# name
Characterization of intracranial aneurysms in middle cerebral artery (MCA)

# metadata.dc.description.abstract[].value
Intracranial aneurysms have been the aim of many researchers, but despite finding multiple correlations between aneurysm rupture and risk factors, their study still remains limited. Since cerebral aneurysms morphology has been considered as a potential surrogate of rupture, in this project the influence of morphological variables in the aneurysm's rupture have been studied.
Among the proposed morphological characteristics, several indices have allowed to establish links between these descriptors and the risk of rupture for a specific lesion. These might lead to a better understanding of the cerebral aneurysm rupture mechanisms.
This study presents a complete and efficient image-based methodology for the analysis of cerebral aneurysms on a patient-specific basis including the sac and the parent vessel. This methodology allows the modeling of a large number of aneurysms in a time-efficient manner to obtain a patient specific model of the aneurysm geometry. Our pipeline uses algorithms that starting from Three-Dimensional Rotational Angiography (3DRA) images create a mesh model that is morphologically processed producing a number of basic shape measurements as well as most sophisticated ones such as the Zernike Moments. These computations have been performed on a population of 95 ruptured and unruptured aneurysms (79 patients) located at the Middle Cerebral Artery (MCA).
Among the set of computed indices, statistical studies show that the more tridimensional information the morphological descriptors contain, the more robust they are discriminating between ruptured and unruptured aneurysms. This is the case for volume-based Zernike moments, which provided the best prediction results compared to other descriptors such as the Non-Sphericity
Index.

# metadata.dc.title[].value
Characterization of intracranial aneurysms in middle cerebral artery (MCA)