### 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.

Original language | English (US) |
---|---|

Title of host publication | Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention |

Pages | 331-338 |

Number of pages | 8 |

Volume | 12 |

Edition | Pt 2 |

State | Published - 2009 |

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### ASJC Scopus subject areas

- Medicine(all)

### Cite this

*Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention*(Pt 2 ed., Vol. 12, pp. 331-338)

**Incompressible cardiac motion estimation of the left ventricle using tagged MR images.** / Liu, Xiaofeng; Abd-Elmoniem, Khaled Z.; Prince, Jerry Ladd.

Research output: Chapter in Book/Report/Conference proceeding › Chapter

*Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention.*Pt 2 edn, vol. 12, pp. 331-338.

}

TY - CHAP

T1 - Incompressible cardiac motion estimation of the left ventricle using tagged MR images.

AU - Liu, Xiaofeng

AU - Abd-Elmoniem, Khaled Z.

AU - Prince, Jerry Ladd

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84883840648&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84883840648&partnerID=8YFLogxK

M3 - Chapter

C2 - 20426129

AN - SCOPUS:84883840648

VL - 12

SP - 331

EP - 338

BT - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

ER -