Aneurysm rupture risk estimation​

Unruptured intracranial aneurysm (UIA) is a severe, relatively common cerebrovascular disorder in the general population. Although such aneurysms are mostly asymptomatic and may not burst, a considerable number of UIA patients remain on a high risk of aneurysm rupture, a serious life-threatening condition. Thus, is crucial to decide the timing and type (clipping, coils, and/or stent, flow diverter) of surgical intervention. However, the optimal management strategy of UIA is still open to clinical debate, with recommendations for elective repair after diagnosis based primarily on the aneurysm size and location. While cardiovascular flow imaging has strong potential to provide the required information for surgical decision-making, currently, flow imaging and assessment is not sufficient to reliably predict rupture and quantitatively assess the risk of haemorrhage. By identifying factors contributing to UIA rupture and by incorporating advanced computational techniques, i.e. 4D flow magnetic resonance imaging (MRI), machine learning (ML) algorithms, and computational fluid dynamics (CFD) models, this project will technologically advance quantification of rupture risk assessment on an UIA patient-specific basis. Dr Aristokleous is leading a Marie Curie project, Sim4DFlow, whose core technological aim is to develop a novel in silico-based imaging framework that can integrate the advanced computational techniques of 4D flow MRI, ML, and CFD. Subsequently, we will test and validate the framework prognostic capacity in the laboratory setting using data of UIA patients.

Link to the Sim4DFlow project web-page: 

IN CLOCKWISE ORDER: Personalized intracranial aneurysm rupture prognosis through a in silico-based 4D Flow MRI & Machine Learning (ML) approach. Sim4DFlow will combine medical imaging, computational fluid dynamics and ML to assess the risk for aneurysm rupture and to improve the way cardiovascular disease is diagnosed and treated.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No.101038084.

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