Markodimitrakis Emmanouil, Lin Siyu, Koutoulakis Emmanouil, Marín-Castrillón Diana Marcela, Tovar Sáez Francisco Aarón, Leclerc Sarah, Bernard Chloé, Boucher Arnaud, Presles Benoit, Bouchot Olivier, Decourselle Thomas, Morgant Marie-Catherine, Lalande Alain
ImViA Laboratory, EA 7535, University of Burgundy and Franche-Comte, 21000 Dijon, France.
Department of Cardio-Vascular and Thoracic Surgery, University Hospital of Dijon, 21079 Dijon, France.
J Clin Med. 2023 Jan 4;12(2):402. doi: 10.3390/jcm12020402.
Ascending aortic aneurysm is a pathology that is important to be supervised and treated. During the years the aorta dilates, it becomes stiff, and its elastic properties decrease. In some cases, the aortic wall can rupture leading to aortic dissection with a high mortality rate. The main reference standard to measure when the patient needs to undertake surgery is the aortic diameter. However, the aortic diameter was shown not to be sufficient to predict aortic dissection, implying other characteristics should be considered. Therefore, the main objective of this work is to assess in-vivo the elastic properties of four different quadrants of the ascending aorta and compare the results with equivalent properties obtained ex-vivo. The database consists of 73 cine-MRI sequences of thoracic aorta acquired in axial orientation at the level of the pulmonary trunk. All the patients have dilated aorta and surgery is required. The exams were acquired just prior to surgery, each consisting of 30 slices on average across the cardiac cycle. Multiple deep learning architectures have been explored with different hyperparameters and settings to automatically segment the contour of the aorta on each image and then automatically calculate the aortic compliance. A semantic segmentation U-Net network outperforms the rest explored networks with a Dice score of 98.09% (±0.96%) and a Hausdorff distance of 4.88 mm (±1.70 mm). Local aortic compliance and local aortic wall strain were calculated from the segmented surfaces for each quadrant and then compared with elastic properties obtained ex-vivo. Good agreement was observed between Young's modulus and in-vivo strain. Our results suggest that the lateral and posterior quadrants are the stiffest. In contrast, the medial and anterior quadrants have the lowest aortic stiffness. The in-vivo stiffness tendency agrees with the values obtained ex-vivo. We can conclude that our automatic segmentation method is robust and compatible with clinical practice (thanks to a graphical user interface), while the in-vivo elastic properties are reliable and compatible with the ex-vivo ones.
升主动脉瘤是一种需要密切监测和治疗的病理状况。随着岁月的推移，主动脉会扩张、变硬，其弹性特性会降低。在某些情况下，主动脉壁可能破裂，导致主动脉夹层，死亡率很高。衡量患者何时需要进行手术的主要参考标准是主动脉直径。然而，研究表明主动脉直径不足以预测主动脉夹层，这意味着还应考虑其他特征。因此，这项工作的主要目的是在体内评估升主动脉四个不同象限的弹性特性，并将结果与体外获得的等效特性进行比较。该数据库由73个在肺动脉水平轴向采集的胸主动脉电影磁共振成像序列组成。所有患者的主动脉均已扩张，需要进行手术。这些检查是在手术前进行的，每个检查在心动周期中平均包含30层图像。研究人员探索了多种具有不同超参数和设置的深度学习架构，以自动分割每个图像上的主动脉轮廓，然后自动计算主动脉顺应性。一个语义分割U-Net网络的表现优于其他探索的网络，其Dice评分为98.09%（±0.96%），豪斯多夫距离为4.88毫米（±1.70毫米）。从每个象限的分割表面计算局部主动脉顺应性和局部主动脉壁应变，然后与体外获得的弹性特性进行比较。观察到杨氏模量与体内应变之间有良好的一致性。我们的结果表明，外侧和后侧象限最硬。相比之下，内侧和前侧象限的主动脉僵硬度最低。体内僵硬度趋势与体外获得的值一致。我们可以得出结论，我们的自动分割方法稳健且与临床实践兼容（得益于图形用户界面），而体内弹性特性可靠且与体外特性兼容。
