Automatic Cardiac Motion Tracking Using Both Untagged and 3D Tagged MR Images

  • Haiyan Wang
  • Wenzhe Shi
  • Xiahai Zhuang
  • Simon Duckett
  • KaiPin Tung
  • Philip Edwards
  • Reza Razavi
  • Sebastien Ourselin
  • Daniel Rueckert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7085)

Abstract

We present a fully automatic framework for cardiac motion tracking based on non-rigid image registration for the analysis of myocardial motion using both untagged and 3D tagged MR images. We detect and track anatomical landmarks in the heart and combine this with intensity-based motion tracking to allow accurately model cardiac motion while significantly reduce the computational complexity. A collaborative similarity measure simultaneously computed in three LA views is employed to register a sequence of images taken during the cardiac cycle to a reference image taken at end-diastole. We then integrate a valve plane tracker into the framework which uses short-axis and long-axis untagged MR images as well as 3D tagged images to estimate a fully four-dimensional motion field of the left ventricle.

Keywords

Motion Tracking Cardiac Motion Valve Annulus Trigger Time Valve Plane 
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 2012

Authors and Affiliations

  • Haiyan Wang
    • 1
  • Wenzhe Shi
    • 1
  • Xiahai Zhuang
    • 2
  • Simon Duckett
    • 3
  • KaiPin Tung
    • 1
  • Philip Edwards
    • 1
  • Reza Razavi
    • 3
  • Sebastien Ourselin
    • 2
  • Daniel Rueckert
    • 1
  1. 1.Biomedical Image Analysis GroupImperial College LondonUK
  2. 2.Center for Medical Image ComputingUniversity College LondonUK
  3. 3.The Rayne InstituteKings College LondonUK

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