Aim 

A validated algorithm for automatic aortic arch measurements in aortic coarctation (CoA) patients could standardize procedures for clinical planning.

Methods 

The model-based assessment of the aortic arch anatomy consisted of three steps: first, machine-learning-based algorithms were trained on 212 three-dimensional magnetic resonance (MR) data to automatically allocate the aortic arch position in patients and segment the aortic arch; second, for each CoA patient (N = 33), the min/max aortic arch diameters were measured using the proposed software, manually and automatically, from noncontrast-enhanced three-dimensional steady-state free precession MRI sequence at five selected sites and compared (‘internal comparison’ referring to the same environment); third, moreover, the same min/max aortic arch diameters were compared, obtaining them independently, manually from common MR management software (MR Viewforum) and automatically from the model (external comparison). The measured sites were: aortic sinus, sino–tubular junction, mid-ascending aorta, transverse arch and thoracoabdominal aorta at the level of the diaphragm.

Results 

Manual and software-assisted measurements showed a good agreement: the difference between diameter measurements was not statistically significant (at α = 0.05), with only one exception, for both internal and external comparison. A high coefficient of correlation was attained for both maximum and minimum diameters in each site (for internal comparison, R > 0.73 for every site, with P < 2 × 10⁻⁵). Notably, in tricuspid aortic valve patients external comparison showed no statistically significant difference at any measurement sites.

Conclusion 

The automatically derived aortic arch model, starting from three-dimensional MR images, could be a support to take the measurements in CoA patients and to quickly provide a patient-specific model of aortic arch anomalies.

中文翻译：

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针对患者的三维主动脉弓三维模型，可进行自动测量：主动脉缩窄的临床验证。

目标 

经过验证的用于主动脉缩窄（CoA）患者的自动主动脉弓测量的算法可以标准化临床计划程序。

方法 

基于模型的主动脉弓解剖评估包括三个步骤：首先，在212个三维磁共振（MR）数据上训练了基于机器学习的算法，以自动分配患者的主动脉弓位置并分割主动脉弓; 其次，对于每个CoA患者（N= 33），使用建议的软件手动和自动地从五个选定部位的无对比度增强的三维稳态自由进动MRI序列中手动和自动测量主动脉弓的最小/最大直径，并进行比较（“内部比较”指的是相同的环境）；第三，此外，比较相同的最小/最大主动脉弓直径，分别从通用MR管理软件（MR Viewforum）手动获得模型，并从模型自动获得（外部比较）。被测部位为：were肌水平的主动脉窦，窦管交界处，升中主动脉，横弓和胸腹主动脉。

结果 

手动和软件辅助测量显示出很好的一致性：对于内部和外部比较，直径测量之间的差异无统计学意义（α= 0.05），只有一个例外。在每个部位的最大和最小直径上都获得了很高的相关系数（内部比较，每个部位的R > 0.73，P <2×10 ^-5）。值得注意的是，在三尖瓣主动脉瓣患者中，外部比较显示在任何测量部位均无统计学显着差异。

结论 

从三维MR图像开始，自动获取的主动脉弓模型可以为在CoA患者中进行测量并快速提供针对患者的主动脉弓异常模型提供支持。