Quantification of aortic pulse wave velocity from a population based cohort: a fully automatic method
Abstract Background Aortic pulse wave velocity (PWV) is an indicator of aortic stiffness and is used as a predictor of adverse cardiovascular events. PWV can be non-invasively assessed using magnetic resonance imaging (MRI). PWV computation requires two components, the length of the aortic arch and...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2019-05-01
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| Series: | Journal of Cardiovascular Magnetic Resonance |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12968-019-0530-y |
| _version_ | 1797204798602215424 |
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| author | Rahil Shahzad Arun Shankar Raquel Amier Robin Nijveldt Jos J. M. Westenberg Albert de Roos Boudewijn P. F. Lelieveldt Rob J. van der Geest on behalf of the Heart Brain Connection study group |
| author_facet | Rahil Shahzad Arun Shankar Raquel Amier Robin Nijveldt Jos J. M. Westenberg Albert de Roos Boudewijn P. F. Lelieveldt Rob J. van der Geest on behalf of the Heart Brain Connection study group |
| author_sort | Rahil Shahzad |
| collection | DOAJ |
| description | Abstract Background Aortic pulse wave velocity (PWV) is an indicator of aortic stiffness and is used as a predictor of adverse cardiovascular events. PWV can be non-invasively assessed using magnetic resonance imaging (MRI). PWV computation requires two components, the length of the aortic arch and the time taken for the systolic pressure wave to travel through the aortic arch. The aortic length is calculated using a multi-slice 3D scan and the transit time is computed using a 2D velocity encoded MRI (VE) scan. In this study we present and evaluate an automatic method to quantify the aortic pulse wave velocity using a large population-based cohort. Methods For this study 212 subjects were retrospectively selected from a large multi-center heart-brain connection cohort. For each subject a multi-slice 3D scan of the aorta was acquired in an oblique-sagittal plane and a 2D VE scan acquired in a transverse plane cutting through the proximal ascending and descending aorta. PWV was calculated in three stages: (i) a multi-atlas-based segmentation method was developed to segment the aortic arch from the multi-slice 3D scan and subsequently estimate the length of the proximal aorta, (ii) an algorithm that delineates the proximal ascending and descending aorta from the time-resolved 2D VE scan and subsequently obtains the velocity-time flow curves was also developed, and (iii) automatic methods that can compute the transit time from the velocity-time flow curves were implemented and investigated. Finally the PWV was obtained by combining the aortic length and the transit time. Results Quantitative evaluation with respect to the length of the aortic arch as well as the computed PWV were performend by comparing the results of the novel automatic method to those obtained manually. The mean absolute difference in aortic length obtained automatically as compared to those obtained manually was 3.3 ± 2.8 mm (p < 0.05), the manual inter-observer variability on a subset of 45 scans was 3.4 ± 3.4 mm (p = 0.49). Bland-Altman analysis between the automataic method and the manual methods showed a bias of 0.0 (-5.0,5.0) m/s for the foot-to-foot approach, -0.1 (-1.2, 1.1) and -0.2 (-2.6, 2.1) m/s for the half-max and the cross-correlation methods, respectively. Conclusion We proposed and evaluated a fully automatic method to calculate the PWV on a large set of multi-center MRI scans. It was observed that the overall results obtained had very good agreement with manual analysis. Our proposed automatic method would be very beneficial for large population based studies, where manual analysis requires a lot of manpower. |
| first_indexed | 2024-04-24T08:40:57Z |
| format | Article |
| id | doaj.art-5161ba9d6626466195a6facafa2241b7 |
| institution | Directory Open Access Journal |
| issn | 1532-429X |
| language | English |
| last_indexed | 2024-04-24T08:40:57Z |
| publishDate | 2019-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Cardiovascular Magnetic Resonance |
