A robust and accurate center-frequency estimation (RACE) algorithm for improving motion estimation performance of SinMod on tagged cardiac MR images without known tagging parameters

Hong Liu, Jie Wang, Xiangyang Xu, Enmin Song, Qian Wang, Renchao Jin, Chih Cheng Hung, Baowei Fei

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

A robust and accurate center-frequency (CF) estimation (RACE) algorithm for improving the performance of the local sine-wave modeling (SinMod) method, which is a good motion estimation method for tagged cardiac magnetic resonance (MR) images, is proposed in this study. The RACE algorithm can automatically, effectively and efficiently produce a very appropriate CF estimate for the SinMod method, under the circumstance that the specified tagging parameters are unknown, on account of the following two key techniques: (1) the well-known mean-shift algorithm, which can provide accurate and rapid CF estimation; and (2) an original two-direction-combination strategy, which can further enhance the accuracy and robustness of CF estimation. Some other available CF estimation algorithms are brought out for comparison. Several validation approaches that can work on the real data without ground truths are specially designed. Experimental results on human body in vivo cardiac data demonstrate the significance of accurate CF estimation for SinMod, and validate the effectiveness of RACE in facilitating the motion estimation performance of SinMod.

Original languageEnglish (US)
Pages (from-to)1139-1155
Number of pages17
JournalMagnetic Resonance Imaging
Volume32
Issue number9
DOIs
Publication statusPublished - Nov 1 2014
Externally publishedYes

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Keywords

  • Center-frequency
  • Mean-shift
  • Motion estimation
  • SinMod
  • Tagged cardiac MR image
  • Two-direction-combination

ASJC Scopus subject areas

  • Biophysics
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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