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Incompressible Cardiac Motion Estimation of the Left Ventricle Using Tagged MR Images

  • Xiaofeng Liu
  • Khaled Z. Abd-Elmoniem
  • Jerry L. Prince
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)

Abstract

Interpolation from sparse imaging data is typically required to achieve dense, three-dimensional quantification of left ventricular function. Although the heart muscle is known to be incompressible, this fact is ignored by most previous approaches that address this problem. In this paper, we present a method to reconstruct a dense representation of the three-dimensional, incompressible deformation of the left ventricle from tagged MR images acquired in both short-axis and long axis orientations. The approach applies a smoothing, divergence-free, vector spline to interpolate velocity fields at intermediate discrete times such that the collection of velocity fields integrate over time to match the observed displacement components. Through this process, the method yields a dense estimate of a displacement field that matches our observations and also corresponds to an incompressible motion.

Keywords

Short Axis Slice Short Axis Image Jacobian Determinant Myocardial Motion Harmonic Phase 
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 2009

Authors and Affiliations

  • Xiaofeng Liu
    • 1
  • Khaled Z. Abd-Elmoniem
    • 3
  • Jerry L. Prince
    • 1
    • 2
  1. 1.Departments of Computer Science 
  2. 2.Electrical and Computer Engineering 
  3. 3.Radiology and Radiological Science Johns Hopkins UniversityBaltimoreUSA

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