Abstract

We have developed a waveform shape model-based algorithm for the extraction of blood flow from dynamic arterial x-ray angiographic images. We have carried out in-vitro validation of this technique. A pulsatile physiological blood flow circuit was constructed using an anthropomorphic cerebral vascular phantom to simulate the cerebral arterial circulation with whole blood as the fluid. Instantaneous recording of flow from an electromagnetic flow meter (EMF) provided the gold standard measurement. Biplane dynamic digital x-ray images of the vascular phantom with injection of contrast medium were acquired at 25 fps using a PC frame capture card with calibration using a Perspex cube. Principal component analysis was used to construct a shape model by collecting 434 flow waveforms from the EMF under varying flow conditions. Blood flow waveforms were calculated from the angiographic data by using our previous concentration-distance curve matching (ORG) algorithm and by using the new model-based (MB) algorithm. Both instantaneous and mean flow values calculated using the MB algorithm showed greater correlation, less bias, and lower variability than those calculated using the ORG algorithm when compared to the EMF values. We have successfully demonstrated that use of a priori waveform shape information can improve flow measurements from dynamic x-ray angiograms.