Blood Flow and Velocity Estimation Based on Vessel Transit Time by Combining 2D and 3D X-Ray Angiography

  • Hrvoje Bogunović
  • Sven Lončarić
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


The X-ray imaging equipment could be used to measure hemodynamic function in addition to visualizing the morphology. The parameters of specific interest are arterial blood flow and velocity. Current monoplane X-ray systems can perform 3D reconstruction of the arterial tree as well as to capture the propagation of the injected contrast agent on a sequence of 2D angiograms. We combine the 2D digital subtraction angiography sequence with the mechanically registered 3D volume of the vessel tree. From 3D vessel tree we extract each vessel and obtain its centerline and cross-section area. We get our velocity estimation from 2D sequence by comparing time-density signals measured at different ends of the projected vessel. From the average velocity and cross-section area we get the average blood flow estimate for each vessel. The algorithm described here is applied to datasets from real neuroradiological studies.


Contrast Agent Digital Subtraction Angiography Velocity Estimation Vessel Tree Digital Subtraction Angiography Study 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Shpilfoygel, S.D., Close, R.A., Valentino, D.J., Duckwiler, G.R.: X-ray videodensitometric methods for blood flow and velocity measurement: A critical review of literature. Medical Physics 27, 2008–2023 (2000)CrossRefGoogle Scholar
  2. 2.
    Rhode, K.S., Lambrou, T., Hawkes, D.J., Seifalian, A.M.: Novel approaches to the measurement of arterial blood flow from dynamic digial x-ray images. IEEE Trans. on Medical Imaging 24, 500–513 (2005)CrossRefGoogle Scholar
  3. 3.
    Shpilfoygel, S.D., Close, R.A., Valentino, D.J., Duckwiler, G.R.: Comparison of methods for instantaneous angiographic blood flow measurement. Medical Physics 26, 862–871 (1999)CrossRefGoogle Scholar
  4. 4.
    Huang, S.P., Decker, R.J., Goodrich, K.C., Parker, D.J., Muhlestein, J.B., Blatter, D.D., Parker, D.L.: Velocity measurement based on bolus tracking with the aid of three-dimensional reconstruction from digital subtraction angiography. Medical Physics 25, 677–686 (1997)CrossRefGoogle Scholar
  5. 5.
    Rhode, K.S., Lambrou, T., Hawkes, D.J., Hamilton, G., Seifalian, A.M.: Validation of an optical flow algorithm to measure blood flow waveforms in arteries using dynamic digital x-ray images. In: SPIE Medical Imaging: Image Processing, pp. 1414–1425 (2000)Google Scholar
  6. 6.
    Bogunovic, H., Loncaric, S.: Denoising of time-density data in digital subtraction angiography. In: Scandinavian Conference on Image Analysis, pp. 1157–1166 (2005)Google Scholar
  7. 7.
    Farag, A.A., Hassouna, M.S., Falik, R., Hushek, S.: Reliable fly-throughs of vascular trees. Technical report, CVIP Laboratory, University of Louisville (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hrvoje Bogunović
    • 1
  • Sven Lončarić
    • 1
  1. 1.Faculty of Electrical Engineering and ComputingUniversity of ZagrebZagrebCroatia

Personalised recommendations