Blood Flow Quantication using 1D CFD Parameter Identication

Patient-specic measurements of cerebral blood ow provide valuable diagnostic information concerning cerebrovascular diseases rather than visually driven qualitative evaluation. In this paper, we present a quantitative method to estimate blood ow parameters with high temporal resolution from digital subtraction angiography (DSA) image sequences. Using a 3D DSA dataset and a 2D+t DSA sequence, the proposed algorithm employs a 1D Computational Fluid Dynamics (CFD) model for estimation of time-dependent ow values along a cerebral vessel, combined with an additional Advection Diusion Equation (ADE) for contrast agent propagation. The CFD system, followed by the ADE, is solved with a nite volume approximation, which ensures the conservation of mass. Instead of dening a new imaging protocol to obtain relevant data, our cost function optimizes the bolus arrival time (BAT) of the contrast agent in 2D+t DSA sequences. The visual determination of BAT is common clinical practice and can be easily derived from and be compared to values, generated by a 1D-CFD simulation. Using this strategy, we ensure that our proposed method ts best to clinical practice and does not