Exploring the use of proper orthogonal decomposition for enhancing blood flow images via computational fluid dynamics.

Obtaining high quality patient-specific flow velocity information is not an easy task. Available clinical data are usually poorly resolved and contain a significant amount of noise. We propose a novel approach to integrate computational fluid dynamics with measurement data to overcome this difficulty. By performing a proper orthogonal decomposition of simulated blood flow patterns for a given vascular location with various anatomical configurations it is possible to obtain a basis model for flow reconstruction. This is used to interpolate imaging data intelligently without having to perform a full flow simulation for each individual patient. This work focuses on assessing the feasibility of such a method.