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发展中起着重要的作用。准确地量化血流或血流动力学相互作用可能导致针对患者的治疗方法，从而产生更好的治疗和降低死亡率。在这个项目中，研究人员将开发技术，使用通常用于产生用于分析血管结构的静态解剖图像的常用医学成像模式，非侵入性地推断复杂的动态血流动力学行为。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来开发一种新的物理信息血流模型。这将使研究人员能够推断动态的时间分辨率三维血流速度和相对压力场。结果将被用于准确计算相关的血流动力学因素。这个项目将培训一批研究生，学习工程学中最新的数据驱动的深度学习技术。它将通过密尔沃基大学和北亚利桑那大学成熟的项目吸引本科生参与研究。将通过密尔沃基大学密尔沃基分校的暑期项目，向高中生，特别是那些属于代表性不足社区的高中生开展外联活动。该项目的目标是使用常用图像进行准确的基于图像的血流动力学分析。对比度浓度、三维血流速度和相对压力将被建模为深神经网络。训练神经网络将涉及损失函数，该损失函数将来自带有时间戳的SCTA正弦图的实际数据与使用从用于对对比度浓度建模的神经网络的正向评估计算的线积分生成的预测正弦图相匹配。此外，在求解过程中将使用血液流动和对比度平流-扩散物理作为约束。系统噪声将通过深度学习算法的贝叶斯公式来处理。神经网络公式将允许对血流速度场和相对压力场进行高分辨率采样，并使用自动微分精确计算速度派生的血流动力学参数。这些方法将通过使用粒子图像测速仪的数值和体外流动实验来验证。通过能够从到目前为止被认为是静态数据的血液动力学数据进行估计，拟议的研究最大化了可以从SCTA成像数据得出的推断，而不需要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NSF的法定使命，并通过使用基金会的智力优势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.