this project, the researchers will develop techniques to non-invasively inferring the complex, dynamic hemodynamic behavior using a commonly used medical imaging modality that is typically used to produce static anatomical images for analyzing blood vessel structure. In this project, the researchers propose to develop a novel physics-informed model of the blood flow using a deep-learning based processing method. This will allow the researchers infer dynamic time-resolved three-dimensional blood velocity and relative pressure field. The results will be used to accurately compute relevant hemodynamic factors. This project will train a cohort of graduate students in the latest data-driven deep learning techniques in engineering. It will engage undergraduate students in research through well-established programs at UW Milwaukee and Northern Arizona University. Outreach to high school students, particularly those belonging to under-represented communities will be accomplished through summer programs at UW Milwaukee. The goal of this project is accurate image-based hemodynamic analysis using commonly available images. Contrast concentration, three-dimensional blood velocity, and relative pressure will be modeled as deep neural nets. Training the neural nets will involve a loss function that matches actual data from time-stamped sCTA sinograms with predicted sinograms generated using line integrals computed from forward evaluation of the neural net used to model the contrast concentration. Additionally, blood flow and contrast advection-diffusion physics will be used as constraints in the solution process. System noise will be handled through a Bayesian formulation of the deep learning algorithm. The neural net formulation will allow high resolution sampling of the blood velocity and relative pressure fields and accurate computation of velocity-derive hemodynamic parameters using automatic differentiation. The methods will be validated using numerical and in vitro flow experiments using particle image velocimetry. By enabling the estimation of hemodynamic data from what, until now, has been considered to be static data, the proposed research maximizes inference that can be derived from sCTA imaging data without the need for additional computed tomography hardware or new scan protocols.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.