Investigation of the quantitative intracranial aneurysm wall enhancement and geometric features associated with aneurysm volume growth

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Abstract: There are almost 500,000 deaths worldwide each year caused by rupture of intracranial aneurysms (IAs) with half the victims younger than age 50. Unruptured intracranial aneurysms (UIAs) can be treated by endovascular and microsurgical interventions to prevent rupture, however, the treatment carries a non- negligible risk of morbidity (5%–7%) and mortality (1%–2%). Current guidelines recommend intervention for UIAs larger than 7mm, when their rupture risk is higher than the intervention risk. However, more than 50% of ruptured IAs are smaller than 7mm. Identifying small aneurysms that are prone to rupture and performing selective intervention can potentially prevent rupture of these small aneurysms. A recent meta-analysis including more than 4000 UIAs with an average of 4 years’ follow-up showed aneurysms that grew during the follow-up were 30 times more likely to rupture than the non-growing aneurysms (3.1% vs. 0.1%). Identifying the factors that predict aneurysm growth can help select these high risk UIAs for treatment. Aneurysm wall enhancement (a surrogate marker of inflammation, identified by contrast-enhanced vessel wall MRI) and aneurysm geometric factors (such as shape or size ratio as identified on imaging) are two promising markers that may predict aneurysm growth. However, the current evaluation of these factors is limited by non-optimized imaging techniques that have flow artifacts, long scan time and subjective, qualitative image analysis. This project will develop optimally accelerated and blood suppressed imaging methods and quantitative image analysis methods for the evaluation of UIA wall enhancement and geometric characteristics, and investigate the parameters associated with aneurysm volume growth by longitudinal UIA evaluation using MRI. First, we will develop and optimize blood suppression and imaging acceleration techniques using in vitro phantoms and in vivo testing in patients. Second, we will develop automatic segmentation and quantification methods (Radiomics) for evaluating UIA wall enhancement and geometry. Finally, we will follow 200 patients with >3mm UIAs using MRI each year for up to 4 years, and investigate which clinical and quantitative imaging parameters are predictive of UIA volume growth.