Increasing interest is drawn on hemodynamic parameters for classifying the risk of rupture as well as treatment planning of cerebral aneurysms. A proposed method to obtain quantities such as wall shear stress, pressure, and blood flow velocity is to numerically simulate the blood flow using computational fluid dynamics (CFD) methods. For the validation of those calculated quantities, virtually generated angiograms, based on the CFD results, are increasingly used for a subsequent comparison with real, acquired angiograms. For the generation of virtual angiograms, several patient-specific parameters have to be incorporated to obtain virtual angiograms which match the acquired angiograms as best as possible. For this purpose, a workflow is presented and demonstrated involving multiple phantom and patient cases.

4D digital subtraction angiography (4D DSA) imaging based on C-arm data is compromised by ambiguities due to the lack of sufficient projection data. Furthermore, missing ground truth data complicates the evaluation of reconstructed 4D DSA data sets. In this paper, we present an approach to generate 4D ground truth data and propose a method for iteratively improving 4D DSA reconstructions. The method is evaluated for both ground truth as well as real patient data. For ground truth data, the iterative reconstruction was able to improve the reconstructed data in a way that time-contrast curves (TCCs) are closer to ground truth by 35% on average. Real data showed a more reasonable physiological flow behavior after iterative reconstruction.

Visualization of 4-dimensional DSA data set.