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Bayesian Vessel Extraction for Planning of Radiofrequency-Ablation

  • Stephan Zidowitz
  • Johann Drexl
  • Tim Kröger
  • Tobias Preusser
  • Felix Ritter
  • Andreas Weihusen
  • Heinz-Otto Peitgen
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Abstract

The software-assisted planning of radiofrequency-ablation of liver tumors calls for robust and fast methods to segment the tumor and surrounding vascular structures from clinical data to allow a numerical estimation, whether a complete thermal destruction of the tumor is feasible taking the cooling effect of the vessels into account. As the clinical workflow in radiofrequency-ablation does not allow for time consuming planning procedures, the implementation of robust and fast segmentation algorithms is critical in building a streamlined software application tailored to the clinical needs. To suppress typical artifacts in clinical CT or MRT data - like inhomogeneous background density due to the imaging procedure - a Bayesian background compensation is developed, which subsequently allows a robust segmentation of the vessels by fast threshold based algorithms. The presented Bayesian background compensation has proven to handle a wide range of image perturbances in MRT and CT data and leads to a fast and reliable identification of vascular structures in clinical data.

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References

  1. 1.
    Arnold JB, Liow JS, Schaper KA, et al. Qualitative and quantitative evaluation of six algorithms for correcting intensity nonuniformity effects. Neuroimage 2001;13:931–943.CrossRefGoogle Scholar
  2. 2.
    Brinkmann BH, Manduca A, Robb RA. Optimized homomorphic unsharp masking for MR grayscale inhomogeneity correction. IEEE TransMed Imaging 1998;17:161–171.CrossRefGoogle Scholar
  3. 3.
    Sled JG, Zijdenbos AP, Evans AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 1998;17:87–97.CrossRefGoogle Scholar
  4. 4.
    Shattuck DW, Sandor-Leahy SR, Schaper KA, et al. Magnetic resonance image tissue classification using a partial volume model. Neuroimage 2001;13:856–876.CrossRefGoogle Scholar
  5. 5.
    Fischer R, Hanson KM, V Dose V, von Der Linden W. Background estimation in experimental spectra. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics 2000 Feb;61(2):1152–60 2000;61:1152–1160.Google Scholar
  6. 6.
    Guglielmetti F, Fischer R, Dose V. Mixture modeling for background and sources separation in x-ray astronomical images. American Institut of Physics; 2004. 111–118.Google Scholar
  7. 7.
    Schumaker LL, Utreras FI. On generalized cross validation for tensor smoothing splines. SIAM J Sci Stat Comput 1990;11(4):713–731.zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Bornemann L, Kuhnigk JM, Dicken V, et al. OncoTREAT-A software assistant for oncological therapy monitoring. Procs CARS 2005; 429–434.Google Scholar
  9. 9.
    Weihusen A, Ritter F, Pereira P, et al. Towards a workflow-oriented software assistance for the radiofrequency ablation. Lecture Notes in Informatics 2006;93:507–513.Google Scholar
  10. 10.
    Kröger T, Altrogge I, Preusser T, et al. Numerical simulation of radio frequency ablation with state dependent material parameters in three space dimensions. LNCS 2006;4191:380–388.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Stephan Zidowitz
    • 1
  • Johann Drexl
    • 1
  • Tim Kröger
    • 2
  • Tobias Preusser
    • 2
  • Felix Ritter
    • 1
  • Andreas Weihusen
    • 1
  • Heinz-Otto Peitgen
    • 1
  1. 1.MeVis Research GmbHBremen
  2. 2.CeVis - Center for Complex Systems and Visualization, Department of Mathematics and Computer ScienceUniversity of BremenBremen

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