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Personalized intracranial aneurysm rupture prognosis using Simulation-Based 4D Flow MRI and Machine Learning

Project description

Simulation-based imaging framework for assessing risk of intracranial aneurysm rupture

An unruptured intracranial aneurysm (UIA) is a relatively common cerebrovascular disorder with a considerable number of patients at high risk of aneurysm rupture, a life-threatening condition. The EU-funded Sim4DFlow project’s core objective is to develop a novel simulation-based imaging framework that integrates the advanced computational techniques of 4D flow magnetic resonance imaging (MRI), machine learning (ML) and computational fluid dynamics (CFD). Identifying the factors contributing to UIA rupture and applying 4D flow MRI, ML algorithms and CFD models will enable advanced quantification of personal rupture risk assessment in UIA patients.

Objective

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.
This project, Sim4DFlow, core technological aim is to develop a novel simulation-based imaging framework that can integrate the advanced computational techniques of 4D flow MRI, ML, and CFD. Subsequently, Sim4DFlow will test and validate the framework prognostic capacity in the laboratory setting against relevant data of UIA patients.

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MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)

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Call for proposal

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(opens in new window) H2020-WF-2018-2020

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Coordinator

UNIVERSITY OF CYPRUS
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 157 941,12
Address
AVENUE PANEPISTIMIOU 2109 AGLANTZI
1678 Nicosia
Cyprus

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Region
Κύπρος Κύπρος Κύπρος
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 157 941,12
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