Cerebrovascular diseases are a leading cause of death and long term disability in the United States. Detailed knowledge of the human brain's vascular architecture is important in relating hemodynamics to physiopathology, and can help optimize the diagnosis and treatment of cerebrovascular disease. This exploratory collaboration with the UCLA Center for Computational Biology (CCB) will develop a framework for the quantitative characterization of brain vasculature and its variability among normal subjects from non-invasive magnetic resonance angiography (MRA). The project is organized around three specific aims, broadly corresponding to angiographic reconstruction, analysis, and probabilistic atlasing:
Aim1 - Digital reconstruction of a normative set of MRA: a) Build 3D vascular reconstructions from MRA data of normal subjects. b) Evaluate and verify the vascular reconstructions using a battery of reliability tests.
Aim2 - Statistical morphometric analysis: a) Characterize the geometry and branching topology of the arterial structures of the brain. b) Conduct statistical analysis to establish inter-subject variability of the vascular structural characteristics and test for differences between males and females, and left/right hemispheres.
Aim3 - Probabilistic angiographic atlas: a) Construct a brain vascular atlas using the CCB registration pipeline and integrate with the other modalities of the CCB brain atlas. At the completion of the project, the anonymized raw image stacks, the vascular reconstructions and the morphometric characterizations will be made available to the scientific community through the CCB pipeline. The datasets and information generated and disseminated will be of broad and extreme value.
This project aims at constructing brain vascular reconstructions from magnetic resonance angiography data and constructing a vascular atlas of the brain. Both the algorithms and data generated and disseminated in this project will be of broad and high value for a better understanding of the mechanisms involved in the initiation, progression and outcome of cerebrovascular diseases such as stroke and aneurysms. This knowledge is important for improving current evaluation and treatment of patients with cerebrovascular disease.
| Doddasomayajula, R; Chung, B J; Mut, F et al. (2017) Hemodynamic Characteristics of Ruptured and Unruptured Multiple Aneurysms at Mirror and Ipsilateral Locations. AJNR Am J Neuroradiol 38:2301-2307 |
| Mut, Fernando; Wright, Susan; Ascoli, Giorgio A et al. (2014) Morphometric, geographic, and territorial characterization of brain arterial trees. Int J Numer Method Biomed Eng 30:755-66 |
| Wright, Susan N; Kochunov, Peter; Mut, Fernando et al. (2013) Digital reconstruction and morphometric analysis of human brain arterial vasculature from magnetic resonance angiography. Neuroimage 82:170-81 |
