Automated 3D motion tracking using Gabor filter bank, robust point matching, and deformable models.
Automated 3D motion tracking using Gabor filter bank, robust point matching, and deformable models.
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DOI：
10.1109/tmi.2009.2021041
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发表时间：
2010-01
影响因子：
10.6
通讯作者：
Axel L
中科院分区：
文献类型：
--
作者：
Chen T;Wang X;Chung S;Metaxas D;Axel L
标记磁共振成像（taged MRI或tMRI）提供了一种直接和非侵入性显示心肌内部运动的方法。需要运动场的重建来量化重要的临床信息，例如，心肌应变，并检测局部心脏功能丧失。在本文中，我们提出了一个三步的方法来完成这项任务。首先，我们使用一个Gabor滤波器组检测和定位标记交叉点的图像帧，基于局部相位分析。接下来，我们使用改进版本的鲁棒点匹配（RPM）方法稀疏地跟踪心肌的运动，通过建立一个转换函数和一对一的对应关系，在不同的图像帧中的网格标签交叉点。特别地，RPM有助于最小化对运动跟踪结果的影响：1）通过平面运动，以及2）相对大的变形和/或相对小的标签间距。在最后一步中，使用RPM计算的变换函数初始化无网格可变形模型。该模型通过在图像信息的影响下变形来细化运动跟踪并生成密集的位移图，并且受到位移大小的约束以保持其几何结构。短轴和长轴图像平面中的2D位移图可以被组合以使用移动最小二乘法来驱动3D可变形模型，该移动最小二乘法受到标签交叉点处的残留误差的最小化的约束。该方法已被测试的数字幻影，以及在体内心脏正常志愿者和心脏病患者的数据。实验结果表明，该方法对合成数据和真实的数据都具有良好的性能。此外，该方法已被用于初步的临床研究，以评估心脏病（左心室肥大）患者和正常对照组之间的心肌应变分布的差异。最终结果表明，该方法能够将患者与健康个体分离。此外，该方法检测心肌应变分布中的局部异常并使其量化成为可能，这对于患者临床状况的定量分析至关重要。这种运动跟踪方法可以提高心脏病患者定量应变分析的吞吐量和可靠性，并具有进一步临床应用的潜力。
Tagged Magnetic Resonance Imaging (tagged MRI or tMRI) provides a means of directly and noninvasively displaying the internal motion of the myocardium. Reconstruction of the motion field is needed to quantify important clinical information, e.g., the myocardial strain, and detect regional heart functional loss. In this paper, we present a three-step method for this task. First, we use a Gabor filter bank to detect and locate tag intersections in the image frames, based on local phase analysis. Next, we use an improved version of the Robust Point Matching (RPM) method to sparsely track the motion of the myocardium, by establishing a transformation function and a one-to-one correspondence between grid tag intersections in different image frames. In particular, the RPM helps to minimize the impact on the motion tracking result of: 1) through-plane motion, and 2) relatively large deformation and/or relatively small tag spacing. In the final step, a meshless deformable model is initialized using the transformation function computed by RPM. The model refines the motion tracking and generates a dense displacement map, by deforming under the influence of image information, and is constrained by the displacement magnitude to retain its geometric structure. The 2D displacement maps in short and long axis image planes can be combined to drive a 3D deformable model, using the Moving Least Square method, constrained by the minimization of the residual error at tag intersections. The method has been tested on a numerical phantom, as well as on in vivo heart data from normal volunteers and heart disease patients. The experimental results show that the new method has a good performance on both synthetic and real data. Furthermore, the method has been used in an initial clinical study to assess the differences in myocardial strain distributions between heart disease (left ventricular hypertrophy) patients and the normal control group. The final results show that the proposed method is capable of separating patients from healthy individuals. In addition, the method detects and makes possible quantification of local abnormalities in the myocardium strain distribution, which is critical for quantitative analysis of patients’ clinical conditions. This motion tracking approach can improve the throughput and reliability of quantitative strain analysis of heart disease patients, and has the potential for further clinical applications.