主动脉瓣介导的血流模式致升主动脉重构的4D Flow MRI可视化预测模型研究

主动脉瓣病变介导的血流模式在升主动脉扩张的发生发展中发挥关键作用，其生物力学机制尚不明确，亟需无创的血流动力学评估手段实现在体研究。4D Flow MRI为解决这一问题提供了新方法，但其现有的空间分辨率、基于参考平面的定量方式等严重影响评估参数的精度、准确性和可视化应用。我们前期利用样条插值等数学建模工具开发了高精度图像处理方式，获取主动脉内连续的血流速度分布以提高检测精度。本项目拟在此基础上提取主动脉立体结构，通过医学图像配准插值，创建同步量化和可视化主动脉血流动力学和血管壁顺应性的方法；并将上述方法应用于主动脉瓣疾病动物模型，结合血管体外力学、病理和分子生物学检测，研究主动脉瓣病变介导的血流模式如何通过改变升主动脉所处力学环境，导致血管壁力学和组织学特性发生重构的力学响应机制，据此建立4D Flow MRI可视化模型预测升主动脉扩张趋势，以期为主动脉疾病精准治疗提供新的思路和理论依据。

The aortic valve-mediated flow patterns play a key role in the pathogenesis of ascending aortic dilatation in aortic valve disorders. However, its biomechanical mechanism is yet to be elucidated, which urges an in-vivo non-invasive method to evaluate the hemodynamic alterations. Four-dimensional flow MRI has provided a new approach, while its current bottlenecks including limited spatial resolution and the reliance on reference plane for quantification have interfered the precision, accuracy, and visualization application of its derived parameters. Utilizing mathematical modeling tools including spline interpolation to develop a high-precision image processing approach, our preliminary work has successfully acquired continuous distribution of flow velocity within the aorta. The present study intends to develop an approach to realize quantification and visualization of aortic hemodynamic forces and wall compliance synchronically via aortic 3D structure extraction as well as medical image registration and interpolation techniques. Along with in-vitro measurements of aortic wall mechanical properties, histopathology, and molecular biology analysis, we also intend to apply the above approach in miniature swine models with aortic valve disorders, aiming to elaborate on how aortic valve-mediated flow patterns alter the hemodynamic environment of the ascending aorta and induce vessel wall remodeling. Furthermore, the present study attempts to establish a 4D Flow MRI visualization model for prediction of ascending aortic dilatation tendency, thus providing novel vantage point and theoretical evidence for the precision management of aortic diseases.