Dept. of Radiol., Johns Hopkins Univ. Sch. of Med., Baltimore, MD.
IEEE Trans Med Imaging. 1994;13(1):74-88. doi: 10.1109/42.276146.
Tracking magnetic resonance tags in myocardial tissue promises to be an effective tool for the assessment of myocardial motion. The authors describe a hierarchy of image processing steps which rapidly detects both the contours of the myocardial boundaries of the left ventricle and the tags within the myocardium. The method works on both short axis and long axis images containing radial and parallel tag patterns, respectively. Left ventricular boundaries are detected by first removing the tags using morphological closing and then selecting candidate edge points. The best inner and outer boundaries are found using a dynamic program that minimizes a nonlinear combination of several local cost functions. Tags are tracked by matching a template of their expected profile using a least squares estimate. Since blood pooling, contiguous and adjacent tissue, and motion artifacts sometimes cause detection errors, a graphical user interface was developed to allow user correction of anomalous points. The authors present results on several tagged images of a human. A fully automated run generally finds the endocardial boundary and the tag lines extremely well, requiring very little manual correction. The epicardial boundary sometimes requires more intervention to obtain an acceptable result. These methods are currently being used in the analysis of cardiac strain and as a basis for the analysis of alternate tag geometries.
在心肌组织中跟踪磁共振标记有望成为评估心肌运动的有效工具。作者描述了一个图像处理步骤的层次结构，该层次结构可以快速检测左心室的心肌边界和心肌内的标记。该方法适用于分别包含径向和平行标记模式的短轴和长轴图像。通过首先使用形态学闭运算去除标记，然后选择候选边缘点来检测左心室边界。使用动态规划找到最佳的内边界和外边界，该规划最小化几个局部成本函数的非线性组合。通过使用最小二乘估计匹配其预期轮廓的模板来跟踪标记。由于血液积聚、连续和相邻组织以及运动伪影有时会导致检测错误，因此开发了一个图形用户界面来允许用户纠正异常点。作者在几个人类标记图像上呈现了结果。全自动运行通常可以极好地找到心内膜边界和标记线，只需要很少的手动校正。心外膜边界有时需要更多的干预才能获得可接受的结果。这些方法目前正在用于分析心脏应变，并作为分析替代标记几何形状的基础。
