Conference Proceeding

Development of a Method to Construct Three-Dimensional Finite Element Models of Thoracic Aortic Aneurysms from MRI Images.

Graduate Sch. of Eng., Tohoku Univ., Sendai
01/2001; DOI:10.1109/MIAR.2001.930275 In proceeding of: Medical Imaging and Augmented Reality: First International Workshop, MIAR 2001, Hong Kong, China, June 10-12, 2001. Proceedings
Source: DBLP

ABSTRACT Most patients die when thoracic aortic aneurysms rupture. In order
to avoid the ruptures, the aneurysms are replaced with aortic prostheses
when their maximum diameter exceeds 5 cm. Because this criterion is
based on the experiences, some aneurysms rupture even if the diameters
are smaller than this criterion. To treat the aneurysm properly, it is
necessary to find out the new criterion. The rupture is thought to have
a close relationship with the stress in the wall. Hence, there is much
research about the stress, but this research uses the straight tube
model. The model shape does not seem appropriate to a thoracic aorta
shape. Hence, we developed the method to construct three-dimensional
finite element models of thoracic aortic aneurysms from MRI images

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    ABSTRACT: The thoracic aortic aneurysm (TAA) is a pathology that involves an expansion of the aortic diameter in the thoracic aorta, leading to risk of rupture. Recent studies have suggested that internal wall stress, which is affected by TAA geometry and the presence or absence of thrombus, is a more reliable predictor of rupture than the maximum diameter, the current clinical criterion. Accurate reconstruction of TAA geometry is a crucial step in patient-specific stress calculations. In this work, a novel methodology was developed, which combines data from several sets of magnetic resonance (MR) images with different levels of detail and different resolutions. Two sets of images were employed to create the final model, which has the highest level of detail for each component of the aneurysm (lumen, thrombus, and wall). A reference model was built by using a single set of images for comparison. This approach was applied to two patient-specific TAAs in the descending thoracic aorta. The results of finite element simulations showed differences in stress pattern between the coarse and fine models: higher stress values were found with the coarse model and the differences in predicted maximum wall stress were 30% for patient A and 11% for patient B. This paper presents a new approach to the reconstruction of an aneurysm model based on the use of several sets of MR images. This enables more accurate representation of not only the lumen but also the wall surface of a TAA taking account of intraluminal thrombus.
    BioMedical Engineering OnLine 02/2006; 5:59. · 1.61 Impact Factor
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    ABSTRACT: An aneurysm is a gradual and progressive ballooning of a blood vessel due to wall degeneration. Rupture of abdominal aortic aneurysm (AAA) constitutes a significant portion of deaths in the US. In this study, we describe a technique to reconstruct AAA geometry from CT images in an inexpensive and streamlined fashion. A 3D reconstruction technique was implemented with a GUI interface in MATLAB using the active contours technique. The lumen and the thrombus of the AAA were segmented individually in two separate protocols and were then joined together into a hybrid surface. This surface was then used to obtain the aortic wall. This method can deal with very poor contrast images where the aortic wall is indistinguishable from the surrounding features. Data obtained from the segmentation of image sets were smoothed in 3D using a Support Vector Machine technique. The segmentation method presented in this paper is inexpensive and has minimal user-dependency in reconstructing AAA geometry (lumen and wall) from patient image sets. The AAA model generated using this segmentation algorithm can be used to study a variety of biomechanical issues remaining in AAA biomechanics including stress estimation, endovascular stent-graft performance, and local drug delivery studies.
    Annals of biomedical engineering 11/2009; 38(1):164-76. · 2.41 Impact Factor