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

SCH：一种基于物理的机器学习方法，用于从静态减影计算机断层血管造影成像中进行动态血流分析

• 批准号: 2205265
• 负责人: Roshan D'souza
• 金额: $ 110万
• 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: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2205265&HistoricalAwards=false
• 关键词: SCH; physics; informed; machine; learning

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.

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重要作用。 准确量化血流或血液动力学相互作用可能会导致患者特异性疗法的方法，从而可以更好地治疗和降低死亡率。在该项目中，研究人员将使用常用的医学成像方式开发非侵入性的技术，以推断复杂的动态血液动力学行为，该模式通常用于产生静态解剖图像，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加工方法开发一种新型的血流物理模型。这将使研究人员推断动态时间分辨的三维血液速度和相对压力场。结果将用于准确计算相关的血液动力学因素。该项目将培训一群研究生，以最新的数据驱动工程技术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的公认课程与本科生一起研究。向高中生，尤其是属于代表性不足社区的学生的宣传将通过密尔沃基大学夏季计划来完成。该项目的目的是使用常用图像进行准确的基于图像的血液动力学分析。对比度浓度，三维血液速度和相对压力将建模为深神经网。训练神经网将涉及一个损失函数，该损失函数与时间stamp的SCTA辛图与使用从前向评估进行的线积分产生的预测曲目与用于对比度浓度建模的神经网的预测曲目。另外，血流和对比度扩散物理学将用作溶液过程中的约束。系统噪声将通过深度学习算法的贝叶斯公式来处理。神经净配方将允许使用自动分化的速度 - 源性血流动力学参数的血液速度和相对压力场进行高分辨率采样。该方法将使用数值和体外流量实验使用粒子图像速度法进行验证。通过启用血液动力学数据的估计，到目前为止，该数据被认为是静态数据，拟议的研究最大化了可以从SCTA成像数据中得出的推理，而无需其他计算机层析成像硬件或新的扫描协议。 NSF的法定使命，并使用基金会的知识分子优点和更广泛的影响审查标准来评估值得支持。

项目成果

Ensemble physics informed neural networks: A framework to improve inverse transport modeling in heterogeneous domains

集合物理通知神经网络：改进异构域逆向传输建模的框架

• DOI: 10.1063/5.0150016
• 发表时间: 2023
• 期刊: Physics of Fluids
• 影响因子: 4.6
• 作者: Aliakbari, Maryam;Soltany Sadrabadi, Mohammadreza;Vadasz, Peter;Arzani, Amirhossein
• 通讯作者: Arzani, Amirhossein