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Reduced-Dose Patient to Baseline CT Rigid Registration in 3D Radon Space

  • Guy Medan
  • Achia Kronman
  • Leo Joskowicz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

We present a new method for rigid registration of CT scans in Radon space. The inputs are the two 3D Radon transforms of the CT scans, one densely sampled and the other sparsely sampled. The output is the rigid transformation that best matches them. The algorithm starts by finding the best matching between each direction vector in the sparse transform and the corresponding direction vector in the dense transform. It then solves the system of linear equations derived from the direction vector pairs. Our method can be used to register two CT scans and to register a baseline scan to the patient with reduced-dose scanning without compromising registration accuracy. Our preliminary simulation results on the Shepp-Logan head phantom dataset and a pair of clinical head CT scans indicates that our 3D Radon space rigid registration method performs significantly better than image-based registration for very few scan angles and comparably for densely-sampled scans.

Keywords

Direction Vector Rigid Transformation Normalize Cross Correlation Rigid Registration Normal Direction Vector 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Guy Medan
    • 1
  • Achia Kronman
    • 1
  • Leo Joskowicz
    • 1
  1. 1.The Rachel and Selim Benin School of Computer Science and EngineeringThe Hebrew University of JerusalemIsrael

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