Three-dimensional Flow Quantification in Arteries from Simulated Multi-Angle Angiography*

X-ray angiography has been widely used for optical diagnosis of vascular diseases by leveraging its capability of visualization of blood flow inside of human blood vessels. Optical flow method can be applied to the cinema to obtain the detailed flow distribution within the image with a high resolution (one velocity vector per pixel), therefore, OFM is promising for hemodynamic studies. The accuracy of the velocity assessment is highly dependent on the arbitrary 3D contrast agent distribution pattern and the complexity of geometries as well. To overcome this limitation, direct 3D flow velocity assessments based on 3D blood flow angiographic images is desired. The objective of the current study is to demonstrate the feasibility of 3D flow field assessment by using OFM based on reconstructed 3D image of the blood vessel from the numerically simulated simultaneous multi-angle X-ray angiography of the blood flow. In the present study, a computational fluid dynamics (CFD) study is first carried out to simulate the blood flow with contrast agent injections on a patient specific internal carotid artery model by using an experimentally validated CFD model. Then the multi-perspective angiographic images are obtained through an X-ray simulation based on the Beer-Lambert law. The multi-perspective angiographic images are used to reconstruct the 3D geometry (72×72×60 voxels) of the artery and the distribution of the contrast perfusion in the 3D flow field by utilizing algebraic reconstruction techniques (ART) due to its prevalence in previous tomographic applications. The spatial resolution of the 3D image is 0.33 mm/voxel. The 3D OFM is applied on both the original 3D images and the reconstructed images to estimate the 3D flow field. The overall difference in velocity magnitude estimation between the original image and the reconstructed image is about 16%. Compared to the exact velocity distribution from CFD, the relative averaged error is 46%.