TY - GEN
T1 - Direct estimation of wall shear stress from aneurysmal morphology
T2 - A statistical approach
AU - Sarrami-Foroushani, Ali
AU - Lassila, Toni
AU - Pozo, Jose M.
AU - Gooya, Ali
AU - Frangi, Alejandro F.
N1 - Funding Information:
This project was partly supported by the Marie Curie Individual Fellowship (625745, A. Gooya). The aneurysm dataset has been provided by the European integrated project @neurIST (IST-027703).
Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - Computational fluid dynamics (CFD) is a valuable tool for studying vascular diseases,but requires long computational time. To alleviate this issue,we propose a statistical framework to predict the aneurysmal wall shear stress patterns directly from the aneurysm shape. A database of 38 complex intracranial aneurysm shapes is used to generate aneurysm morphologies and CFD simulations. The shapes and wall shear stresses are then converted to clouds of hybrid points containing both types of information. These are subsequently used to train a joint statistical model implementing a mixture of principal component analyzers. Given a new aneurysmal shape,the trained joint model is firstly collapsed to a shape only model and used to initialize the missing shear stress values. The estimated hybrid point set is further refined by projection to the joint model space. We demonstrate that our predicted patterns can achieve significant similarities to the CFD-based results.
AB - Computational fluid dynamics (CFD) is a valuable tool for studying vascular diseases,but requires long computational time. To alleviate this issue,we propose a statistical framework to predict the aneurysmal wall shear stress patterns directly from the aneurysm shape. A database of 38 complex intracranial aneurysm shapes is used to generate aneurysm morphologies and CFD simulations. The shapes and wall shear stresses are then converted to clouds of hybrid points containing both types of information. These are subsequently used to train a joint statistical model implementing a mixture of principal component analyzers. Given a new aneurysmal shape,the trained joint model is firstly collapsed to a shape only model and used to initialize the missing shear stress values. The estimated hybrid point set is further refined by projection to the joint model space. We demonstrate that our predicted patterns can achieve significant similarities to the CFD-based results.
UR - http://www.scopus.com/inward/record.url?scp=84996490705&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46726-9_24
DO - 10.1007/978-3-319-46726-9_24
M3 - Conference contribution
AN - SCOPUS:84996490705
SN - 9783319467252
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 201
EP - 209
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
A2 - Joskowicz, Leo
A2 - Sabuncu, Mert R.
A2 - Wells, William
A2 - Unal, Gozde
A2 - Ourselin, Sebastian
PB - Springer-Verlag Italia
ER -