Comparison of In-Vivo and Ex-Vivo Ascending Aorta Elastic Properties through Automatic Deep Learning Segmentation of Cine-MRI and Biomechanical Testing.
Comparison of In-Vivo and Ex-Vivo Ascending Aorta Elastic Properties through Automatic Deep Learning Segmentation of Cine-MRI and Biomechanical Testing.
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DOI：
10.3390/jcm12020402
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发表时间：
2023-01-04
影响因子：
3.9
通讯作者：
Lalande, Alain
中科院分区：
文献类型：
--
作者：
Markodimitrakis, Emmanouil;Lin, Siyu;Koutoulakis, Emmanouil;Marin-Castrillon, Diana Marcela;Saez, Francisco Aaron Tovar;Leclerc, Sarah;Bernard, Chloe;Boucher, Arnaud;Presles, Benoit;Bouchot, Olivier;Decourselle, Thomas;Morgant, Marie-Catherine;Lalande, Alain
升主动脉瘤是一种病理，是重要的监督和治疗。随着年龄的增长，主动脉扩张，变得僵硬，弹性下降。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。当患者需要进行手术时，测量的主要参考标准是主动脉直径。然而，主动脉直径不足以预测主动脉夹层，这意味着应考虑其他特征。因此，这项工作的主要目的是评估在体内的四个不同象限的升主动脉的弹性性能，并比较结果与等效性能获得离体。该数据库由73个在肺动脉干水平轴向采集的胸主动脉电影MRI序列组成。所有患者都有主动脉扩张，需要手术治疗。这些检查是在手术前获得的，每个检查平均包括30个心动周期的切片。已经使用不同的超参数和设置探索了多种深度学习架构，以自动分割每张图像上的主动脉轮廓，然后自动计算主动脉顺应性。语义分割U-Net网络的Dice得分为98.09%（±0.96%），Hausdorff距离为4.88 mm（±1.70 mm），优于其他已探索的网络。局部主动脉顺应性和局部主动脉壁应变计算从分割的表面为每个象限，然后与离体获得的弹性性能进行比较。杨氏模量和体内应变之间观察到良好的一致性。我们的研究结果表明，外侧和后象限是最硬的。相反，内侧和前象限的主动脉硬度最低。体内刚度趋势与离体获得的值一致。我们可以得出结论，我们的自动分割方法是稳健的，并与临床实践兼容（由于图形用户界面），而体内弹性特性是可靠的，并与离体弹性特性兼容。
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.
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影响因子：
3.3
作者：
Giavarina D
通讯作者：
Giavarina D
影响因子：
5.9
作者：
Pfeiffer, Michael P.;Biederman, Robert W. W.
通讯作者：
Biederman, Robert W. W.
影响因子：
5.1
作者：
Miteran, J.;Bouchot, O.;Lalande, A.
通讯作者：
Lalande, A.
影响因子：
39.3
作者：
Erbel, Raimund;Aboyans, Victor;Vrints, Christiaan J. M.
通讯作者：
Vrints, Christiaan J. M.
影响因子：
3.4
作者：
BELZ, GG
通讯作者：
BELZ, GG