Fast Elastic Registration for Adaptive Radiotherapy

  • Urban Malsch
  • Christian Thieke
  • Rolf Bendl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


A new method for elastic mono-modal image registration for adaptive fractionated radiotherapy is presented. Elastic registration is a prerequisite for many medical applications in diagnosis, therapy planning, and therapy. Especially for adaptive radiotherapy efficient and accurate registration is required. Therefore, we developed a fast block matching algorithm for robust image registration. Anatomical landmarks are automatically selected at tissue borders and relocated in the frequency domain. A smooth interpolation is calculated by modified thin-plate splines with local impact. The concept of the algorithm allows different handling of different image structures. Thus, more features were included, like handling of discontinuities (e. g. air cavities in the intestinal track or rectum, observable in only one image), which can not be registered in a conventional way. The planning CT as well as delineated structures of target volume and organs at risks are transformed according to deviations observed in daily acquired verification CTs prior each dose fraction. This way, the time consuming repeated delineation, a prerequisite for adaptive radiotherapy, is avoided. The total calculation time is below 5 minutes and the accurateness is higher than voxel precision, which allows to use this tool in the clinical workflow. We present results of prostate, head-and-neck, and paraspinal tumors with verification by manually selected landmarks. We think this registration technique is not only suitable for adaptive radiotherapy, but also for other applications which require fast registration and possibilities to process special structures (e. g. discontinuities) in a different way.


Image Registration Translation Vector Normal Tissue Complication Probability Rigid Registration Total Calculation Time 
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

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Urban Malsch
    • 1
  • Christian Thieke
    • 2
    • 3
  • Rolf Bendl
    • 1
  1. 1.Department of Medical Physics 
  2. 2.Clinical Cooperation Unit RadiooncologyDKFZ Heidelberg 
  3. 3.Department of Radiation OncologyUniversity of HeidelbergGermany

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