3D geometric reconstruction of thoracic aortic aneurysms.
BACKGROUND
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.


METHODS
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.


RESULTS
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.


CONCLUSIONS
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.
Borghi
Alessandro
A
Department of Chemical Engineering, South Kensington Campus, Imperial College London, UK. alessandro.borghi@imperial.ac.uk
Wood
Nigel B
NB
Mohiaddin
Raad H
RH
Xu
X Yun
XY
D017545:Aortic Aneurysm, Thoracic / Q000175:diagnosis / Q000473:pathology*; D001019:Aortic Rupture / Q000473:pathology; D001698:Biomedical Engineering / Q000379:methods; D003198:Computer Simulation; D020342:Finite Element Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008953:Models, Anatomic; D008954:Models, Biological; D008955:Models, Cardiovascular; D015233:Models, Statistical; D012984:Software; D013314:Stress, Mechanical