Divergence Compensatory Optical Flow Method for Blood Velocimetry

  • Zifeng Yang
    Mem. ASME Department of Mechanical and Materials Engineering, Wright State University, 3640 Colonel Glenn Highway, Dayton, OH 45435 e-mail:
  • Hongtao Yu
    Department of Mechanical and Materials Engineering, Wright State University, Dayton, OH 45435 e-mail:
  • George P. Huang
    Fellow ASME Department of Mechanical and Materials Engineering, Wright State University, Dayton, OH 45435 e-mail:
  • Bryan Ludwig
    Boonshoft School of Medicine, Wright State University, Dayton, OH 45435;

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<jats:p>Detailed blood velocity map in the vascular system can be obtained by applying the optical flow method (OFM) in processing fluoroscopic digital subtracted catheter angiographic images; however, there are still challenges with the accuracy of this method. In the present study, a divergence compensatory optical flow method (DC-OFM), in which a nonzero divergence of velocity is assumed due to the finite resolution of the image, was explored and applied to the digital subtraction angiography (DSA) images of blood flow. The objective of this study is to examine the applicability and evaluate the accuracy of DC-OFM in assessing the blood flow velocity in vessels. First, an Oseen vortex flow was simulated on the standard particle image to generate an image pair. Then, the DC-OFM was applied on the particle image pair to recover the velocity field for validation. Second, DSA images of intracranial arteries were used to examine the accuracy of the current method. For each set of images, the first image is the in vivo DSA image, and the second image is generated by superimposing a given flow field. The recovered velocity map by DC-OFM agrees well with the exact velocity for both the particle images and the angiographic images. In comparison with the traditional OFM, the present method can provide more accurate velocity estimation. The accuracy of the velocity estimation can also be improved by implementing preprocess techniques including image intensification, Gaussian filtering, and “image-shift.”</jats:p>

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