TY - GEN
T1 - Towards regional elastography of intracranial aneurysms
AU - Balocco, Simone
AU - Camara, Oscar
AU - Frangi, Alejandro F.
PY - 2008
Y1 - 2008
N2 - Weak spots in the aneurysm could be identified estimating the regional stiffness of the wall. Our approach consists in defining a parametric biomechanical model of the vessel which, given the patient's vascular morphology and the blood in- and outflow obtained from non-invasive imaging as well as parameters describing the local elasticity of the wall, enables the computation of the theoretical deformed wall position. The distance between this latter and the one obtained from the aneurysm pulsation is iteratively minimized in order to estimate the optimal set of stiffness parameters. In order to reduce the number of variables to estimate, the aneurysm morphology is clustered into a limited number of regions with uniform stiffness. A random noise perturbation (<5mm) is applied to the reference deformations and strains, showing that the robustness of the clustering decreases to 75% and errors of the stiffness estimates remain below 10% of the reference values.
AB - Weak spots in the aneurysm could be identified estimating the regional stiffness of the wall. Our approach consists in defining a parametric biomechanical model of the vessel which, given the patient's vascular morphology and the blood in- and outflow obtained from non-invasive imaging as well as parameters describing the local elasticity of the wall, enables the computation of the theoretical deformed wall position. The distance between this latter and the one obtained from the aneurysm pulsation is iteratively minimized in order to estimate the optimal set of stiffness parameters. In order to reduce the number of variables to estimate, the aneurysm morphology is clustered into a limited number of regions with uniform stiffness. A random noise perturbation (<5mm) is applied to the reference deformations and strains, showing that the robustness of the clustering decreases to 75% and errors of the stiffness estimates remain below 10% of the reference values.
UR - http://www.scopus.com/inward/record.url?scp=79551685464&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-85990-1_16
DO - 10.1007/978-3-540-85990-1_16
M3 - Conference contribution
C2 - 18982598
AN - SCOPUS:79551685464
SN - 3540859896
SN - 9783540859895
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 131
EP - 138
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008 - 11th International Conference, Proceedings
PB - Springer-Verlag Italia
T2 - 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2008
Y2 - 6 September 2008 through 10 September 2008
ER -