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3D ultrasound-CT registration of the liver using combined landmark-intensity information

  • Thomas LangeEmail author
  • Nils Papenberg
  • Stefan Heldmann
  • Jan Modersitzki
  • Bernd Fischer
  • Hans Lamecker
  • Peter M. Schlag
Original Article

Abstract

Purpose

An important issue in computer-assisted surgery of the liver is a fast and reliable transfer of preoperative resection plans to the intraoperative situation. One problem is to match the planning data, derived from preoperative CT or MR images, with 3D ultrasound images of the liver, acquired during surgery. As the liver deforms significantly in the intraoperative situation non-rigid registration is necessary. This is a particularly challenging task because pre- and intraoperative image data stem from different modalities and ultrasound images are generally very noisy.

Methods

One way to overcome these problems is to incorporate prior knowledge into the registration process. We propose a method of combining anatomical landmark information with a fast non-parametric intensity registration approach. Mathematically, this leads to a constrained optimization problem. As distance measure we use the normalized gradient field which allows for multimodal image registration.

Results

A qualitative and quantitative validation on clinical liver data sets of three different patients has been performed. We used the distance of dense corresponding points on vessel center lines for quantitative validation. The combined landmark and intensity approach improves the mean and percentage of point distances above 3 mm compared to rigid and thin-plate spline registration based only on landmarks.

Conclusion

The proposed algorithm offers the possibility to incorporate additional a priori knowledge—in terms of few landmarks—provided by a human expert into a non-rigid registration process.

Keywords

Image Registration Portal Venous Phase Rigid Registration Landmark Pair Vessel Center Line 
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.

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Copyright information

© CARS 2008

Authors and Affiliations

  • Thomas Lange
    • 1
    Email author
  • Nils Papenberg
    • 2
  • Stefan Heldmann
    • 2
  • Jan Modersitzki
    • 2
  • Bernd Fischer
    • 2
  • Hans Lamecker
    • 3
  • Peter M. Schlag
    • 1
  1. 1.Charité Comprehensive Cancer Center, CharitéUniversitätsmedizin BerlinBerlinGermany
  2. 2.Institute of MathematicsUniversity of LübeckLübeckGermany
  3. 3.Zuse Institute BerlinBerlinGermany

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