Automatic cardiac motion tracking using both untagged and 3d tagged MR images

Authors:
Haiyan Wang;Wenzhe Shi;Xiahai Zhuang;Simon Duckett;KaiPin Tung;Philip Edwards;Reza Razavi;Sebastien Ourselin;Daniel Rueckert
Affiliations:
Biomedical Image Analysis Group, Imperial College London, UK;Biomedical Image Analysis Group, Imperial College London, UK;Center for Medical Image Computing, University College London, UK;The Rayne Institute, Kings College London, UK;Biomedical Image Analysis Group, Imperial College London, UK;Biomedical Image Analysis Group, Imperial College London, UK;The Rayne Institute, Kings College London, UK;Center for Medical Image Computing, University College London, UK;Biomedical Image Analysis Group, Imperial College London, UK
Venue:
STACOM'11 Proceedings of the Second international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
Year:
2011

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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.