Abstract
PURPOSE: Previous studies have demonstrated the efficacy of contrast dilution gradient (CDG) analysis in determining large vessel velocity distributions from 1000 fps high-speed angiography (HSA). However, the method required vessel centerline extraction, which made it applicable only to non-tortuous geometries using a highly specific contrast injection technique. This study seeks to remove the need for a priori knowledge regarding the direction of flow and modify the vessel sampling method to make the algorithm more robust to non-linear geometries. MATERIALS AND METHODS: 1000 fps HSA acquisitions were obtained in vitro with a benchtop flow loop using the XC-Actaeon (Varex Inc.) photon-counting detector, and in silico using a passive-scalar transport model within a computational fluid dynamics (CFD) simulation. CDG analyses were obtained using gridline sampling across the vessel, and subsequent 1D velocity measurement in both the x- and y-directions. The velocity magnitudes derived from the component CDG velocity vectors were aligned with CFD results via co-registration of the resulting velocity maps and compared using mean absolute percent error (MAPE) between pixels values in each method after temporal averaging of the 1-ms velocity distributions. RESULTS: Regions well-saturated with contrast throughout the acquisition showed agreement when compared to CFD (MAPE of 18% for the carotid bifurcation inlet and MAPE of 27% for the internal carotid aneurysm), with respective completion times of 137 seconds and 5.8 seconds. CONCLUSIONS: CDG may be used to obtain velocity distributions in and surrounding vascular pathologies provided the contrast injection is sufficient to provide a gradient, and diffusion of contrast through the system is negligible.