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

• Type of Material: Article
• Authors: Hong Liu, Huazhong University of Science and Technology; Jie Wang, Huazhong University of Science and Technology; Xiangyang Xu, Huazhong University of Science and Technology; Enmin Song, Huazhong University of Science and Technology; Qian Wang, Emory University; Renchao Jin, Huazhong University of Science and Technology; Chih-Cheng Hung, Southern Polytechnic State University; Baowei Fei, Emory University
• Language: English
• Date: 2014-11-01
• Publisher: Elsevier
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2014 Elsevier Inc.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.mri.2014.07.005
• Title of Journal or Parent Work: Magnetic Resonance Imaging
• ISSN: 0730-725X
• Volume: 32
• Issue: 9
• Start Page: 1139
• End Page: 1155
• Grant/Funding Information: This work is supported by the National Natural Science Foundation of China (Grant No. 61075010), National Natural Science Foundation of China (Grant No. 61370179), Fundamental Research Funds for the Central Universities of China (Grant No. 2014TS009), and National Key Technology Research and Development Program of China (Grant No. 2012BAI23B07).
• Keywords: HEART; SPATIAL MODULATION; HARMONIC-PHASE MRI; MAGNETIZATION; Two-direction-combination; Radiology, Nuclear Medicine & Medical Imaging; TRACKING; Life Sciences & Biomedicine; Mean-shift; Tagged cardiac MR image; SIMPLE RECURSIVE ALGORITHM; SinMod; Center-frequency; Science & Technology; Motion estimation
• Research Categories: Health Sciences, Radiology

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.