Tag Separation in Cardiac Tagged MRI

  • Junzhou Huang
  • Zhen Qian
  • Xiaolei Huang
  • Dimitris Metaxas
  • Leon Axel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5242)


In this paper we introduce a tag separation method for better cardiac boundary segmentation and tag tracking. Our approach is based on two observations in the cardiac tagged MR images: 1) the tag patterns have a regular texture; 2) the cardiac images without tag patterns are piecewise smooth with sparse gradients. These observations motivate us to use two dictionaries, one based on the Discrete Cosine Transform for representing tag patterns and the other based on the Wavelet Transform for representing the underlying cardiac image without tag patterns. The two dictionaries are built such that they can lead to sparse representations of the tag patterns and of the piece-wise smooth regions without tag patterns. With the two dictionaries, a new tag separation approach is proposed to simultaneously optimize w.r.t. the two sparse representations, where optimization is directed by the Total Variation regularization scheme. While previous methods have focused on tag removal, our approach to acquiring both optimally-decomposed tag-only image and the cardiac image without tags simultaneously can be used for better tag tracking and cardiac boundary segmentation. We demonstrate the superior performance of the proposed approach through extensive experiments on large sets of cardiac tagged MR images.

Supplementary material

978-3-540-85990-1_35_MOESM1_ESM.pdf (1.6 mb)
Electronic Supplementary Material (1,657 KB)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Junzhou Huang
    • 1
  • Zhen Qian
    • 1
  • Xiaolei Huang
    • 2
  • Dimitris Metaxas
    • 1
  • Leon Axel
    • 3
  1. 1.Division of Computer and Information SciencesRutgers UniversityUSA
  2. 2.Department of Computer Science and EngineeringLehigh UniversityUSA
  3. 3.Department of RadiologyNew York UniversityNew YorkUSA

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