Angiographic velocimetry analysis using contrast dilution gradient method with a 1000 frames per second photon-counting detector.

Purpose: Contrast dilution gradient (CDG) analysis is a quantitative method allowing blood velocity estimation using angiographic acquisitions. Currently, CDG is restricted to peripheral vasculature due to the suboptimal temporal resolution of current imaging systems. We investigate extension of CDG methods to the flow conditions of proximal vasculature using 1000 frames per second (fps) high-speed angiographic (HSA) imaging. Approach: We performed in-vitro HSA acquisitions using the XC-Actaeon detector and 3D-printed patient-specific phantoms. The CDG approach was used for blood velocity estimation expressed as the ratio of temporal and spatial contrast gradients. The gradients were extracted from 2D contrast intensity maps synthesized by plotting intensity profiles along the arterial centerline at each frame. In-vitro results obtained at various frame rates via temporal binning of 1000 fps data were retrospectively compared to computational fluid dynamics (CFD) velocimetry. Full-vessel velocity distributions were estimated at 1000 fps via parallel line expansion of the arterial centerline analysis. Results: Using HSA, the CDG method displayed agreement with CFD at or above 250 fps [mean-absolute error (MAE): 2.6±6.3 cm/s, p=0.05]. Relative velocity distributions correlated well with CFD at 1000 fps with universal underapproximation due to effects of pulsatile contrast injection (MAE: 4.3 cm/s). Conclusions: Using 1000 fps HSA, CDG-based extraction of velocities across large arteries is possible. The method is sensitive to noise; however, image processing techniques and a contrast injection, which adequately fills the vessel assist algorithm accuracy. The CDG method provides high resolution quantitative information for rapidly transient flow patterns observed in arterial circulation.

Semi-automatic Co-Registration of 3D CFD Vascular Geometry to 1000 FPS High-Speed Angiographic (HSA) Projection Images for Flow Determination Comparisons.

Image co-registration is an important tool that is commonly used to quantitatively or qualitatively compare information from images or data sets that vary in time, origin, etc. This research proposes a method for the semi-automatic co-registration of the 3D vascular geometry of an intracranial aneurysm to novel high-speed angiographic (HSA) 1000 fps projection images. Using the software Tecplot 360, 3D velocimetry data generated from computational fluid dynamics (CFD) for patient-specific vasculature models can be extracted and uploaded into Python. Dilation, translation, and angular rotation of the 3D velocimetry data can then be performed in order to co-register its geometry to corresponding 2D HSA projection images of the 3D printed vascular model. Once the 3D CFD velocimetry data is geometrically aligned, a 2D velocimetry plot can be generated and the Sørensen-Dice coefficient can be calculated in order to determine the success of the co-registration process. The co-registration process was performed ten times for two different vascular models and had an average Sørensen-Dice coefficient of 0.84 ± 0.02. The method presented in this research allows for a direct comparison between 3D CFD velocimetry data and in-vitro 2D velocimetry methods. From the 3D CFD, we can compare various flow characteristics in addition to velocimetry data with HSA-derived flow metrics. The method is robust to other vascular geometries as well.