Denney T S
Department of Electrical and Computer Engineering, Auburn University, AL 36849, USA.
IEEE Trans Med Imaging. 1999 Apr;18(4):330-44. doi: 10.1109/42.768842.
Magnetic resonance (MR) tagging has been shown to be a useful technique for noninvasively measuring the deformation of an in vivo heart. An important step in analyzing tagged images is the identification of tag lines in each image of a cine sequence. Most existing tag identification algorithms require user defined myocardial contours. Contour identification, however, is time consuming and requires a considerable amount of user intervention. In this paper, a new method for identifying tag lines, which we call the ML/MAP method, is presented that does not require user defined myocardial contours. The ML/MAP method is composed of three stages. First, a set of candidate tag line centers is estimated across the entire region-of-interest (ROI) with a snake algorithm based on a maximum-likelihood (ML) estimate of the tag center. Next, a maximum a posteriori (MAP) hypothesis test is used to detect the candidate tag centers that are actually part of a tag line. Finally, a pruning algorithm is used to remove any detected tag line centers that do not meet a spatio-temporal continuity criterion. The ML/MAP method is demonstrated on data from ten in vivo human hearts.
磁共振（MR）标记已被证明是一种用于无创测量活体心脏变形的有用技术。分析标记图像的一个重要步骤是在电影序列的每个图像中识别标记线。大多数现有的标记识别算法需要用户定义心肌轮廓。然而，轮廓识别既耗时又需要大量用户干预。本文提出了一种新的标记线识别方法，我们称之为ML/MAP方法，该方法不需要用户定义心肌轮廓。ML/MAP方法由三个阶段组成。首先，基于标记中心的最大似然（ML）估计，使用蛇形算法在整个感兴趣区域（ROI）估计一组候选标记线中心。接下来，使用最大后验（MAP）假设检验来检测实际上是标记线一部分的候选标记中心。最后，使用修剪算法去除任何不符合时空连续性标准的检测到的标记线中心。在来自十个活体人类心脏的数据上演示了ML/MAP方法。
