Sundström Elias, Laudato Marco
Department of Engineering Mechanics, FLOW Research Center, KTH Royal Institute of Technology, Teknikringen 8, 10044 Stockholm, Sweden.
Department of Engineering Mechanics, The Marcus Wallenberg Laboratory for Sound and Vibration Research, KTH Royal Institute of Technology, Teknikringen 8, 10044 Stockholm, Sweden.
Bioengineering (Basel). 2023 Oct 18;10(10):1216. doi: 10.3390/bioengineering10101216.
Subjects with bicuspid aortic valves (BAV) are at risk of developing valve dysfunction and need regular clinical imaging surveillance. Management of BAV involves manual and time-consuming segmentation of the aorta for assessing left ventricular function, jet velocity, gradient, shear stress, and valve area with aortic valve stenosis. This paper aims to employ machine learning-based (ML) segmentation as a potential for improved BAV assessment and reducing manual bias. The focus is on quantifying the relationship between valve morphology and vortical structures, and analyzing how valve morphology influences the aorta's susceptibility to shear stress that may lead to valve incompetence. The ML-based segmentation that is employed is trained on whole-body Computed Tomography (CT). Magnetic Resonance Imaging (MRI) is acquired from six subjects, three with tricuspid aortic valves (TAV) and three functionally BAV, with right-left leaflet fusion. These are used for segmentation of the cardiovascular system and delineation of four-dimensional phase-contrast magnetic resonance imaging (4D-PCMRI) for quantification of vortical structures and wall shear stress. The ML-based segmentation model exhibits a high Dice score (0.86) for the heart organ, indicating a robust segmentation. However, the Dice score for the thoracic aorta is comparatively poor (0.72). It is found that wall shear stress is predominantly symmetric in TAVs. BAVs exhibit highly asymmetric wall shear stress, with the region opposite the fused coronary leaflets experiencing elevated tangential wall shear stress. This is due to the higher tangential velocity explained by helical flow, proximally of the sinutubal junction of the ascending aorta. ML-based segmentation not only reduces the runtime of assessing the hemodynamic effectiveness, but also identifies the significance of the tangential wall shear stress in addition to the axial wall shear stress that may lead to the progression of valve incompetence in BAVs, which could guide potential adjustments in surgical interventions.
患有二叶式主动脉瓣（BAV）的患者有发生瓣膜功能障碍的风险，需要定期进行临床影像监测。BAV的管理涉及对主动脉进行手动且耗时的分割，以评估左心室功能、射流速度、压力阶差、剪切应力以及主动脉瓣狭窄时的瓣膜面积。本文旨在采用基于机器学习（ML）的分割方法，以改善BAV评估并减少人为偏差。重点是量化瓣膜形态与涡流结构之间的关系，并分析瓣膜形态如何影响主动脉对剪切应力的易感性，而这种易感性可能导致瓣膜功能不全。所采用的基于ML的分割方法是在全身计算机断层扫描（CT）上进行训练的。从六名受试者获取了磁共振成像（MRI），其中三名患有三尖瓣主动脉瓣（TAV），三名功能上为BAV且存在右-左瓣叶融合。这些用于心血管系统的分割以及四维相位对比磁共振成像（4D-PCMRI）的描绘，以量化涡流结构和壁面剪切应力。基于ML的分割模型对心脏器官的Dice分数较高（0.86），表明分割效果稳健。然而，胸主动脉的Dice分数相对较差（0.72）。研究发现，TAV中的壁面剪切应力主要是对称的。BAV表现出高度不对称的壁面剪切应力，在融合的冠状瓣叶相对区域，切向壁面剪切应力升高。这是由于升主动脉窦管交界处近端的螺旋流导致切向速度较高。基于ML的分割不仅减少了评估血流动力学有效性的运行时间，还识别出切向壁面剪切应力除了轴向壁面剪切应力之外的重要性，而轴向壁面剪切应力可能导致BAV中瓣膜功能不全的进展，这可为手术干预中的潜在调整提供指导。
