Machine Learning-Based Segmentation of the Thoracic Aorta with Congenital Valve Disease Using MRI
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 val...
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| Format: | Article |
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MDPI AG
2023-10-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/10/10/1216 |
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| author | Elias Sundström Marco Laudato |
| author_facet | Elias Sundström Marco Laudato |
| author_sort | Elias Sundström |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T21:25:05Z |
| format | Article |
| id | doaj.art-0413b996dd7d4516a834b0c745d69c26 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-10T21:25:05Z |
| publishDate | 2023-10-01 |
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| series | Bioengineering |
| spelling | doaj.art-0413b996dd7d4516a834b0c745d69c262023-11-19T15:42:39ZengMDPI AGBioengineering2306-53542023-10-011010121610.3390/bioengineering10101216Machine Learning-Based Segmentation of the Thoracic Aorta with Congenital Valve Disease Using MRIElias Sundström0Marco Laudato1Department of Engineering Mechanics, FLOW Research Center, KTH Royal Institute of Technology, Teknikringen 8, 10044 Stockholm, SwedenDepartment of Engineering Mechanics, FLOW Research Center, KTH Royal Institute of Technology, Teknikringen 8, 10044 Stockholm, SwedenSubjects 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.https://www.mdpi.com/2306-5354/10/10/1216machine learning segmentation4D-PCMRIaortic valve disease |
| spellingShingle | Elias Sundström Marco Laudato Machine Learning-Based Segmentation of the Thoracic Aorta with Congenital Valve Disease Using MRI Bioengineering machine learning segmentation 4D-PCMRI aortic valve disease |
| title | Machine Learning-Based Segmentation of the Thoracic Aorta with Congenital Valve Disease Using MRI |
| title_full | Machine Learning-Based Segmentation of the Thoracic Aorta with Congenital Valve Disease Using MRI |
| title_fullStr | Machine Learning-Based Segmentation of the Thoracic Aorta with Congenital Valve Disease Using MRI |
| title_full_unstemmed | Machine Learning-Based Segmentation of the Thoracic Aorta with Congenital Valve Disease Using MRI |
| title_short | Machine Learning-Based Segmentation of the Thoracic Aorta with Congenital Valve Disease Using MRI |
| title_sort | machine learning based segmentation of the thoracic aorta with congenital valve disease using mri |
| topic | machine learning segmentation 4D-PCMRI aortic valve disease |
| url | https://www.mdpi.com/2306-5354/10/10/1216 |
| work_keys_str_mv | AT eliassundstrom machinelearningbasedsegmentationofthethoracicaortawithcongenitalvalvediseaseusingmri AT marcolaudato machinelearningbasedsegmentationofthethoracicaortawithcongenitalvalvediseaseusingmri |