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Model-Based Motion Artifact Correction in Digital Subtraction Angiography Using Optical-Flow

  • Sai Gokul HariharanEmail author
  • Christian Kaethner
  • Norbert Strobel
  • Markus Kowarschik
  • Julie DiNitto
  • Rebecca Fahrig
  • Nassir Navab
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

Digital subtraction angiography is an important method for obtaining an accurate visualization of contrast-enhanced blood vessels. The technique involves the digital subtraction of two X-ray images, one with contrast filled vessels (fill image) and one without (mask image). Unfortunately, artifacts that are introduced due to the subtraction of misaligned mask and fill images may potentially degrade the diagnostic value of an image. The techniques used for correcting such artifacts involve the use of affine image registration techniques for aligning the mask and fill images and image processing techniques for suppressing the artifacts. Although affine registration techniques often yield acceptable results, they may fail when the imaged object undergoes 3D transformations. The techniques used for suppressing artifacts may cause blurring, when a projection image can no longer be corrected using a globally uniform motion model. In this paper, we have introduced an optical-ow based local motion compensation approach, where pixel-wise deformation fields are computed based on an X-ray imaging model. A visual inspection of the results shows a significant improvement in the image quality due to a reduction in the artifacts caused by misregistrations.

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Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  • Sai Gokul Hariharan
    • 1
    • 2
    Email author
  • Christian Kaethner
    • 2
  • Norbert Strobel
    • 2
    • 3
  • Markus Kowarschik
    • 1
    • 2
  • Julie DiNitto
    • 4
  • Rebecca Fahrig
    • 2
    • 5
  • Nassir Navab
    • 1
    • 5
  1. 1.Computer Aided Medical Procedures (CAMP)Technische Universität MünchenMunichDeutschland
  2. 2.Fakultät für ElektrotechnikHochschule für angewandte Wissenschaften Würzburg- SchweinfurtSchweinfurtDeutschland
  3. 3.Siemens Medical SolutionsHoffman EstatesUSA
  4. 4.Pattern Recognition LabFriedrich-Alexander-Universität Erlangen- Nürnberg (FAU)ErlangenDeutschland
  5. 5.Whiting School of EngineeringJohns Hopkins UniversityBaltimoreUSA

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