Liu Hong, Yan Meng, Song Enmin, Wang Jie, Wang Qian, Jin Renchao, Jin Lianghai, Hung Chih-Cheng
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China; Key Laboratory of Education ministry for Image Processing and Intelligence Control, Wuhan, Hubei, China.
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, China; Key Laboratory of Education ministry for Image Processing and Intelligence Control, Wuhan, Hubei, China; Hubei Key Laboratory of Medical Information Analysis and Tumor Diagnosis and Treatment.
Magn Reson Imaging. 2016 May;34(4):579-95. doi: 10.1016/j.mri.2015.12.022. Epub 2015 Dec 19.
Myocardial motion estimation of tagged cardiac magnetic resonance (TCMR) images is of great significance in clinical diagnosis and the treatment of heart disease. Currently, the harmonic phase analysis method (HARP) and the local sine-wave modeling method (SinMod) have been proven as two state-of-the-art motion estimation methods for TCMR images, since they can directly obtain the inter-frame motion displacement vector field (MDVF) with high accuracy and fast speed. By comparison, SinMod has better performance over HARP in terms of displacement detection, noise and artifacts reduction. However, the SinMod method has some drawbacks: 1) it is unable to estimate local displacements larger than half of the tag spacing; 2) it has observable errors in tracking of tag motion; and 3) the estimated MDVF usually has large local errors. To overcome these problems, we present a novel motion estimation method in this study. The proposed method tracks the motion of tags and then estimates the dense MDVF by using the interpolation. In this new method, a parameter estimation procedure for global motion is applied to match tag intersections between different frames, ensuring specific kinds of large displacements being correctly estimated. In addition, a strategy of tag motion constraints is applied to eliminate most of errors produced by inter-frame tracking of tags and the multi-level b-splines approximation algorithm is utilized, so as to enhance the local continuity and accuracy of the final MDVF. In the estimation of the motion displacement, our proposed method can obtain a more accurate MDVF compared with the SinMod method and our method can overcome the drawbacks of the SinMod method. However, the motion estimation accuracy of our method depends on the accuracy of tag lines detection and our method has a higher time complexity.
标记心脏磁共振（TCMR）图像的心肌运动估计在心脏病的临床诊断和治疗中具有重要意义。目前，谐波相位分析方法（HARP）和局部正弦波建模方法（SinMod）已被证明是两种用于TCMR图像的先进运动估计方法，因为它们能够以高精度和快速速度直接获得帧间运动位移矢量场（MDVF）。相比之下，SinMod在位移检测、噪声和伪影减少方面比HARP具有更好的性能。然而，SinMod方法存在一些缺点：1）它无法估计大于标记间距一半的局部位移；2）在标记运动跟踪中存在明显误差；3）估计的MDVF通常存在较大的局部误差。为了克服这些问题，我们在本研究中提出了一种新颖的运动估计方法。所提出的方法跟踪标记的运动，然后通过插值估计密集的MDVF。在这种新方法中，应用全局运动的参数估计过程来匹配不同帧之间的标记交点，确保正确估计特定类型的大位移。此外，应用标记运动约束策略来消除标记帧间跟踪产生的大部分误差，并利用多级b样条逼近算法，以提高最终MDVF的局部连续性和准确性。在运动位移估计中，与SinMod方法相比，我们提出的方法可以获得更准确的MDVF，并且我们的方法可以克服SinMod方法的缺点。然而，我们方法的运动估计精度取决于标记线检测的精度，并且我们的方法具有更高的时间复杂度。
