Advanced risk prediction for aortic dissection patients using imaging-based computational flow analysis
Yu Zhu, Xiao Yun Xu, Ulrich Rosendahl, J Pepper, Saeed Mirsadraee
Abstract
Patients with either a repaired or medically managed aortic dissection have varying degrees of risk of developing late complications. High-risk patients would benefit from earlier intervention to improve their long-term survival. Currently serial imaging is used for risk stratification, which is not always reliable. On the other hand, understanding aortic haemodynamics within a dissection is essential to fully evaluate the disease and predict how it may progress. In recent decades, computational fluid dynamics (CFD) has been extensively applied to simulate complex haemodynamics within aortic diseases, and more recently, four-dimensional (4D)-flow magnetic resonance imaging (MRI) techniques have been developed for in vivo haemodynamic measurement. This paper presents a comprehensive review on the application of image-based CFD simulations and 4D-flow MRI analysis for risk prediction in aortic dissection. The key steps involved in patient-specific CFD analyses are demonstrated. Finally, we propose a workflow incorporating computational modelling for personalised assessment to aid in risk stratification and treatment decision-making.