Purpose

Coarctation of Aorta (CoA) is a congenital disease consisting of a narrowing that obstructs the systemic blood flow. This proof-of-concept study aimed to develop a framework for automatically and robustly personalizing aortic hemodynamic computations for the assessment of pre- and post-intervention CoA patients from 3D rotational angiography (3DRA) data.

Methods

We propose a framework that combines hemodynamic modelling and machine learning (ML) based techniques, and rely on 3DRA data for non-invasive pressure computation in CoA patients. The key features of our framework are a parameter estimation method for calibrating inlet and outlet boundary conditions, and regional mechanical wall properties, to ensure that the computational results match the patient-specific measurements, and an improved ML based pressure drop model capable of predicting the instantaneous pressure drop for a wide range of flow conditions and anatomical CoA variations.

Results

We evaluated the framework by investigating 6 patient datasets, under pre- and post-operative setting, and, since all calibration procedures converged successfully, the proposed approach is deemed robust. We compared the peak-to-peak and the cycle-averaged pressure drop computed using the reduced-order hemodynamic model with the catheter based measurements, before and after virtual and actual stenting. The mean absolute error for the peak-to-peak pressure drop, which is the most relevant measure for clinical decision making, was 2.98 mmHg for the pre- and 2.11 mmHg for the post-operative setting. Moreover, the proposed method is computationally efficient: the average execution time was of only \(2.1 \pm 0.8\) minutes on a standard hardware configuration.

Conclusion

The use of 3DRA for hemodynamic modelling could allow for a complete hemodynamic assessment, as well as virtual interventions or surgeries and predictive modeling. However, before such an approach can be used routinely, significant advancements are required for automating the workflow.

中文翻译：

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3D 旋转血管造影对主动脉缩窄的个性化术前和术后血流动力学评估

目的

主动脉缩窄 (CoA) 是一种先天性疾病，由阻塞全身血流的狭窄组成。这项概念验证研究旨在开发一个框架，用于自动和稳健地个性化主动脉血流动力学计算，以根据 3D 旋转血管造影 (3DRA) 数据评估干预前和干预后的 CoA 患者。

方法

我们提出了一个框架，该框架结合了血液动力学建模和基于机器学习 (ML) 的技术，并依靠 3DRA 数据对 CoA 患者进行无创压力计算。我们框架的主要特点是用于校准入口和出口边界条件和区域机械壁特性的参数估计方法，以确保计算结果与患者特定测量相匹配，以及改进的基于 ML 的压降模型能够预测各种流量条件和解剖 CoA 变化的瞬时压降。

结果

我们通过在术前和术后设置下调查 6 个患者数据集来评估框架，并且由于所有校准程序都成功收敛，因此所提出的方法被认为是稳健的。我们比较了使用降阶血液动力学模型计算的峰峰值和周期平均压降与基于导管的测量值，在虚拟和实际支架置入前后。峰峰值压降的平均绝对误差是临床决策制定最相关的指标，术前为 2.98 mmHg，术后为 2.11 mmHg。此外，所提出的方法计算效率高：在标准硬件配置上平均执行时间仅为\(2.1 \pm 0.8\)分钟。

结论

使用 3DRA 进行血液动力学建模可以进行完整的血液动力学评估，以及虚拟干预或手术和预测建模。然而，在常规使用这种方法之前，自动化工作流程需要取得重大进展。