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.
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.
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DOI：
10.1016/j.mri.2014.07.005
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发表时间：
2014-11
影响因子：
2.5
通讯作者：
Fei B
中科院分区：
文献类型：
--
作者：
Liu H;Wang J;Xu X;Song E;Wang Q;Jin R;Hung CC;Fei B
提出了一种鲁棒且精确的中心频率（CF）估计（RACE）算法，用于改善局部正弦波建模（SinMod）方法的性能，该方法是一种针对标记心脏磁共振（MR）图像的良好运动估计方法。RACE算法能够在未知标注参数的情况下，自动、有效、高效地为SinMod方法产生一个非常合适的CF估计，其关键技术有两个：（1）均值漂移算法，能够提供准确、快速的CF估计;（2）提出了一种新颖的双向组合策略，进一步提高了CF估计的精度和鲁棒性。为了比较，还提出了其它几种可行的CF估计算法。设计了几种可以在没有地面真值的情况下对真实的数据进行验证的方法。通过对人体在体心脏数据的实验，验证了精确CF估计对SinMod运动估计的重要性，并验证了RACE算法在改善SinMod运动估计性能方面的有效性。
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.