Semi-automatic co-registration of 3D CFD vascular geometry to 1000 fps high-speed angiographic (HSA) projection images for flow determination comparisons

Mitchell Chudzik; Kyle Williams; Allison Shields; SV Setlur Nagesh; Eric Paccione; Daniel R. Bednarek; Stephen Rudin; Ciprian N. Ionita

doi:10.1117/12.2612361

4 April 2022 Semi-automatic co-registration of 3D CFD vascular geometry to 1000 fps high-speed angiographic (HSA) projection images for flow determination comparisons

Mitchell Chudzik, Kyle Williams, Allison Shields, SV Setlur Nagesh, Eric Paccione, Daniel R. Bednarek, Stephen Rudin, Ciprian N. Ionita

Author Affiliations +

Proceedings Volume 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging; 120361U (2022) https://doi.org/10.1117/12.2612361
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

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 HSAderived flow metrics. The method is robust to other vascular geometries as well.

Conference Presentation

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Mitchell Chudzik, Kyle Williams, Allison Shields, SV Setlur Nagesh, Eric Paccione, Daniel R. Bednarek, Stephen Rudin, and Ciprian N. Ionita "Semi-automatic co-registration of 3D CFD vascular geometry to 1000 fps high-speed angiographic (HSA) projection images for flow determination comparisons", Proc. SPIE 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging, 120361U (4 April 2022); https://doi.org/10.1117/12.2612361

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