Kinematics of Abdominal Aortic Aneurysms
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910874540703744 |
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| author | Jamshidian, Mostafa Wittek, Adam Sekhavat, Saeideh Miller, Karol |
| author_facet | Jamshidian, Mostafa Wittek, Adam Sekhavat, Saeideh Miller, Karol |
| contents | A search in Scopus within "Article title, Abstract, Keywords" unveils 2,444 documents focused on the biomechanics of Abdominal Aortic Aneurysm (AAA), mostly on AAA wall stress. Only 24 documents investigated AAA kinematics, an important topic that could potentially offer significant insights into the biomechanics of AAA. In this paper, we present an image-based approach for patient-specific, in vivo, and non-invasive AAA kinematic analysis using patient's time-resolved 3D computed tomography angiography (4D-CTA) images, with an objective to measure wall displacement and strain during the cardiac cycle. Our approach relies on regularized deformable image registration for estimating wall displacement, estimation of the local wall strain as the ratio of its normal displacement to its local radius of curvature, and local surface fitting with non-deterministic outlier detection for estimating the wall radius of curvature. We verified our approach against synthetic ground truth image data created by warping a 3D-CTA image of AAA using a realistic displacement field obtained from a finite element biomechanical model. We applied our approach to assess AAA wall displacements and strains in ten patients. Our kinematic analysis results indicated that the 99th percentile of circumferential wall strain, among all patients, ranged from 2.62% to 5.54%, with an average of 4.45% and a standard deviation of 0.87%. We also observed that AAA wall strains are significantly lower than those of a healthy aorta. Our work demonstrates that the registration-based measurement of AAA wall displacements in the direction normal to the wall is sufficiently accurate to reliably estimate strain from these displacements. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_13377 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Kinematics of Abdominal Aortic Aneurysms Jamshidian, Mostafa Wittek, Adam Sekhavat, Saeideh Miller, Karol Computational Engineering, Finance, and Science A search in Scopus within "Article title, Abstract, Keywords" unveils 2,444 documents focused on the biomechanics of Abdominal Aortic Aneurysm (AAA), mostly on AAA wall stress. Only 24 documents investigated AAA kinematics, an important topic that could potentially offer significant insights into the biomechanics of AAA. In this paper, we present an image-based approach for patient-specific, in vivo, and non-invasive AAA kinematic analysis using patient's time-resolved 3D computed tomography angiography (4D-CTA) images, with an objective to measure wall displacement and strain during the cardiac cycle. Our approach relies on regularized deformable image registration for estimating wall displacement, estimation of the local wall strain as the ratio of its normal displacement to its local radius of curvature, and local surface fitting with non-deterministic outlier detection for estimating the wall radius of curvature. We verified our approach against synthetic ground truth image data created by warping a 3D-CTA image of AAA using a realistic displacement field obtained from a finite element biomechanical model. We applied our approach to assess AAA wall displacements and strains in ten patients. Our kinematic analysis results indicated that the 99th percentile of circumferential wall strain, among all patients, ranged from 2.62% to 5.54%, with an average of 4.45% and a standard deviation of 0.87%. We also observed that AAA wall strains are significantly lower than those of a healthy aorta. Our work demonstrates that the registration-based measurement of AAA wall displacements in the direction normal to the wall is sufficiently accurate to reliably estimate strain from these displacements. |
| title | Kinematics of Abdominal Aortic Aneurysms |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2405.13377 |