Why the study?
Clinical surveillance of bicuspid aortic valves requires manual, time-consuming aortic segmentation, prompting evaluation of machine learning-based segmentation to improve assessment and reduce manual bias.
Can machine learning-based segmentation of the thoracic aorta using MRI accurately assess hemodynamics and wall shear stress in patients with bicuspid aortic valves?
Can machine learning-based segmentation of the thoracic aorta using MRI accurately assess hemodynamics and wall shear stress in patients with bicuspid aortic valves?
Machine learning-based segmentation of 4D-PCMRI can efficiently quantify asymmetric wall shear stress in bicuspid aortic valves, potentially guiding surgical interventions.
Answers are grounded in this paper and cite their sources.
ML segmentation may speed BAV assessment and cut bias; leaves open prospective validation before clinical adoption.
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.
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Sundström et al. (2023) studied this question.
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