An Embedding Framework for Myocardial Velocity Processing with MRI

  • Longfei Cong
  • Su-Lin Lee
  • Andrew Huntbatch
  • Tianzi Jiang
  • Guang-Zhong Yang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4091)


This paper presents an embedding framework for myocardial velocity processing with MRI based on a hollow, semi-spherical template. The relationship between vector bundle and manifold mapping is analysed and three different mapping methods that include tangent mapping, normal mapping, and normal-tangent mapping are assessed for their practical value of myocardial contractility analysis. The proposed method provides a basis for consistent volume matching and vector correspondence, in addition to the ease of calculating biomechanical indices such as radial, circumferential and longitudinal strain rates without the concern of boundary effects. Detailed analysis results with both synthetic and in vivo MR velocity data sets are provided.


Vector Bundle Nonrigid Registration Myocardial Velocity Left Ventricle Volume Surface Normal 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-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Longfei Cong
    • 1
  • Su-Lin Lee
    • 2
  • Andrew Huntbatch
    • 2
  • Tianzi Jiang
    • 1
  • Guang-Zhong Yang
    • 2
  1. 1.National Laboratory of Pattern Recognition, Institution of AutomationChinese Academy of SciencesBeijingChina
  2. 2.Royal Society/Wolfson MIC Laboratory , Department of ComputingImperial College LondonUnited Kingdom

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