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 coregistration 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.