Diaphragm Tracking in Cardiac C-Arm Projection Data

  • Marco BögelEmail author
  • Andreas Maier
  • Hannes G. Hofmann
  • Joachim Hornegger
  • Rebecca Fahrig
Part of the Informatik aktuell book series (INFORMAT)


Long acquisition times of several seconds lead to image artifacts in cardiac C-arm CT. These artifacts are mostly caused by respiratory motion. In order to improve image quality, it is important to accurately estimate the breathing motion that occurred during image acquisition. It has been shown that diaphragm motion is correlated to the respiration-induced motion of the heart. We describe the development of a method that is able to accurately track the contour of the diaphragm in projection space using a 2D quadratic curve model of the diaphragm to simplify the process. In order to provide robust and stable tracking, additional constraints based on prior knowledge of the projection geometry and human anatomy are introduced. Results show that the tracking is very accurate. A mean model error per pixel of 0.93 ± 0.44 pixels for the left and 0.79 ± 0.19 pixels for the right hemidiaphragm was observed. The diaphragm top is tracked with an even lower error of only 0.75 ± 0.84 pixels for the left and 0.45 ± 056 pixels for the right hemidiaphragm respectively.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jahnke C, Paetsch I, Achenbach S, et al. Coronary MR imaging: breath-hold capability and patterns, coronary artery rest periods, and beta-blocker use. Radiology. 2006;239:71–8.CrossRefGoogle Scholar
  2. 2.
    Wiesner S, Yaniv Z. Respiratory signal generation for retrospective gating of cone- beam CT images. Proc SPIE. 2008;6918:71.Google Scholar
  3. 3.
    Marchant TE, Price GJ, Matuszewiski BJ, et al. Reduction of motion artefacts in on-board cone beam CT by warping of projection images. Br J Radiol. 2011;84:251– 64.CrossRefGoogle Scholar
  4. 4.
    Wang Y, Riederer S, Ehman R. Respiratory motion of the heart: kinematics and the implications for spatial resolution in coronary imaging. Magn Reson Med. 1995;33:716–19.Google Scholar
  5. 5.
    Sonke JJ, Zijp L, Remeijer P, et al. Respiratory correlated cone beam CT. Med Phys. 2005;32:1176–86.CrossRefGoogle Scholar
  6. 6.
    Canny FJ. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell. 1986;8(6):679–98.CrossRefGoogle Scholar
  7. 7.
    Fischler MA, Bolles RC. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM. 1981;24:381–95.MathSciNetCrossRefGoogle Scholar
  8. 8.
    Segars WP, Mahesh M, Beck TJ, et al. Realistic CT simulation using the 4D XCAT phantom. Med Phys. 2008;35(8):3800–8.CrossRefGoogle Scholar
  9. 9.
    Schwemmer C, Prummer M, Daum V, et al. High-Density object removal from projection images using low-frequency-based object masking. Proc BVM. 2010; p. 365–9.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Marco Bögel
    • 1
    Email author
  • Andreas Maier
    • 1
  • Hannes G. Hofmann
    • 1
  • Joachim Hornegger
    • 1
  • Rebecca Fahrig
    • 2
  1. 1.Pattern Recognition LabUniversität Erlangen-NürnbergNürnbergDeutschland
  2. 2.Department of Radiology, Lucas MRS CenterStanford UniversityPalo Alto, CAUSA

Personalised recommendations