Computational Fluid Dynamics (CFD) For Predicting Pathological Changes In The Aorta: Is It Ready For Clinical Use?

Patient-specific modelling

Patient-specific models have been successfully deployed by many researchers. Nevertheless, the translation of high-resolution radiological data into geometrical vascular models for CFD remains a time-intensive task which is often cumbersome and requires trained specialists. There is a lack of automated segmentation and meshing tools for biomedical tasks. Recent developments based on deep neural networks may provide acceptable solutions for clinical use.

The setting of boundary conditions (e.g., velocity profiles at inflow/outflow) requires great care, because boundary conditions have a strong effect on the quality of the results. Therefore, it is useful to include flow measurements at well-defined locations (e.g., 2D+time or 3D+time blood flow MRI) into radiologic protocols. Next to the adequate choice of the modality and body location, this requires appropriate temporal and spatial resolution of the scans.

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