Diffeomorphic density registration in thoracic computed tomography

Caleb Rottman, Ben Larson, Pouya Sabouri, Amit Sawant, Sarang Joshi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

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).

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
PublisherSpringer Verlag
Pages46-53
Number of pages8
Volume9902 LNCS
ISBN (Print)9783319467252
DOIs
StatePublished - 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9902 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

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Keywords

  • Density action
  • Diffeomorphisms
  • Image registration
  • Thoracic motion estimation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Rottman, C., Larson, B., Sabouri, P., Sawant, A., & Joshi, S. (2016). Diffeomorphic density registration in thoracic computed tomography. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (Vol. 9902 LNCS, pp. 46-53). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9902 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46726-9_6