Department of Radiology, New York University, New York, NY 10016, USA.
IEEE Trans Med Imaging. 2010 Jan;29(1):1-11. doi: 10.1109/TMI.2009.2021041. Epub 2009 Apr 14.
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.
标记磁共振成像（标记 MRI 或 tMRI）提供了一种直接且非侵入式显示心肌内部运动的方法。为了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，需要重建运动场。在本文中，我们提出了一种用于此任务的三步方法。首先，我们使用基于局部相位分析的 Gabor 滤波器组检测和定位图像帧中的标记交点。接下来，我们使用改进的稳健点匹配（RPM）方法稀疏地跟踪心肌的运动，通过建立变换函数和不同图像帧中网格标记交点之间的一一对应关系。特别是，RPM 有助于最小化对运动跟踪结果的影响：1） 切向运动和 2） 相对较大的变形和/或相对较小的标记间距。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。该模型通过受图像信息影响的变形来细化运动跟踪，并生成密集的位移图，同时受位移幅度的约束以保留其几何结构。短轴和长轴图像平面中的 2D 位移图可以结合起来，使用移动最小二乘法驱动 3D 可变形模型，通过在标记交点处的残差最小化来约束。该方法已在数字体模以及正常志愿者和心脏病患者的体内心脏数据上进行了测试。实验结果表明，该新方法在合成数据和真实数据上均具有良好的性能。此外，该方法已用于初步临床研究，以评估心脏病（左心室肥厚）患者与正常对照组之间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区分开来。此外，该方法可以检测并可能量化心肌应变分布的局部异常，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速度和可靠性，并且具有进一步临床应用的潜力。