| spelling | doaj.art-5161ba9d6626466195a6facafa2241b72024-04-16T15:35:40ZengElsevierJournal of Cardiovascular Magnetic Resonance1532-429X2019-05-0121111410.1186/s12968-019-0530-yQuantification of aortic pulse wave velocity from a population based cohort: a fully automatic methodRahil Shahzad0Arun Shankar1Raquel Amier2Robin Nijveldt3Jos J. M. Westenberg4Albert de Roos5Boudewijn P. F. Lelieveldt6Rob J. van der Geest7on behalf of the Heart Brain Connection study groupDepartment of Radiology, Leiden University Medical CenterDepartment of Radiology, Leiden University Medical CenterDepartment of Cardiology, VU University Medical CenterDepartment of Cardiology, VU University Medical CenterDepartment of Radiology, Leiden University Medical CenterDepartment of Radiology, Leiden University Medical CenterDepartment of Radiology, Leiden University Medical CenterDepartment of Radiology, Leiden University Medical CenterAbstract Background Aortic pulse wave velocity (PWV) is an indicator of aortic stiffness and is used as a predictor of adverse cardiovascular events. PWV can be non-invasively assessed using magnetic resonance imaging (MRI). PWV computation requires two components, the length of the aortic arch and the time taken for the systolic pressure wave to travel through the aortic arch. The aortic length is calculated using a multi-slice 3D scan and the transit time is computed using a 2D velocity encoded MRI (VE) scan. In this study we present and evaluate an automatic method to quantify the aortic pulse wave velocity using a large population-based cohort. Methods For this study 212 subjects were retrospectively selected from a large multi-center heart-brain connection cohort. For each subject a multi-slice 3D scan of the aorta was acquired in an oblique-sagittal plane and a 2D VE scan acquired in a transverse plane cutting through the proximal ascending and descending aorta. PWV was calculated in three stages: (i) a multi-atlas-based segmentation method was developed to segment the aortic arch from the multi-slice 3D scan and subsequently estimate the length of the proximal aorta, (ii) an algorithm that delineates the proximal ascending and descending aorta from the time-resolved 2D VE scan and subsequently obtains the velocity-time flow curves was also developed, and (iii) automatic methods that can compute the transit time from the velocity-time flow curves were implemented and investigated. Finally the PWV was obtained by combining the aortic length and the transit time. Results Quantitative evaluation with respect to the length of the aortic arch as well as the computed PWV were performend by comparing the results of the novel automatic method to those obtained manually. The mean absolute difference in aortic length obtained automatically as compared to those obtained manually was 3.3 ± 2.8 mm (p < 0.05), the manual inter-observer variability on a subset of 45 scans was 3.4 ± 3.4 mm (p = 0.49). Bland-Altman analysis between the automataic method and the manual methods showed a bias of 0.0 (-5.0,5.0) m/s for the foot-to-foot approach, -0.1 (-1.2, 1.1) and -0.2 (-2.6, 2.1) m/s for the half-max and the cross-correlation methods, respectively. Conclusion We proposed and evaluated a fully automatic method to calculate the PWV on a large set of multi-center MRI scans. It was observed that the overall results obtained had very good agreement with manual analysis. Our proposed automatic method would be very beneficial for large population based studies, where manual analysis requires a lot of manpower.http://link.springer.com/article/10.1186/s12968-019-0530-yPulse wave velocityVelocity encoded MRIImage registrationCenterline estimationMulti-atlas-based segmentation |
| spellingShingle | Rahil Shahzad Arun Shankar Raquel Amier Robin Nijveldt Jos J. M. Westenberg Albert de Roos Boudewijn P. F. Lelieveldt Rob J. van der Geest on behalf of the Heart Brain Connection study group Quantification of aortic pulse wave velocity from a population based cohort: a fully automatic method Journal of Cardiovascular Magnetic Resonance Pulse wave velocity Velocity encoded MRI Image registration Centerline estimation Multi-atlas-based segmentation |
| title | Quantification of aortic pulse wave velocity from a population based cohort: a fully automatic method |
| title_full | Quantification of aortic pulse wave velocity from a population based cohort: a fully automatic method |
| title_fullStr | Quantification of aortic pulse wave velocity from a population based cohort: a fully automatic method |
| title_full_unstemmed | Quantification of aortic pulse wave velocity from a population based cohort: a fully automatic method |
| title_short | Quantification of aortic pulse wave velocity from a population based cohort: a fully automatic method |
| title_sort | quantification of aortic pulse wave velocity from a population based cohort a fully automatic method |
| topic | Pulse wave velocity Velocity encoded MRI Image registration Centerline estimation Multi-atlas-based segmentation |
| url | http://link.springer.com/article/10.1186/s12968-019-0530-y |
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