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Title:

Probabilistic noninvasive prediction of wall properties of abdominal aortic aneurysms using Bayesian regression.

Document type:
Journal Article
Author(s):
Biehler, Jonas; Kehl, Sebastian; Gee, Michael W; Schmies, Fadwa; Pelisek, Jaroslav; Maier, Andreas; Reeps, Christian; Eckstein, Hans-Henning; Wall, Wolfgang A
Abstract:
Multiple patient-specific parameters, such as wall thickness, wall strength, and constitutive properties, are required for the computational assessment of abdominal aortic aneurysm (AAA) rupture risk. Unfortunately, many of these quantities are not easily accessible and could only be determined by invasive procedures, rendering a computational rupture risk assessment obsolete. This study investigates two different approaches to predict these quantities using regression models in combination with...     »
Journal title abbreviation:
Biomech Model Mechanobiol
Year:
2017
Journal volume:
16
Journal issue:
1
Pages contribution:
45-61
Language:
eng
Fulltext / DOI:
doi:10.1007/s10237-016-0801-6
Pubmed ID:
http://view.ncbi.nlm.nih.gov/pubmed/27260299
Print-ISSN:
1617-7959
TUM Institution:
Fachgebiet Gefäßchirurgie (Prof. Eckstein)
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