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Diffeomorphic Density Registration in Thoracic Computed Tomography

  • Caleb Rottman
  • Ben Larson
  • Pouya Sabouri
  • Amit Sawant
  • Sarang Joshi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)

Abstract

Accurate motion estimation in thoracic computed tomography (CT) plays a crucial role in the diagnosis and treatment planning of lung cancer. This paper provides two key contributions to this motion estimation. First, we show we can effectively transform a CT image of effective linear attenuation coefficients to act as a density, i.e. exhibiting conservation of mass while undergoing a deformation. Second, we propose a method for diffeomorphic density registration for thoracic CT images. This algorithm uses the appropriate density action of the diffeomorphism group while offering a weighted penalty on local tissue compressibility. This algorithm appropriately models highly compressible areas of the body (such as the lungs) and incompressible areas (such as surrounding soft tissue and bones).

Keywords

Diffeomorphisms Thoracic motion estimation Density action Image registration 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Caleb Rottman
    • 1
  • Ben Larson
    • 1
  • Pouya Sabouri
    • 2
  • Amit Sawant
    • 2
  • Sarang Joshi
    • 1
  1. 1.Scientific Computing and Imaging Institute, Department of BioengineeringUniversity of UtahSalt Lake CityUSA
  2. 2.University of Maryland School of MedicineBaltimoreUSA

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